diff --git a/data/2dsdb/2dsdb.biotools.json b/data/2dsdb/2dsdb.biotools.json
new file mode 100644
index 0000000000000..b56e9e0d63c37
--- /dev/null
+++ b/data/2dsdb/2dsdb.biotools.json
@@ -0,0 +1,48 @@
+{
+    "additionDate": "2023-02-09T13:46:00.115365Z",
+    "biotoolsCURIE": "biotools:2dsdb",
+    "biotoolsID": "2dsdb",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Vei Wang"
+        }
+    ],
+    "description": "The 2D semiconductor database (2DSdb) provides an ideal platform for computational modeling and design of new 2D semiconductors and heterostructures in photocatalysis, nanoscale devices, and other applications.",
+    "editPermission": {
+        "type": "public"
+    },
+    "homepage": "https://materialsdb.cn/2dsdb/index.html",
+    "lastUpdate": "2023-02-09T13:46:00.117941Z",
+    "license": "CC-BY-4.0",
+    "name": "2DSdb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JPCLETT.2C02972",
+            "pmid": "36480578"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Chemistry",
+            "uri": "http://edamontology.org/topic_3314"
+        },
+        {
+            "term": "Electron microscopy",
+            "uri": "http://edamontology.org/topic_0611"
+        },
+        {
+            "term": "Physics",
+            "uri": "http://edamontology.org/topic_3318"
+        }
+    ]
+}
diff --git a/data/3dpolys-le/3dpolys-le.biotools.json b/data/3dpolys-le/3dpolys-le.biotools.json
new file mode 100644
index 0000000000000..f854a668ddbc1
--- /dev/null
+++ b/data/3dpolys-le/3dpolys-le.biotools.json
@@ -0,0 +1,98 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-18T22:10:21.312699Z",
+    "biotoolsCURIE": "biotools:3dpolys-le",
+    "biotoolsID": "3dpolys-le",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "daniel.jost@ens-lyon.fr",
+            "name": "Daniel Jost",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Gabriel Zala"
+        },
+        {
+            "name": "Peter Meister"
+        },
+        {
+            "name": "Todor Gitchev"
+        }
+    ],
+    "description": "An accessible simulation framework to model the interplay between chromatin and loop extrusion.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Loop modelling",
+                    "uri": "http://edamontology.org/operation_0481"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://gitlab.com/togop/3DPolyS-LE",
+    "language": [
+        "Fortran",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-18T22:10:21.315528Z",
+    "license": "MIT",
+    "name": "3DPolyS-LE",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac705",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: Recent studies suggest that the loop extrusion activity of Structural Maintenance of Chromosomes complexes is central to proper organization of genomes in vivo. Polymer physics-based modeling of chromosome structure has been instrumental to assess which structures such extrusion can create. Only few laboratories however have the technical and computational expertise to create in silico models combining dynamic features of chromatin and loop extruders. Here, we present 3DPolyS-LE, a self-contained, easy to use modeling and simulation framework allowing non-specialists to ask how specific properties of loop extruders and boundary elements impact on 3D chromosome structure. 3DPolyS-LE also provides algorithms to compare predictions with experimental Hi-C data. AVAILABILITY AND IMPLEMENTATION: Software available at https://gitlab.com/togop/3DPolyS-LE; implemented in Python and Fortran 2003 and supported on any Unix-based operating system (Linux and Mac OS). SUPPLEMENTARY INFORMATION: Supplementary information are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Gitchev T."
+                    },
+                    {
+                        "name": "Jost D."
+                    },
+                    {
+                        "name": "Meister P."
+                    },
+                    {
+                        "name": "Zala G."
+                    }
+                ],
+                "date": "2022-12-13T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "3DPolyS-LE: an accessible simulation framework to model the interplay between chromatin and loop extrusion"
+            },
+            "pmcid": "PMC9750120",
+            "pmid": "36355469"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "Chromosome conformation capture",
+            "uri": "http://edamontology.org/topic_3940"
+        },
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        }
+    ]
+}
diff --git a/data/4accpred/4accpred.biotools.json b/data/4accpred/4accpred.biotools.json
new file mode 100644
index 0000000000000..ecc3e00a4f277
--- /dev/null
+++ b/data/4accpred/4accpred.biotools.json
@@ -0,0 +1,102 @@
+{
+    "additionDate": "2023-01-25T09:56:08.039558Z",
+    "biotoolsCURIE": "biotools:4accpred",
+    "biotoolsID": "4accpred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "daiyun.huang@liverpool.ac.uk",
+            "name": "Daiyun Huang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Weakly supervised prediction of N4-acetyldeoxycytosine DNA modification from sequences",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                },
+                {
+                    "term": "Sequence motif discovery",
+                    "uri": "http://edamontology.org/operation_0238"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.rnamd.org/4accpred",
+    "lastUpdate": "2023-01-25T09:56:08.042167Z",
+    "license": "Other",
+    "name": "4acCPred",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.OMTN.2022.10.004",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsDNA methylation is one of the earliest epigenetic regulation mechanisms studied extensively, and it is critical for normal development, diseases, and gene expression. As a recently identified chemical modification of DNA, N4-acetyldeoxycytosine (4acC) was shown to be abundant in Arabidopsis and highly associated with gene expression and actively transcribed genes. Precise identification of 4acC is essential for studying its biological function. We proposed the 4acCPred, the first computational framework for predicting 4acC-carrying regions from Arabidopsis genomic DNA sequences. Since the existing 4acC data are not precise for a specific base but only report regions that are hundreds of bases long, we formulated the task as a weakly supervised learning problem and built 4acCPred using a multi-instance-based deep neural network. Both cross-validation and independent testing on the four datasets under different conditions show promising performance, with mean areas under the receiver operating characteristic curve (AUCs) of 0.9877 and 0.9899, respectively. 4acCPred also provides motif mining through model interpretation. The motifs found by 4acCPred are consistent with existing knowledge, indicating that the model successfully captured real biological signals. In addition, a user-friendly web server was built to facilitate 4acC prediction, motif visualization, and data access. Our framework and web server should serve as useful tools for 4acC research.",
+                "authors": [
+                    {
+                        "name": "Huang D."
+                    },
+                    {
+                        "name": "Meng J."
+                    },
+                    {
+                        "name": "Wang X."
+                    },
+                    {
+                        "name": "Wei Z."
+                    },
+                    {
+                        "name": "Zhou J."
+                    }
+                ],
+                "date": "2022-12-13T00:00:00Z",
+                "journal": "Molecular Therapy - Nucleic Acids",
+                "title": "4acCPred: Weakly supervised prediction of N4-acetyldeoxycytosine DNA modification from sequences"
+            },
+            "pmcid": "PMC9636570",
+            "pmid": "36381577"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Methylated DNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3674"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ]
+}
diff --git a/data/4d-fed-gnn/4d-fed-gnn.biotools.json b/data/4d-fed-gnn/4d-fed-gnn.biotools.json
new file mode 100644
index 0000000000000..4dae3fc003a17
--- /dev/null
+++ b/data/4d-fed-gnn/4d-fed-gnn.biotools.json
@@ -0,0 +1,70 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T10:05:03.802136Z",
+    "biotoolsCURIE": "biotools:4d-fed-gnn",
+    "biotoolsID": "4d-fed-gnn",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Islem Rekik"
+        },
+        {
+            "name": "Zeynep Gurler"
+        }
+    ],
+    "description": "Federated Brain Graph Evolution Prediction using Decentralized Connectivity Datasets with Temporally-varying Acquisitions.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/basiralab/4D-FedGNN-Plus",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T10:05:03.807367Z",
+    "license": "Not licensed",
+    "name": "4D-FED-GNN++",
+    "operatingSystem": [
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1109/TMI.2022.3225083",
+            "metadata": {
+                "abstract": "IEEEForeseeing the evolution of brain connectivity between anatomical regions from a baseline observation can propel early disease diagnosis and clinical decision making. Such task becomes challenging when learning from multiple decentralized datasets with missing timepoints (e.g., datasets collected from different hospitals with a varying sequence of acquisitions). Federated learning (FL) is an emerging paradigm that enables collaborative learning among multiple clients (i.e., hospitals) in a fully privacy-preserving fashion. However, to the best of our knowledge, there is no FL work that foresees the time-dependent brain connectivity evolution from a single timepoint &#x2013;let alone learning from non-iid decentralized longitudinal datasets with <italic>varying acquisition timepoints</italic>. In this paper, we propose the first FL framework to significantly boost the predictive performance of local hospitals with missing acquisition timepoints while benefiting from other hospitals with available data at those timepoints without sharing data. Specifically, we introduce 4D-FED-GNN+, a novel longitudinal federated GNN framework that works in (i) a uni-mode, where it acts as a graph self-encoder if the next timepoint is locally missing or (ii) in a dual-mode, where it concurrently acts as a graph generator and a self-encoder if the local follow-up data is available. Further, we propose a dual federation strategy, where (i) GNN layer-wise weight aggregation and (ii) pairwise GNN weight exchange between hospitals in a random order. To improve the performance of the poorly-conditioned hospitals (e.g., consecutive missing timepoints, intermediate missing timepoint), we further propose a second variant, namely 4D-FED-GNN++, which federates based on an ordering of the local hospitals computed using their incomplete sequential patterns. Our comprehensive experiments on real longitudinal datasets show that overall 4D-FED-GNN+ and 4D-FED-GNN++ significantly outperform benchmark methods. Our source code is available at https: //github.com/basiralab/4D-FedGNN-Plus.",
+                "authors": [
+                    {
+                        "name": "Gurler Z."
+                    },
+                    {
+                        "name": "Rekik I."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "IEEE Transactions on Medical Imaging",
+                "title": "Federated Brain Graph Evolution Prediction using Decentralized Connectivity Datasets with Temporally-varying Acquisitions"
+            },
+            "pmid": "36441899"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Workbench"
+    ],
+    "topic": [
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        }
+    ]
+}
diff --git a/data/4dr-gan/4dr-gan.biotools.json b/data/4dr-gan/4dr-gan.biotools.json
new file mode 100644
index 0000000000000..bda0fd88aed4e
--- /dev/null
+++ b/data/4dr-gan/4dr-gan.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-28T19:58:12.318455Z",
+    "biotoolsCURIE": "biotools:4dr-gan",
+    "biotoolsID": "4dr-gan",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Lingkun Gu"
+        },
+        {
+            "name": "Mei Yang"
+        },
+        {
+            "name": "Mo Weng"
+        },
+        {
+            "name": "Yingtao Jiang"
+        },
+        {
+            "name": "Yang Jiao",
+            "orcidid": "http://orcid.org/0000-0002-6390-2517"
+        }
+    ],
+    "description": "Digitally Predicting Protein Localization and Manipulating Protein Activity in Fluorescence Images Using Four-dimensional Reslicing GAN.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                },
+                {
+                    "term": "Subcellular localisation prediction",
+                    "uri": "http://edamontology.org/operation_2489"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/YangJiaoUSA/4DR-GAN",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-28T19:58:12.321100Z",
+    "license": "MIT",
+    "name": "4DR-GAN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac719",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: While multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. One solution is using deep neural networks to model the localization relationship between two proteins so that the localization of one protein can be digitally predicted. Furthermore, the input and predicted localization implicitly reflect the modeled relationship. Accordingly, observing the response of the prediction via manipulating input localization could provide an informative way to analyze the modeled relationships between the input and the predicted proteins. RESULTS: We propose a protein localization prediction (PLP) method using a cGAN named 4D Reslicing Generative Adversarial Network (4DR-GAN) to digitally generate additional channels. 4DR-GAN models the joint probability distribution of input and output proteins by simultaneously incorporating the protein localization signals in four dimensions including space and time. Because protein localization often correlates with protein activation state, based on accurate PLP, we further propose two novel tools: digital activation (DA) and digital inactivation (DI) to digitally activate and inactivate a protein, in order to observing the response of the predicted protein localization. Compared with genetic approaches, these tools allow precise spatial and temporal control. A comprehensive experiment on six pairs of proteins shows that 4DR-GAN achieves higher-quality PLP than Pix2Pix, and the DA and DI responses are consistent with the known protein functions. The proposed PLP method helps simultaneously visualize additional proteins, and the developed DA and DI tools provide guidance to study localization-based protein functions. AVAILABILITY AND IMPLEMENTATION: The open-source code is available at https://github.com/YangJiaoUSA/4DR-GAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Gu L."
+                    },
+                    {
+                        "name": "Jiang Y."
+                    },
+                    {
+                        "name": "Jiao Y."
+                    },
+                    {
+                        "name": "Weng M."
+                    },
+                    {
+                        "name": "Yang M."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Digitally predicting protein localization and manipulating protein activity in fluorescence images using 4D reslicing GAN"
+            },
+            "pmcid": "PMC9805574",
+            "pmid": "36373962"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        }
+    ]
+}
diff --git a/data/aau-net/aau-net.biotools.json b/data/aau-net/aau-net.biotools.json
new file mode 100644
index 0000000000000..ea353b6db4a20
--- /dev/null
+++ b/data/aau-net/aau-net.biotools.json
@@ -0,0 +1,62 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-09T13:49:01.874577Z",
+    "biotoolsCURIE": "biotools:aau-net",
+    "biotoolsID": "aau-net",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Gongping Chen"
+        }
+    ],
+    "description": "An adaptive attention U-net (AAU-net) to segment breast lesions automatically and stably from ultrasound images.",
+    "editPermission": {
+        "type": "public"
+    },
+    "homepage": "https://github.com/CGPxy/AAU-net",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-09T13:49:01.877130Z",
+    "license": "Not licensed",
+    "name": "AAU-net",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1109/TMI.2022.3226268",
+            "metadata": {
+                "abstract": "IEEEVarious deep learning methods have been proposed to segment breast lesions from ultrasound images. However, similar intensity distributions, variable tumor morphologies and blurred boundaries present challenges for breast lesions segmentation, especially for malignant tumors with irregular shapes. Considering the complexity of ultrasound images, we develop an adaptive attention U-net (AAU-net) to segment breast lesions automatically and stably from ultrasound images. Specifically, we introduce a hybrid adaptive attention module (HAAM), which mainly consists of a channel self-attention block and a spatial self-attention block, to replace the traditional convolution operation. Compared with the conventional convolution operation, the design of the hybrid adaptive attention module can help us capture more features under different receptive fields. Different from existing attention mechanisms, the HAAM module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation. Extensive experiments with several state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets show that our method has better performance on breast lesions segmentation. Furthermore, robustness analysis and external experiments demonstrate that our proposed AAU-net has better generalization performance in the breast lesion segmentation. Moreover, the HAAM module can be flexibly applied to existing network frameworks. The source code is available on https://github.com/CGPxy/AAU-net.",
+                "authors": [
+                    {
+                        "name": "Chen G."
+                    },
+                    {
+                        "name": "Dai Y."
+                    },
+                    {
+                        "name": "Li L."
+                    },
+                    {
+                        "name": "Yap M.H."
+                    },
+                    {
+                        "name": "Zhang J."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "IEEE Transactions on Medical Imaging",
+                "title": "AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images"
+            },
+            "pmid": "36455083"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Echography",
+            "uri": "http://edamontology.org/topic_3954"
+        }
+    ]
+}
diff --git a/data/accuvir/accuvir.biotools.json b/data/accuvir/accuvir.biotools.json
new file mode 100644
index 0000000000000..3aa8483fedd04
--- /dev/null
+++ b/data/accuvir/accuvir.biotools.json
@@ -0,0 +1,93 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-09T13:54:47.967501Z",
+    "biotoolsCURIE": "biotools:accuvir",
+    "biotoolsID": "accuvir",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "yannisun@cityu.edu.hk",
+            "name": "Yanni Sun",
+            "orcidid": "https://orcid.org/0000-0003-1373-8023",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "AccuVIR -- an Accurate VIRal genome assembler and polisher -- utilizes path searching and sampling in sequence alignment graphs to assemble or polish draft assembly of viral genomes.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome assembly",
+                    "uri": "http://edamontology.org/operation_0525"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Sequence alignment",
+                    "uri": "http://edamontology.org/operation_0292"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/rainyrubyzhou/AccuVIR",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-09T13:54:47.970133Z",
+    "license": "Not licensed",
+    "name": "AccuVIR",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC827",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: RNA viruses tend to mutate constantly. While many of the variants are neutral, some can lead to higher transmissibility or virulence. Accurate assembly of complete viral genomes enables the identification of underlying variants, which are essential for studying virus evolution and elucidating the relationship between genotypes and virus properties. Recently, third-generation sequencing platforms such as Nanopore sequencers have been used for real-time virus sequencing for Ebola, Zika, coronavirus disease 2019, etc. However, their high per-base error rate prevents the accurate reconstruction of the viral genome. RESULTS: In this work, we introduce a new tool, AccuVIR, for viral genome assembly and polishing using error-prone long reads. It can better distinguish sequencing errors from true variants based on the key observation that sequencing errors can disrupt the gene structures of viruses, which usually have a high density of coding regions. Our experimental results on both simulated and real third-generation sequencing data demonstrated its superior performance on generating more accurate viral genomes than generic assembly or polish tools. AVAILABILITY AND IMPLEMENTATION: The source code and the documentation of AccuVIR are available at https://github.com/rainyrubyzhou/AccuVIR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Cai D."
+                    },
+                    {
+                        "name": "Sun Y."
+                    },
+                    {
+                        "name": "Yu R."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "AccuVIR: an ACCUrate VIRal genome assembly tool for third-generation sequencing data"
+            },
+            "pmcid": "PMC9825286",
+            "pmid": "36571490"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/acinetobase/acinetobase.biotools.json b/data/acinetobase/acinetobase.biotools.json
new file mode 100644
index 0000000000000..7d9821f69307f
--- /dev/null
+++ b/data/acinetobase/acinetobase.biotools.json
@@ -0,0 +1,98 @@
+{
+    "additionDate": "2023-01-25T10:13:57.511222Z",
+    "biotoolsCURIE": "biotools:acinetobase",
+    "biotoolsID": "acinetobase",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "charles.vanderhenst@vub.vib.be",
+            "name": "Charles Van der Henst",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A database and repository of Acinetobacter strains",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://acinetobase.vib.be/",
+    "lastUpdate": "2023-01-25T10:13:57.513756Z",
+    "license": "GPL-3.0",
+    "name": "Acinetobase",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/DATABASE/BAAC099",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press.Acinetobacter baumannii is one of the most problematic nosocomial pathogens that can efficiently thrive within hospital settings, mainly due to resistances toward antibiotics, desiccation, disinfectants, human serum and oxidative stress. Recently, increased resistance against last-resort antibiotics earns this bacterium the highest priority concern classified by the Centers for Disease Control and Prevention and the World Health Organization. An obvious hallmark of this bacterium is the high heterogeneity observed among A. baumannii isolates, with a limited core genome. This feature complexifies the study of A. baumannii bacteria as an entity, subsequently reflected in a diversity of phenotypes of not only antimicrobial and environmental resistance but also virulence. A high degree of genome plasticity, along with the use of a limited subset of established strains, can lead to strain-specific observations, decreasing the global understanding of this pathogenic agent. Phenotypic variability of A. baumannii strains is easily observable such as with the macrocolony morphologies, in vitro and in vivo virulence, natural competence level, production of different capsular polysaccharide structures and cellular densities. Some strains encode an extensive amount of virulence factors, while others, including the established strains, lack several key ones. The lack/excess of genes or specific physiological processes might interfere with in vivo and in vitro experiments, thus providing a limited impact on the global understanding of Acinetobacter bacteria. As an answer to the high heterogeneity among A. baumannii strains, we propose a first comprehensive database that includes the bacterial strains and the associated phenotypic and genetic data. This new repository, freely accessible to the entire scientific community, allows selecting the best bacterial isolate(s) related to any biological question, using an efficient and fast exchange platform. Database URL: https://acinetobase.vib.be/",
+                "authors": [
+                    {
+                        "name": "Botzki A."
+                    },
+                    {
+                        "name": "Collier J."
+                    },
+                    {
+                        "name": "Valcek A."
+                    },
+                    {
+                        "name": "Van Der Henst C."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Database",
+                "title": "Acinetobase: The comprehensive database and repository of Acinetobacter strains"
+            },
+            "pmid": "36412325"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biological databases",
+            "uri": "http://edamontology.org/topic_3071"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        }
+    ]
+}
diff --git a/data/acl/acl.biotools.json b/data/acl/acl.biotools.json
new file mode 100644
index 0000000000000..1a3e641fa8921
--- /dev/null
+++ b/data/acl/acl.biotools.json
@@ -0,0 +1,93 @@
+{
+    "additionDate": "2023-02-09T13:57:42.181800Z",
+    "biotoolsCURIE": "biotools:acl",
+    "biotoolsID": "acl",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Shaoning Zeng",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A framework for adjusting the threshold parameters according to the image contrast. Three functions include Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL).",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Lqs-github/ACL",
+    "language": [
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-02-09T13:57:42.184195Z",
+    "license": "Not licensed",
+    "name": "ACL",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.BSPC.2022.104486",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdThe ground glass opacity (GGO) of the lung is one of the essential features of COVID-19. The GGO in computed tomography (CT) images has various features and low-intensity contrast between the GGO and edge structures. These problems pose significant challenges for segmenting the GGO. To tackle these problems, we propose a new threshold method for accurate segmentation of GGO. Specifically, we offer a framework for adjusting the threshold parameters according to the image contrast. Three functions include Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL). The lung is divided into three areas using the attention mechanism threshold. Further, the segmentation parameters of the attention mechanism thresholds of the three parts are adaptively adjusted according to the image contrast. Only the segmentation regions restricted by the lung segmentation results are retained. Extensive experiments on four COVID datasets show that ACL can segment GGO images at low contrast well. Compared with the state-of-the-art methods, the similarity Dice of the ACL segmentation results is improved by 8.9%, the average symmetry surface distance ASD is reduced by 23%, and the required computational power FLOPs are only 0.09% of those of deep learning models. For GGO segmentation, ACL is more lightweight, and the accuracy is higher. Code will be released at https://github.com/Lqs-github/ACL.",
+                "authors": [
+                    {
+                        "name": "Cheng Z."
+                    },
+                    {
+                        "name": "Gao Y."
+                    },
+                    {
+                        "name": "Huang C."
+                    },
+                    {
+                        "name": "Lv Q."
+                    },
+                    {
+                        "name": "Rao Y."
+                    },
+                    {
+                        "name": "Sun J."
+                    },
+                    {
+                        "name": "Yi Y."
+                    },
+                    {
+                        "name": "Zeng S."
+                    }
+                ],
+                "date": "2023-03-01T00:00:00Z",
+                "journal": "Biomedical Signal Processing and Control",
+                "title": "COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold"
+            },
+            "pmcid": "PMC9721288",
+            "pmid": "36505089"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Tomography",
+            "uri": "http://edamontology.org/topic_3452"
+        }
+    ]
+}
diff --git a/data/acorn/acorn.biotools.json b/data/acorn/acorn.biotools.json
new file mode 100644
index 0000000000000..df65f7f1d8f04
--- /dev/null
+++ b/data/acorn/acorn.biotools.json
@@ -0,0 +1,62 @@
+{
+    "additionDate": "2023-02-26T12:52:47.290095Z",
+    "biotoolsCURIE": "biotools:acorn",
+    "biotoolsID": "acorn",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "description": "Clinically Oriented antimicrobial Resistance surveillance Network (ACORN) as a lightweight but comprehensive platform, in which we combine clinical data collection with diagnostic stewardship, microbiological data collection and visualisation of the linked clinical-microbiology dataset.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Antimicrobial resistance prediction",
+                    "uri": "http://edamontology.org/operation_3482"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://acornamr.net",
+    "lastUpdate": "2023-02-26T12:52:47.292698Z",
+    "license": "CC-BY-4.0",
+    "name": "ACORN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.12688/WELLCOMEOPENRES.18317.1"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Microbiology",
+            "uri": "http://edamontology.org/topic_3301"
+        },
+        {
+            "term": "Paediatrics",
+            "uri": "http://edamontology.org/topic_3418"
+        }
+    ]
+}
diff --git a/data/acp_ms/acp_ms.biotools.json b/data/acp_ms/acp_ms.biotools.json
new file mode 100644
index 0000000000000..26ed1db888828
--- /dev/null
+++ b/data/acp_ms/acp_ms.biotools.json
@@ -0,0 +1,85 @@
+{
+    "additionDate": "2023-01-25T10:19:15.207217Z",
+    "biotoolsCURIE": "biotools:acp_ms",
+    "biotoolsID": "acp_ms",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dragonbw@163.com",
+            "name": "Bo Liao",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A prediction model  of anticancer peptides based on feature extraction.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Zhoucaimao1998/Zc",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T10:19:15.210506Z",
+    "name": "ACP_MS",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC462",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Anticancer peptides (ACPs) are bioactive peptides with antitumor activity and have become the most promising drugs in the treatment of cancer. Therefore, the accurate prediction of ACPs is of great significance to the research of cancer diseases. In the paper, we developed a more efficient prediction model called ACP_MS. Firstly, the monoMonoKGap method is used to extract the characteristic of anticancer peptide sequences and form the digital features. Then, the AdaBoost model is used to select the most discriminating features from the digital features. Finally, a stochastic gradient descent algorithm is introduced to identify anticancer peptide sequences. We adopt 7-fold cross-validation and independent test set validation, and the final accuracy of the main dataset reached 92.653% and 91.597%, respectively. The accuracy of the alternate dataset reached 98.678% and 98.317%, respectively. Compared with other advanced prediction models, the ACP_MS model improves the identification ability of anticancer peptide sequences. The data of this model can be downloaded from the public website for free https://github.com/Zhoucaimao1998/Zc.",
+                "authors": [
+                    {
+                        "name": "Jia R."
+                    },
+                    {
+                        "name": "Liao B."
+                    },
+                    {
+                        "name": "Peng D."
+                    },
+                    {
+                        "name": "Wu F."
+                    },
+                    {
+                        "name": "Zhou C."
+                    }
+                ],
+                "date": "2022-11-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "ACP_MS: prediction of anticancer peptides based on feature extraction"
+            },
+            "pmid": "36326080"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/acpred-bmf/acpred-bmf.biotools.json b/data/acpred-bmf/acpred-bmf.biotools.json
new file mode 100644
index 0000000000000..71b3ef200aade
--- /dev/null
+++ b/data/acpred-bmf/acpred-bmf.biotools.json
@@ -0,0 +1,106 @@
+{
+    "additionDate": "2023-02-09T14:03:36.007673Z",
+    "biotoolsCURIE": "biotools:acpred-bmf",
+    "biotoolsID": "acpred-bmf",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "muzengchao@sdu.edu.cn",
+            "name": "Zengchao Mu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xinqigong@ruc.edu.cn",
+            "name": "Xinqi Gong",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "ACPred-BMF server is used for anticancer peptide (ACP) prediction.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://mialab.ruc.edu.cn/ACPredBMFServer/",
+    "lastUpdate": "2023-02-09T14:03:36.010333Z",
+    "license": "Other",
+    "name": "ACPred-BMF",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41598-022-24404-1",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Cancer has become a major factor threatening human life and health. Under the circumstance that traditional treatment methods such as chemotherapy and radiotherapy are not highly specific and often cause severe side effects and toxicity, new treatment methods are urgently needed. Anticancer peptide drugs have low toxicity, stronger efficacy and specificity, and have emerged as a new type of cancer treatment drugs. However, experimental identification of anticancer peptides is time-consuming and expensive, and difficult to perform in a high-throughput manner. Computational identification of anticancer peptides can make up for the shortcomings of experimental identification. In this study, a deep learning-based predictor named ACPred-BMF is proposed for the prediction of anticancer peptides. This method uses the quantitative and qualitative properties of amino acids, binary profile feature to numerical representation for the peptide sequences. The Bidirectional LSTM network architecture is used in the model, and the attention mechanism is also considered. To alleviate the black-box problem of deep learning model prediction, we visualized the automatically extracted features and used the Shapley additive explanations algorithm to determine the importance of features to further understand the anticancer peptide mechanism. The results show that our method is one of the state-of-the-art anticancer peptide predictors. A web server as the implementation of ACPred-BMF that can be accessed via: http://mialab.ruc.edu.cn/ACPredBMFServer/.",
+                "authors": [
+                    {
+                        "name": "Gong X."
+                    },
+                    {
+                        "name": "Han B."
+                    },
+                    {
+                        "name": "Mu Z."
+                    },
+                    {
+                        "name": "Zeng C."
+                    },
+                    {
+                        "name": "Zhao N."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "ACPred-BMF: bidirectional LSTM with multiple feature representations for explainable anticancer peptide prediction"
+            },
+            "pmcid": "PMC9763336",
+            "pmid": "36535969"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/act/act.biotools.json b/data/act/act.biotools.json
index fd919be696368..b264589712310 100644
--- a/data/act/act.biotools.json
+++ b/data/act/act.biotools.json
@@ -77,7 +77,7 @@
         }
     ],
     "homepage": "https://www.michalopoulos.net/act/",
-    "lastUpdate": "2022-05-15T12:52:53.578359Z",
+    "lastUpdate": "2023-02-03T12:49:57.576074Z",
     "maturity": "Mature",
     "name": "Arabidopsis Co-expression Tool (ACT)",
     "operatingSystem": [
@@ -123,7 +123,7 @@
                         "name": "Zogopoulos V.L."
                     }
                 ],
-                "citationCount": 3,
+                "citationCount": 7,
                 "date": "2021-08-20T00:00:00Z",
                 "journal": "iScience",
                 "title": "Arabidopsis Coexpression Tool: a tool for gene coexpression analysis in Arabidopsis thaliana"
@@ -149,6 +149,7 @@
                         "name": "Zogopoulos V.L."
                     }
                 ],
+                "citationCount": 2,
                 "date": "2022-03-18T00:00:00Z",
                 "journal": "STAR Protocols",
                 "title": "Gene coexpression analysis in Arabidopsis thaliana based on public microarray data"
@@ -186,7 +187,7 @@
                         "name": "Westhead D.R."
                     }
                 ],
-                "citationCount": 125,
+                "citationCount": 127,
                 "date": "2006-07-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "Arabidopsis Co-expression Tool (ACT): Web server tools for microarray-based gene expression analysis"
@@ -198,37 +199,44 @@
             ]
         },
         {
-            "doi": "10.1111/j.1365-313X.2006.02681.x",
+            "doi": "10.3390/biology11071019",
             "metadata": {
-                "abstract": "We present a new WWW-based tool for plant gene analysis, the Arabidopsis Co-Expression Tool (ACT), based on a large Arabidopsis thaliana microarray data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression analysis tool allows users to identify genes whose expression patterns are correlated across selected experiments or the complete data set. Results are accompanied by estimates of the statistical significance of the correlation relationships, expressed as probability (P) and expectation (E) values. Additionally, highly ranked genes on a correlation list can be examined using the novel CLIQUE FINDER tool to determine the sets of genes most likely to be regulated in a similar manner. In combination, these tools offer three levels of analysis: creation of correlation lists of co-expressed genes, refinement of these lists using two-dimensional scatter plots, and dissection into cliques of co-regulated genes. We illustrate the applications of the software by analysing genes encoding functionally related proteins, as well as pathways involved in plant responses to environmental stimuli. These analyses demonstrate novel biological relationships underlying the observed gene co-expression patterns. To demonstrate the ability of the software to develop testable hypotheses on gene function within a defined biological process we have used the example of cell wall biosynthesis genes. The resource is freely available at http://www.arabidopsis.leeds.ac.uk/ACT/. © 2006 The Authors.",
+                "abstract": "© 2022 by the authors. Licensee MDPI, Basel, Switzerland.Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied ex-tensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensi-ble account of the steps required for performing a complete gene coexpression analysis in eukary-otic organisms. We comment on the use of RNA‐Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.",
                 "authors": [
                     {
-                        "name": "Gilmartin P.M."
+                        "name": "Iconomidou V.A."
                     },
                     {
-                        "name": "Jen C.-H."
+                        "name": "Malatras A."
                     },
                     {
-                        "name": "Manfield I.W."
+                        "name": "Michalopoulos I."
                     },
                     {
-                        "name": "Michalopoulos I."
+                        "name": "Papadopoulos K."
                     },
                     {
-                        "name": "Pinney J.W."
+                        "name": "Saxami G."
                     },
                     {
-                        "name": "Westhead D.R."
+                        "name": "Tsotra I."
                     },
                     {
-                        "name": "Willats W.G.T."
+                        "name": "Zogopoulos V.L."
                     }
                 ],
-                "citationCount": 64,
-                "date": "2006-04-01T00:00:00Z",
-                "journal": "Plant Journal",
-                "title": "The Arabidopsis co-expression tool (ACT): A WWW-based tool and database for microarray-based gene expression analysis"
+                "date": "2022-07-01T00:00:00Z",
+                "journal": "Biology",
+                "title": "Approaches in Gene Coexpression Analysis in Eukaryotes"
             },
+            "pmcid": "PMC9312353",
+            "pmid": "36101400",
+            "type": [
+                "Review"
+            ]
+        },
+        {
+            "doi": "10.1111/j.1365-313X.2006.02681.x",
             "pmid": "16623895",
             "type": [
                 "Other"
diff --git a/data/adappi/adappi.biotools.json b/data/adappi/adappi.biotools.json
new file mode 100644
index 0000000000000..723a559454fe8
--- /dev/null
+++ b/data/adappi/adappi.biotools.json
@@ -0,0 +1,85 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-09T14:06:14.421806Z",
+    "biotoolsCURIE": "biotools:adappi",
+    "biotoolsID": "adappi",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dqwei@sjtu.edu.cn",
+            "name": "Hongyan Wu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "hy.wu@siat.ac.cn",
+            "name": "Dongqing Wei",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "iIentification of novel protein functional modules via adaptive graph convolution networks in a protein-protein interaction network.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Metabolic network modelling",
+                    "uri": "http://edamontology.org/operation_3660"
+                },
+                {
+                    "term": "Protein interaction network analysis",
+                    "uri": "http://edamontology.org/operation_0276"
+                },
+                {
+                    "term": "Protein interaction network prediction",
+                    "uri": "http://edamontology.org/operation_3094"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/aI-area/AdaPPI",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-09T14:06:14.424452Z",
+    "license": "MIT",
+    "name": "AdaPPI",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC523",
+            "pmid": "36526282"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        },
+        {
+            "term": "Protein interaction experiment",
+            "uri": "http://edamontology.org/topic_3957"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        }
+    ]
+}
diff --git a/data/adipoq/adipoq.biotools.json b/data/adipoq/adipoq.biotools.json
new file mode 100644
index 0000000000000..13da4b4e64f12
--- /dev/null
+++ b/data/adipoq/adipoq.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T22:52:58.082514Z",
+    "biotoolsCURIE": "biotools:adipoq",
+    "biotoolsID": "adipoq",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dwachten@uni-bonn.de",
+            "name": "Dagmar Wachten",
+            "orcidid": "http://orcid.org/0000-0003-4800-6332",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "jan.hansen@uni-bonn.de",
+            "name": "Jan N. Hansen",
+            "orcidid": "http://orcid.org/0000-0002-0489-7535",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Katharina Sieckmann"
+        },
+        {
+            "name": "Philipp Leyendecker",
+            "orcidid": "http://orcid.org/0000-0002-4709-9218"
+        }
+    ],
+    "description": "A simple toolbox of two ImageJ plugins for quantifying adipocyte morphology and function in tissues and in vitro.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/hansenjn/AdipoQ",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-26T22:52:58.085306Z",
+    "license": "GPL-3.0",
+    "name": "AdipoQ",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1091/mbc.E21-11-0592",
+            "metadata": {
+                "abstract": "© 2022 Sieckmann et al.The different adipose tissues (ATs) can be distinguished according to their function. For example, white AT stores energy in form of lipids, whereas brown AT dissipates energy in the form of heat. These functional differences are represented in the respective adipocyte morphology; whereas white adipocytes contain large, unilocular lipid droplets, brown adipocytes contain smaller, multilocular lipid droplets. However, an automated, image analysis pipeline to comprehensively analyze adipocytes in vitro in cell culture as well as ex vivo in tissue sections is missing. We here present AdipoQ, an open-source software implemented as ImageJ plugins that allows us to analyze adipocytes in tissue sections and in vitro after histological and/or immunofluorescent labeling. AdipoQ is compatible with different imaging modalities and staining methods, allows batch processing of large datasets and simple post-hoc analysis, provides a broad band of parameters, and allows combining multiple fluorescent readouts. Therefore AdipoQ is of immediate use not only for basic research but also for clinical diagnosis.",
+                "authors": [
+                    {
+                        "name": "Gnad T."
+                    },
+                    {
+                        "name": "Hansen J.N."
+                    },
+                    {
+                        "name": "Huebecker M."
+                    },
+                    {
+                        "name": "Leyendecker P."
+                    },
+                    {
+                        "name": "Pfeifer A."
+                    },
+                    {
+                        "name": "Sieckmann K."
+                    },
+                    {
+                        "name": "Silva Ribeiro D.J."
+                    },
+                    {
+                        "name": "Wachten D."
+                    },
+                    {
+                        "name": "Winnerling N."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "Molecular Biology of the Cell",
+                "title": "AdipoQ—a simple, open-source software to quantify adipocyte morphology and function in tissues and in vitro"
+            },
+            "pmcid": "PMC9635306",
+            "pmid": "35947507"
+        }
+    ],
+    "toolType": [
+        "Plug-in",
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Lipids",
+            "uri": "http://edamontology.org/topic_0153"
+        }
+    ]
+}
diff --git a/data/agora/agora.biotools.json b/data/agora/agora.biotools.json
new file mode 100644
index 0000000000000..18f42d241bdfd
--- /dev/null
+++ b/data/agora/agora.biotools.json
@@ -0,0 +1,196 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-03T08:31:16.844540Z",
+    "biotoolsCURIE": "biotools:agora",
+    "biotoolsID": "agora",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "alexandra.louis@bio.ens.psl.eu",
+            "name": "Alexandra Louis",
+            "orcidid": "http://orcid.org/0000-0001-7032-5650",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ],
+            "url": "http://www.ibens.ens.fr/spip.php?article182"
+        },
+        {
+            "email": "hrc@ens.fr",
+            "name": "Hugues Roest Crollius",
+            "orcidid": "http://orcid.org/0000-0002-8209-173X",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ],
+            "url": "http://www.ibens.ens.fr/?rubrique43"
+        },
+        {
+            "email": "agora@bio.ens.psl.eu",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Matthieu Muffato",
+            "orcidid": "https://orcid.org/0000-0002-7860-3560",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ],
+            "url": "https://www.sanger.ac.uk/person/muffato-matthieu/"
+        },
+        {
+            "name": "IBENS - DYOGEN Team",
+            "typeEntity": "Institute",
+            "url": "http://www.ibens.ens.fr/?rubrique43&lang=en"
+        }
+    ],
+    "description": "AGORA stands for “Algorithm for Gene Order Reconstruction in Ancestors” .\n\nAGORA is used to generate ancestral genomes for the Genomicus online server for gene order comparison, and has been in constant use in the group since.",
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://github.com/DyogenIBENS/Agora"
+        },
+        {
+            "type": "Test data",
+            "url": "https://doi.org/10.5281/zenodo.7479506"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene report",
+                        "uri": "http://edamontology.org/data_0916"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Gene tree",
+                        "uri": "http://edamontology.org/data_3271"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Species tree",
+                        "uri": "http://edamontology.org/data_3272"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Ancestral reconstruction",
+                    "uri": "http://edamontology.org/operation_3745"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Genome report",
+                        "uri": "http://edamontology.org/data_2711"
+                    }
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/DyogenIBENS/Agora",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-03T13:46:31.605568Z",
+    "license": "CECILL-C",
+    "link": [
+        {
+            "note": "Github Repository",
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/DyogenIBENS/Agora"
+        },
+        {
+            "note": "Software Heritage Repository",
+            "type": [
+                "Other"
+            ],
+            "url": "https://archive.softwareheritage.org/swh:1:dir:c23224a7770a761f79815f551c1998c13e18fae7;origin=https://github.com/DyogenIBENS/Agora;visit=swh:1:snp:9dd70a7e94d86dc1ec6f59c91438a4f96888aeea;anchor=swh:1:rev:16fb65ccc9571c42ac2b0b248588347f5754a172"
+        }
+    ],
+    "maturity": "Mature",
+    "name": "AGORA",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "alouis",
+    "publication": [
+        {
+            "doi": "10.1038/s41559-022-01956-z",
+            "metadata": {
+                "abstract": "Ancestral sequence reconstruction is a fundamental aspect of molecular evolution studies and can trace small-scale sequence modifications through the evolution of genomes and species. In contrast, fine-grained reconstructions of ancestral genome organizations are still in their infancy, limiting our ability to draw comprehensive views of genome and karyotype evolution. Here we reconstruct the detailed gene contents and organizations of 624 ancestral vertebrate, plant, fungi, metazoan and protist genomes, 183 of which are near-complete chromosomal gene order reconstructions. Reconstructed ancestral genomes are similar to their descendants in terms of gene content as expected and agree precisely with reference cytogenetic and in silico reconstructions when available. By comparing successive ancestral genomes along the phylogenetic tree, we estimate the intra- and interchromosomal rearrangement history of all major vertebrate clades at high resolution. This freely available resource introduces the possibility to follow evolutionary processes at genomic scales in chronological order, across multiple clades and without relying on a single extant species as reference.",
+                "authors": [
+                    {
+                        "name": "Berthelot C."
+                    },
+                    {
+                        "name": "Louis A."
+                    },
+                    {
+                        "name": "Lucas J."
+                    },
+                    {
+                        "name": "Muffato M."
+                    },
+                    {
+                        "name": "Nguyen N.T.T."
+                    },
+                    {
+                        "name": "Roest Crollius H."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Nature Ecology and Evolution",
+                "title": "Reconstruction of hundreds of reference ancestral genomes across the eukaryotic kingdom"
+            },
+            "pmid": "36646945",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "genomicus",
+            "type": "usedBy"
+        },
+        {
+            "biotoolsID": "genomicus-fungi",
+            "type": "usedBy"
+        },
+        {
+            "biotoolsID": "genomicus-plants",
+            "type": "usedBy"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Phylogenomics",
+            "uri": "http://edamontology.org/topic_0194"
+        },
+        {
+            "term": "Phylogeny",
+            "uri": "http://edamontology.org/topic_0084"
+        }
+    ]
+}
diff --git a/data/airr_tools/airr_tools.biotools.json b/data/airr_tools/airr_tools.biotools.json
new file mode 100644
index 0000000000000..df50750a0ae45
--- /dev/null
+++ b/data/airr_tools/airr_tools.biotools.json
@@ -0,0 +1,21 @@
+{
+    "additionDate": "2023-02-22T10:44:50.553431Z",
+    "biotoolsCURIE": "biotools:airr_tools",
+    "biotoolsID": "airr_tools",
+    "description": "Github repository for the paper \"T cell receptor repertoire sequencing reveals chemotherapy-driven clonal expansion in colorectal liver metastases\" by Høye et al.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/eirikhoye/airr_tools",
+    "lastUpdate": "2023-02-22T10:44:50.556114Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/eirikhoye/airr_tools"
+        }
+    ],
+    "name": "airr_tools",
+    "owner": "eirikhoye"
+}
diff --git a/data/airrscape/airrscape.biotools.json b/data/airrscape/airrscape.biotools.json
new file mode 100644
index 0000000000000..3ab0ed566f846
--- /dev/null
+++ b/data/airrscape/airrscape.biotools.json
@@ -0,0 +1,129 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T00:16:20.718687Z",
+    "biotoolsCURIE": "biotools:airrscape",
+    "biotoolsID": "airrscape",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "eric.waltari@czbiohub.org",
+            "name": "Eric Waltari",
+            "orcidid": "http://orcid.org/0000-0001-6930-9645",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "john.pak@czbiohub.org",
+            "name": "John E. Pak",
+            "orcidid": "http://orcid.org/0000-0002-2998-9735",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Joan Wong",
+            "orcidid": "http://orcid.org/0000-0002-7849-6320"
+        },
+        {
+            "name": "Krista M. McCutcheon",
+            "orcidid": "http://orcid.org/0000-0003-1942-5175"
+        },
+        {
+            "name": "Saba Nafees",
+            "orcidid": "http://orcid.org/0000-0002-3292-7703"
+        }
+    ],
+    "description": "An interactive tool for exploring B-cell receptor repertoires and antibody responses.\n\nTo run AIRRscape, clone the repo and open the app.R file in your RStudio, then click \"Run App\". As a Shiny app, it can run as a window of RStudio, or as a tab in a web browser (recommended).",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                },
+                {
+                    "term": "Side chain modelling",
+                    "uri": "http://edamontology.org/operation_0480"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ewaltari.shinyapps.io/airrscape2/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-17T00:16:20.722128Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/czbiohub/AIRRscape"
+        }
+    ],
+    "name": "AIRRscape",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/journal.pcbi.1010052",
+            "metadata": {
+                "abstract": "© 2022 Waltari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The sequencing of antibody repertoires of B-cells at increasing coverage and depth has led to the identification of vast numbers of immunoglobulin heavy and light chains. However, the size and complexity of these Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) datasets makes it difficult to perform exploratory analyses. To aid in data exploration, we have developed AIRRscape, an R Shiny-based interactive web browser application that enables B-cell receptor (BCR) and antibody feature discovery through comparisons among multiple repertoires. Using AIRR-seq data as input, AIRRscape starts by aggregating and sorting repertoires into interactive and explorable bins of germline V-gene, germline J-gene, and CDR3 length, providing a high-level view of the entire repertoire. Interesting subsets of repertoires can be quickly identified and selected, and then network topologies of CDR3 motifs can be generated for further exploration. Here we demonstrate AIRRscape using patient BCR repertoires and sequences of published monoclonal antibodies to investigate patterns of humoral immunity to three viral pathogens: SARS-CoV-2, HIV-1, and DENV (dengue virus). AIRRscape reveals convergent antibody sequences among datasets for all three pathogens, although HIV-1 antibody datasets display limited convergence and idiosyncratic responses. We have made AIRRscape available as a web-based Shiny application, along with code on GitHub to encourage its open development and use by immuno-informaticians, virologists, immunologists, vaccine developers, and other scientists that are interested in exploring and comparing multiple immune receptor repertoires.",
+                "authors": [
+                    {
+                        "name": "McCutcheon K.M."
+                    },
+                    {
+                        "name": "Nafees S."
+                    },
+                    {
+                        "name": "Pak J.E."
+                    },
+                    {
+                        "name": "Waltari E."
+                    },
+                    {
+                        "name": "Wong J."
+                    }
+                ],
+                "date": "2022-09-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "AIRRscape: An interactive tool for exploring B-cell receptor repertoires and antibody responses"
+            },
+            "pmcid": "PMC9524643",
+            "pmid": "36126074"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Immunogenetics",
+            "uri": "http://edamontology.org/topic_3930"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Vaccinology",
+            "uri": "http://edamontology.org/topic_3966"
+        }
+    ]
+}
diff --git a/data/alphafill/alphafill.biotools.json b/data/alphafill/alphafill.biotools.json
new file mode 100644
index 0000000000000..c368e2f3dba70
--- /dev/null
+++ b/data/alphafill/alphafill.biotools.json
@@ -0,0 +1,129 @@
+{
+    "additionDate": "2023-01-25T10:39:37.145358Z",
+    "biotoolsCURIE": "biotools:alphafill",
+    "biotoolsID": "alphafill",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Ida de Vries"
+        },
+        {
+            "name": "Maarten L Hekkelman"
+        }
+    ],
+    "description": "AlphaFill is an algorithm based on sequence and structure similarity that “transplants” missing compounds to the AlphaFold models. By adding the molecular context to the protein structures, the models can be more easily appreciated in terms of function and structure integrity.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                },
+                {
+                    "term": "Protein structure validation",
+                    "uri": "http://edamontology.org/operation_0321"
+                },
+                {
+                    "term": "Residue distance calculation",
+                    "uri": "http://edamontology.org/operation_2950"
+                },
+                {
+                    "term": "Sequence alignment editing",
+                    "uri": "http://edamontology.org/operation_3081"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://alphafill.eu",
+    "language": [
+        "C++"
+    ],
+    "lastUpdate": "2023-01-25T10:39:37.148046Z",
+    "license": "BSD-2-Clause",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "http://rsync.alphafill.eu/alphafill"
+        },
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://alphafill.eu/alphafill.json.schema"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/PDB-REDO/alphafill"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://zenodo.org/record/6706668#.Y2EXV3bP2Uk"
+        }
+    ],
+    "name": "AlphaFill",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41592-022-01685-Y",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Artificial intelligence-based protein structure prediction approaches have had a transformative effect on biomolecular sciences. The predicted protein models in the AlphaFold protein structure database, however, all lack coordinates for small molecules, essential for molecular structure or function: hemoglobin lacks bound heme; zinc-finger motifs lack zinc ions essential for structural integrity and metalloproteases lack metal ions needed for catalysis. Ligands important for biological function are absent too; no ADP or ATP is bound to any of the ATPases or kinases. Here we present AlphaFill, an algorithm that uses sequence and structure similarity to ‘transplant’ such ‘missing’ small molecules and ions from experimentally determined structures to predicted protein models. The algorithm was successfully validated against experimental structures. A total of 12,029,789 transplants were performed on 995,411 AlphaFold models and are available together with associated validation metrics in the alphafill.eu databank, a resource to help scientists make new hypotheses and design targeted experiments.",
+                "authors": [
+                    {
+                        "name": "Hekkelman M.L."
+                    },
+                    {
+                        "name": "Joosten R.P."
+                    },
+                    {
+                        "name": "Perrakis A."
+                    },
+                    {
+                        "name": "de Vries I."
+                    }
+                ],
+                "citationCount": 2,
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Nature Methods",
+                "title": "AlphaFill: enriching AlphaFold models with ligands and cofactors"
+            },
+            "pmid": "36424442"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Database portal",
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Protein structural motifs and surfaces",
+            "uri": "http://edamontology.org/topic_0166"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        },
+        {
+            "term": "Surgery",
+            "uri": "http://edamontology.org/topic_3421"
+        }
+    ]
+}
diff --git a/data/alphapeptdeep/alphapeptdeep.biotools.json b/data/alphapeptdeep/alphapeptdeep.biotools.json
new file mode 100644
index 0000000000000..4f78c6b67941c
--- /dev/null
+++ b/data/alphapeptdeep/alphapeptdeep.biotools.json
@@ -0,0 +1,132 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-28T19:42:59.541921Z",
+    "biotoolsCURIE": "biotools:alphapeptdeep",
+    "biotoolsID": "alphapeptdeep",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Xie-Xuan Zhou"
+        },
+        {
+            "name": "Matthias Mann",
+            "orcidid": "http://orcid.org/0000-0003-1292-4799"
+        },
+        {
+            "name": "Maximillian T. Strauss",
+            "orcidid": "http://orcid.org/0000-0003-3320-6833"
+        },
+        {
+            "name": "Wen-Feng Zeng",
+            "orcidid": "http://orcid.org/0000-0003-4325-2147"
+        }
+    ],
+    "description": "A modular deep learning framework to predict peptide properties for proteomics.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deisotoping",
+                    "uri": "http://edamontology.org/operation_3629"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                },
+                {
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                },
+                {
+                    "term": "Retention time prediction",
+                    "uri": "http://edamontology.org/operation_3633"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MannLabs/alphapeptdeep",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-28T19:42:59.544575Z",
+    "license": "Apache-2.0",
+    "name": "AlphaPeptDeep",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/s41467-022-34904-3",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides (https://github.com/MannLabs/alphapeptdeep). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition (https://github.com/MannLabs/PeptDeep-HLA).",
+                "authors": [
+                    {
+                        "name": "Ammar C."
+                    },
+                    {
+                        "name": "Bludau I."
+                    },
+                    {
+                        "name": "Mann M."
+                    },
+                    {
+                        "name": "Strauss M.T."
+                    },
+                    {
+                        "name": "Voytik E."
+                    },
+                    {
+                        "name": "Wahle M."
+                    },
+                    {
+                        "name": "Willems S."
+                    },
+                    {
+                        "name": "Zeng W.-F."
+                    },
+                    {
+                        "name": "Zhou X.-X."
+                    }
+                ],
+                "citationCount": 2,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics"
+            },
+            "pmcid": "PMC9700817",
+            "pmid": "36433986"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein modifications",
+            "uri": "http://edamontology.org/topic_0601"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/alvascience/alvascience.biotools.json b/data/alvascience/alvascience.biotools.json
new file mode 100644
index 0000000000000..5f6a9a878a05a
--- /dev/null
+++ b/data/alvascience/alvascience.biotools.json
@@ -0,0 +1,100 @@
+{
+    "additionDate": "2023-01-25T10:49:03.531191Z",
+    "biotoolsCURIE": "biotools:alvascience",
+    "biotoolsID": "alvascience",
+    "confidence_flag": "tool",
+    "cost": "Commercial",
+    "credit": [
+        {
+            "email": "andrea.mauri@alvascience.com",
+            "name": "Andrea Mauri",
+            "orcidid": "https://orcid.org/0000-0002-1966-4347",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Matteo Bertola"
+        }
+    ],
+    "description": "Alvascience: A New Software Suite for the QSAR Workflow Applied to the Blood-Brain Barrier Permeability",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.alvascience.com",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T10:49:03.533590Z",
+    "license": "Proprietary",
+    "name": "Alvascience",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3390/IJMS232112882",
+            "metadata": {
+                "abstract": "© 2022 by the authors.Quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) are established techniques to relate endpoints to molecular features. We present the Alvascience software suite that takes care of the whole QSAR/QSPR workflow necessary to use models to predict endpoints for untested molecules. The first step, data curation, is covered by alvaMolecule. Features such as molecular descriptors and fingerprints are generated by using alvaDesc. Models are built and validated with alvaModel. The models can then be deployed and used on new molecules by using alvaRunner. We use these software tools on a real case scenario to predict the blood–brain barrier (BBB) permeability. The resulting predictive models have accuracy equal or greater than 0.8. The models are bundled in an alvaRunner project available on the Alvascience website.",
+                "authors": [
+                    {
+                        "name": "Bertola M."
+                    },
+                    {
+                        "name": "Mauri A."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "International Journal of Molecular Sciences",
+                "title": "Alvascience: A New Software Suite for the QSAR Workflow Applied to the Blood–Brain Barrier Permeability"
+            },
+            "pmcid": "PMC9655980",
+            "pmid": "36361669"
+        }
+    ],
+    "toolType": [
+        "Desktop application",
+        "Suite"
+    ],
+    "topic": [
+        {
+            "term": "Data submission, annotation and curation",
+            "uri": "http://edamontology.org/topic_0219"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/alveolus_analysis/alveolus_analysis.biotools.json b/data/alveolus_analysis/alveolus_analysis.biotools.json
new file mode 100644
index 0000000000000..c9cd9671fc803
--- /dev/null
+++ b/data/alveolus_analysis/alveolus_analysis.biotools.json
@@ -0,0 +1,103 @@
+{
+    "additionDate": "2023-02-09T14:14:54.062031Z",
+    "biotoolsCURIE": "biotools:alveolus_analysis",
+    "biotoolsID": "alveolus_analysis",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "pbelvitc@uic.edu",
+            "name": "Patrick Belvitch",
+            "orcidid": "https://orcid.org/0000-0002-2404-8346",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A web browser-based tool to analyze lung intravital microscopy.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/uic-evl/AlveolusAnalysis",
+    "language": [
+        "JavaScript",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-09T14:14:54.064543Z",
+    "license": "Other",
+    "name": "Alveolus Analysis",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12890-022-02274-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Acute lung injury and the acute respiratory distress syndrome are characterized by pulmonary inflammation, reduced endothelial barrier integrity and filling of the alveolar space with protein rich edema fluid and infiltrating leukocytes. Animal models are critical to uncovering the pathologic mechanisms of this devastating syndrome. Intravital imaging of the intact lung via two-photon intravital microscopy has proven a valuable method to investigate lung injury in small rodent models through characterization of inflammatory cells and vascular changes in real time. However, respiratory motion complicates the analysis of these time series images and requires selective data extraction to stabilize the image. Consequently, analysis of individual alveoli may not provide a complete picture of the integrated mechanical, vascular and inflammatory processes occurring simultaneously in the intact lung. To address these challenges, we developed a web browser-based visualization application named Alveolus Analysis to process, analyze and graphically display intravital lung microscopy data. Results: The designed tool takes raw temporal image data as input, performs image preprocessing and feature extraction offline, and visualizes the extracted information in a web browser-based interface. The interface allows users to explore multiple experiments in three panels corresponding to different levels of detail: summary statistics of alveolar/neutrophil behavior, characterization of alveolar dynamics including lung edema and inflammatory cells at specific time points, and cross-experiment analysis. We performed a case study on the utility of the visualization with two members or our research team and they found the tool useful because of its ability to preprocess data consistently and visualize information in a digestible and informative format. Conclusions: Application of our software tool, Alveolus Analysis, to intravital lung microscopy data has the potential to enhance the information gained from these experiments and provide new insights into the pathologic mechanisms of inflammatory lung injury.",
+                "authors": [
+                    {
+                        "name": "Belvitch P."
+                    },
+                    {
+                        "name": "Burks A.T."
+                    },
+                    {
+                        "name": "Dong Y."
+                    },
+                    {
+                        "name": "Dudek S.M."
+                    },
+                    {
+                        "name": "Elisabeta Marai G."
+                    },
+                    {
+                        "name": "Htwe Y.M."
+                    },
+                    {
+                        "name": "Politowicz A.L."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Pulmonary Medicine",
+                "title": "Alveolus analysis: a web browser-based tool to analyze lung intravital microscopy"
+            },
+            "pmcid": "PMC9759058",
+            "pmid": "36528564"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/amp-bert/amp-bert.biotools.json b/data/amp-bert/amp-bert.biotools.json
new file mode 100644
index 0000000000000..4159fe491d5b8
--- /dev/null
+++ b/data/amp-bert/amp-bert.biotools.json
@@ -0,0 +1,97 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-09T14:17:57.995019Z",
+    "biotoolsCURIE": "biotools:amp-bert",
+    "biotoolsID": "amp-bert",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hjnam@gist.ac.kr",
+            "name": "Hojung Nam",
+            "orcidid": "https://orcid.org/0000-0002-5109-9114",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Prediction of antimicrobial peptide function based on a BERT model.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Antimicrobial resistance prediction",
+                    "uri": "http://edamontology.org/operation_3482"
+                },
+                {
+                    "term": "Sequence classification",
+                    "uri": "http://edamontology.org/operation_2995"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/GIST-CSBL/AMP-BERT",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-09T14:17:57.997477Z",
+    "license": "GPL-3.0",
+    "name": "AMP-BERT",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1002/PRO.4529",
+            "metadata": {
+                "abstract": "© 2022 The Protein Society.Antimicrobial resistance is a growing health concern. Antimicrobial peptides (AMPs) disrupt harmful microorganisms by nonspecific mechanisms, making it difficult for microbes to develop resistance. Accordingly, they are promising alternatives to traditional antimicrobial drugs. In this study, we developed an improved AMP classification model, called AMP-BERT. We propose a deep learning model with a fine-tuned didirectional encoder representations from transformers (BERT) architecture designed to extract structural/functional information from input peptides and identify each input as AMP or non-AMP. We compared the performance of our proposed model and other machine/deep learning-based methods. Our model, AMP-BERT, yielded the best prediction results among all models evaluated with our curated external dataset. In addition, we utilized the attention mechanism in BERT to implement an interpretable feature analysis and determine the specific residues in known AMPs that contribute to peptide structure and antimicrobial function. The results show that AMP-BERT can capture the structural properties of peptides for model learning, enabling the prediction of AMPs or non-AMPs from input sequences. AMP-BERT is expected to contribute to the identification of candidate AMPs for functional validation and drug development. The code and dataset for the fine-tuning of AMP-BERT is publicly available at https://github.com/GIST-CSBL/AMP-BERT.",
+                "authors": [
+                    {
+                        "name": "Lee H."
+                    },
+                    {
+                        "name": "Lee I."
+                    },
+                    {
+                        "name": "Lee S."
+                    },
+                    {
+                        "name": "Nam H."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Protein Science",
+                "title": "AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model"
+            },
+            "pmcid": "PMC9793967",
+            "pmid": "36461699"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Microbiology",
+            "uri": "http://edamontology.org/topic_3301"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/ampbenchmark/ampbenchmark.biotools.json b/data/ampbenchmark/ampbenchmark.biotools.json
new file mode 100644
index 0000000000000..81725a2300032
--- /dev/null
+++ b/data/ampbenchmark/ampbenchmark.biotools.json
@@ -0,0 +1,147 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-20T02:22:08.626953Z",
+    "biotoolsCURIE": "biotools:ampbenchmark",
+    "biotoolsID": "ampbenchmark",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "michalburdukiewicz@gmail.com",
+            "name": "Michał Burdukiewicz",
+            "orcidid": "http://orcid.org/0000-0001-8926-582X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Katarzyna Sidorczuk",
+            "orcidid": "http://orcid.org/0000-0001-6576-9054"
+        },
+        {
+            "name": "Paweł Mackiewicz",
+            "orcidid": "http://orcid.org/0000-0003-4855-497X"
+        },
+        {
+            "name": "Przemysław Gagat",
+            "orcidid": "http://orcid.org/0000-0001-9077-439X"
+        }
+    ],
+    "description": "Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Adhesin prediction",
+                    "uri": "http://edamontology.org/operation_3968"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Virulence prediction",
+                    "uri": "http://edamontology.org/operation_3461"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://BioGenies.info/AMPBenchmark",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-20T02:22:08.630175Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/BioGenies/AMPBenchmark"
+        }
+    ],
+    "name": "AMPBenchmark",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bib/bbac343",
+            "metadata": {
+                "abstract": "© 2022 The Author(s).Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.",
+                "authors": [
+                    {
+                        "name": "Bakala L."
+                    },
+                    {
+                        "name": "Burdukiewicz M."
+                    },
+                    {
+                        "name": "Cooke I.R."
+                    },
+                    {
+                        "name": "Fingerhut L.C.H.W."
+                    },
+                    {
+                        "name": "Gagat P."
+                    },
+                    {
+                        "name": "Kala J."
+                    },
+                    {
+                        "name": "Kolenda R."
+                    },
+                    {
+                        "name": "MacKiewicz P."
+                    },
+                    {
+                        "name": "Pietluch F."
+                    },
+                    {
+                        "name": "Rafacz D."
+                    },
+                    {
+                        "name": "Rodiger S."
+                    },
+                    {
+                        "name": "Sidorczuk K."
+                    },
+                    {
+                        "name": "Slowik J."
+                    }
+                ],
+                "citationCount": 4,
+                "date": "2022-09-01T00:00:00Z",
+                "journal": "Briefings in Bioinformatics",
+                "title": "Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data"
+            },
+            "pmcid": "PMC9487607",
+            "pmid": "35988923"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Microbiology",
+            "uri": "http://edamontology.org/topic_3301"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/annotapipeline/annotapipeline.biotools.json b/data/annotapipeline/annotapipeline.biotools.json
new file mode 100644
index 0000000000000..54c3f43d6de73
--- /dev/null
+++ b/data/annotapipeline/annotapipeline.biotools.json
@@ -0,0 +1,118 @@
+{
+    "additionDate": "2023-02-09T14:21:54.749056Z",
+    "biotoolsCURIE": "biotools:annotapipeline",
+    "biotoolsID": "annotapipeline",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "glauber.wagner@ufsc.br",
+            "name": "Glauber Wagner",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An integrated tool to annotate eukaryotic proteins using multi-omics data.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/bioinformatics-ufsc/AnnotaPipeline",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-09T14:21:54.752991Z",
+    "license": "Apache-2.0",
+    "name": "AnnotaPipeline",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FGENE.2022.1020100",
+            "metadata": {
+                "abstract": "Copyright © 2022 Maia, Filho, Kawagoe, Teixeira Soratto, Moreira, Grisard and Wagner.Assignment of gene function has been a crucial, laborious, and time-consuming step in genomics. Due to a variety of sequencing platforms that generates increasing amounts of data, manual annotation is no longer feasible. Thus, the need for an integrated, automated pipeline allowing the use of experimental data towards validation of in silico prediction of gene function is of utmost relevance. Here, we present a computational workflow named AnnotaPipeline that integrates distinct software and data types on a proteogenomic approach to annotate and validate predicted features in genomic sequences. Based on FASTA (i) nucleotide or (ii) protein sequences or (iii) structural annotation files (GFF3), users can input FASTQ RNA-seq data, MS/MS data from mzXML or similar formats, as the pipeline uses both transcriptomic and proteomic information to corroborate annotations and validate gene prediction, providing transcription and expression evidence for functional annotation. Reannotation of the available Arabidopsis thaliana, Caenorhabditis elegans, Candida albicans, Trypanosoma cruzi, and Trypanosoma rangeli genomes was performed using the AnnotaPipeline, resulting in a higher proportion of annotated proteins and a reduced proportion of hypothetical proteins when compared to the annotations publicly available for these organisms. AnnotaPipeline is a Unix-based pipeline developed using Python and is available at: https://github.com/bioinformatics-ufsc/AnnotaPipeline.",
+                "authors": [
+                    {
+                        "name": "Filho V.B."
+                    },
+                    {
+                        "name": "Grisard E.C."
+                    },
+                    {
+                        "name": "Kawagoe E.K."
+                    },
+                    {
+                        "name": "Maia G.A."
+                    },
+                    {
+                        "name": "Moreira R.S."
+                    },
+                    {
+                        "name": "Teixeira Soratto T.A."
+                    },
+                    {
+                        "name": "Wagner G."
+                    }
+                ],
+                "date": "2022-11-22T00:00:00Z",
+                "journal": "Frontiers in Genetics",
+                "title": "AnnotaPipeline: An integrated tool to annotate eukaryotic proteins using multi-omics data"
+            },
+            "pmcid": "PMC9723129",
+            "pmid": "36482896"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Script",
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Proteogenomics",
+            "uri": "http://edamontology.org/topic_3922"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/annotate_my_genomes/annotate_my_genomes.biotools.json b/data/annotate_my_genomes/annotate_my_genomes.biotools.json
new file mode 100644
index 0000000000000..e9926ad0ef7e6
--- /dev/null
+++ b/data/annotate_my_genomes/annotate_my_genomes.biotools.json
@@ -0,0 +1,140 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-09T14:26:03.695492Z",
+    "biotoolsCURIE": "biotools:annotate_my_genomes",
+    "biotoolsID": "annotate_my_genomes",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "cfarkas@ucsc.cl",
+            "name": "Carlos Farkas",
+            "orcidid": "https://orcid.org/0000-0002-6245-2622",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "etarisal@udec.cl",
+            "name": "Estefanía Tarifeño-Saldivia",
+            "orcidid": "https://orcid.org/0000-0001-5311-2661",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "tcaprile@udec.cl",
+            "name": "Teresa Caprile",
+            "orcidid": "https://orcid.org/0000-0002-0897-7049",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "annotate_my_genomes is a pipeline that aims to annotate genome-guided transcriptome assemblies from StringTie, coming from long read RNA-Seq alignments in vertebrate genomes (i.e. PacBio technology)",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/cfarkas/annotate_my_genomes/wiki/annotate_my_genomes-benchmarking"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "De-novo assembly",
+                    "uri": "http://edamontology.org/operation_0524"
+                },
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Transcriptome assembly",
+                    "uri": "http://edamontology.org/operation_3258"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/cfarkas/annotate_my_genomes",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-09T14:26:03.698361Z",
+    "license": "MIT",
+    "name": "annotate_my_genomes",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/GIGASCIENCE/GIAC099",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press GigaScience.Background: The advancement of hybrid sequencing technologies is increasingly expanding genome assemblies that are often annotated using hybrid sequencing transcriptomics, leading to improved genome characterization and the identification of novel genes and isoforms in a wide variety of organisms. Results: We developed an easy-to-use genome-guided transcriptome annotation pipeline that uses assembled transcripts from hybrid sequencing data as input and distinguishes between coding and long non-coding RNAs by integration of several bioinformatic approaches, including gene reconciliation with previous annotations in GTF format. We demonstrated the efficiency of this approach by correctly assembling and annotating all exons from the chicken SCO-spondin gene (containing more than 105 exons), including the identification of missing genes in the chicken reference annotations by homology assignments. Conclusions: Our method helps to improve the current transcriptome annotation of the chicken brain. Our pipeline, implemented on Anaconda/Nextflow and Docker is an easy-to-use package that can be applied to a broad range of species, tissues, and research areas helping to improve and reconcile current annotations. The code and datasets are publicly available at https://github.com/cfarkas/annotate_my_genomes",
+                "authors": [
+                    {
+                        "name": "Candia-Herrera D."
+                    },
+                    {
+                        "name": "Caprile T."
+                    },
+                    {
+                        "name": "Farkas C."
+                    },
+                    {
+                        "name": "Haigh J.J."
+                    },
+                    {
+                        "name": "Mella A."
+                    },
+                    {
+                        "name": "Olivero M.G."
+                    },
+                    {
+                        "name": "Recabal A."
+                    },
+                    {
+                        "name": "Tarifeno-Saldivia E."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "GigaScience",
+                "title": "annotate_my_genomes: an easy-to-use pipeline to improve genome annotation and uncover neglected genes by hybrid RNA sequencing"
+            },
+            "pmcid": "PMC9724561",
+            "pmid": "36472574"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Script",
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/antiage-db/antiage-db.biotools.json b/data/antiage-db/antiage-db.biotools.json
new file mode 100644
index 0000000000000..46f0d730c9a5b
--- /dev/null
+++ b/data/antiage-db/antiage-db.biotools.json
@@ -0,0 +1,78 @@
+{
+    "additionDate": "2023-01-25T10:55:42.436257Z",
+    "biotoolsCURIE": "biotools:antiage-db",
+    "biotoolsID": "antiage-db",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "atzakos@uoi.gr",
+            "name": "Andreas G. Tzakos",
+            "orcidid": "https://orcid.org/0000-0001-6391-0288",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "hperez@ucam.edu",
+            "name": "Horacio Pérez-Sánchez",
+            "orcidid": "https://orcid.org/0000-0003-4468-7898",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A database and server termed ANTIAGE-DB that allows the prediction of the anti-aging potential of target compounds.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://bio-hpc.ucam.edu/anti-age-db",
+    "lastUpdate": "2023-01-25T10:55:42.438658Z",
+    "license": "Other",
+    "name": "ANTIAGE-DB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3390/ANTIOX11112268",
+            "pmcid": "PMC9686885",
+            "pmid": "36421454"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Enzymes",
+            "uri": "http://edamontology.org/topic_0821"
+        },
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/antibody_registry/antibody_registry.biotools.json b/data/antibody_registry/antibody_registry.biotools.json
new file mode 100644
index 0000000000000..6ca22f8b67e71
--- /dev/null
+++ b/data/antibody_registry/antibody_registry.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T11:04:55.050911Z",
+    "biotoolsCURIE": "biotools:antibody_registry",
+    "biotoolsID": "antibody_registry",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "abandrowski@ucsd.edu",
+            "name": "Anita Bandrowski",
+            "orcidid": "https://orcid.org/0000-0002-5497-0243",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "The Antibody Registry is a public, open database that enables citation of antibodies by providing a persistent record for any antibody-based reagent used in a publication.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://antibodyregistry.org",
+    "language": [
+        "JavaScript",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T11:04:55.053288Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/MetaCell/scicrunch-antibody-registry"
+        }
+    ],
+    "name": "Antibody Registry",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC927",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Antibodies are ubiquitous key biological research resources yet are tricky to use as they are prone to performance issues and represent a major source of variability across studies. Understanding what antibody was used in a published study is therefore necessary to repeat and/or interpret a given study. However, antibody reagents are still frequently not cited with sufficient detail to determine which antibody was used in experiments. The Antibody Registry is a public, open database that enables citation of antibodies by providing a persistent record for any antibody-based reagent used in a publication. The registry is the authority for antibody Research Resource Identifiers, or RRIDs, which are requested or required by hundreds of journals seeking to improve the citation of these key resources. The registry is the most comprehensive listing of persistently identified antibody reagents used in the scientific literature. Data contributors span individual authors who use antibodies to antibody companies, which provide their entire catalogs including discontinued items. Unlike many commercial antibody listing sites which tend to remove reagents no longer sold, registry records persist, providing an interface between a fast-moving commercial marketplace and the static scientific literature. The Antibody Registry (RRID:SCR_006397) https://antibodyregistry.org.",
+                "authors": [
+                    {
+                        "name": "Bandrowski A."
+                    },
+                    {
+                        "name": "Eckmann P."
+                    },
+                    {
+                        "name": "Grethe J."
+                    },
+                    {
+                        "name": "Martone M.E."
+                    },
+                    {
+                        "name": "Pairish M."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "The Antibody Registry: ten years of registering antibodies"
+            },
+            "pmcid": "PMC9825422",
+            "pmid": "36370112"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biotechnology",
+            "uri": "http://edamontology.org/topic_3297"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        }
+    ]
+}
diff --git a/data/apache_trino/apache_trino.biotools.json b/data/apache_trino/apache_trino.biotools.json
new file mode 100644
index 0000000000000..e0ff8e544a5d3
--- /dev/null
+++ b/data/apache_trino/apache_trino.biotools.json
@@ -0,0 +1,68 @@
+{
+    "additionDate": "2023-01-27T06:55:53.986823Z",
+    "biotoolsCURIE": "biotools:apache_trino",
+    "biotoolsID": "apache_trino",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "url": "https://trino.io/community.html#contributors"
+        }
+    ],
+    "description": "Trino is a distributed SQL query engine designed to query large data sets distributed over one or more heterogeneous data sources.\n\nTrino is a tool designed to efficiently query vast amounts of data using distributed queries. If you work with terabytes or petabytes of data, you are likely using tools that interact with Hadoop and HDFS. Trino was designed as an alternative to tools that query HDFS using pipelines of MapReduce jobs, such as Hive or Pig, but Trino is not limited to accessing HDFS. Trino can be and has been extended to operate over different kinds of data sources, including traditional relational databases and other data sources such as Cassandra.\n\nTrino was designed to handle data warehousing and analytics: data analysis, aggregating large amounts of data and producing reports. These workloads are often classified as Online Analytical Processing (OLAP).",
+    "documentation": [
+        {
+            "note": "Guidelines for participants with corporate interests",
+            "type": [
+                "Other"
+            ],
+            "url": "https://trino.io/guidelines-corporate.html"
+        },
+        {
+            "type": [
+                "Installation instructions"
+            ],
+            "url": "https://trino.io/docs/current/"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://github.com/trinodb/trino"
+        },
+        {
+            "type": "Software package",
+            "url": "https://trino.io/download.html"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://trino.io/",
+    "lastUpdate": "2023-02-01T13:18:44.933243Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/trinodb/trino"
+        }
+    ],
+    "name": "Apache Trino",
+    "owner": "iacs-biocomputacion",
+    "version": [
+        "Release 406 (25 Jan 2023)"
+    ]
+}
diff --git a/data/apache_zeppelin/apache_zeppelin.biotools.json b/data/apache_zeppelin/apache_zeppelin.biotools.json
new file mode 100644
index 0000000000000..da3466bbd3acb
--- /dev/null
+++ b/data/apache_zeppelin/apache_zeppelin.biotools.json
@@ -0,0 +1,61 @@
+{
+    "additionDate": "2023-01-27T13:21:31.125788Z",
+    "biotoolsCURIE": "biotools:apache_zeppelin",
+    "biotoolsID": "apache_zeppelin",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "url": "https://www.apache.org/foundation/how-it-works.html"
+        }
+    ],
+    "description": "Web-based notebook that enables data-driven,\ninteractive data analytics and collaborative documents with SQL, Scala, Python, R and more.",
+    "documentation": [
+        {
+            "type": [
+                "Installation instructions"
+            ],
+            "url": "https://zeppelin.apache.org/docs/0.10.1/"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://zeppelin.apache.org/download.html"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://zeppelin.apache.org/",
+    "lastUpdate": "2023-02-01T12:40:08.458425Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Mailing list"
+            ],
+            "url": "https://zeppelin.apache.org/community.html"
+        }
+    ],
+    "name": "Apache Zeppelin",
+    "owner": "iacs-biocomputacion",
+    "toolType": [
+        "Web application"
+    ],
+    "version": [
+        "0.10.1"
+    ]
+}
diff --git a/data/apinapdb/apinapdb.biotools.json b/data/apinapdb/apinapdb.biotools.json
new file mode 100644
index 0000000000000..f514bfe526db1
--- /dev/null
+++ b/data/apinapdb/apinapdb.biotools.json
@@ -0,0 +1,111 @@
+{
+    "additionDate": "2023-02-09T14:28:42.081221Z",
+    "biotoolsCURIE": "biotools:apinapdb",
+    "biotoolsID": "apinapdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "sh.arab@modares.ac.ir",
+            "name": "Seyed Shahriar Arab",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "yarikhosroushahia@tbzmed.ac.ir",
+            "name": "Ahmad Yari Khosroushahi",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A database of apoptosis-inducing anticancer peptides.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Peptide database search",
+                    "uri": "http://edamontology.org/operation_3646"
+                },
+                {
+                    "term": "Protein property calculation",
+                    "uri": "http://edamontology.org/operation_0250"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bioinf.modares.ac.ir/software/ApInAPDB/",
+    "language": [
+        "JavaScript",
+        "PHP"
+    ],
+    "lastUpdate": "2023-02-09T14:28:42.084029Z",
+    "license": "Other",
+    "name": "ApInAPDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41598-022-25530-6",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).ApInAPDB (Apoptosis-Inducing Anticancer Peptides Database) consists of 818 apoptosis-inducing anticancer peptides which are manually collected from research articles. The database provides scholars with peptide related information such as function, binding target and affinity, IC50 and etc. In addition, GRAVY (grand average of hydropathy), net charge at pH 7, hydrophobicity and other physicochemical properties are calculated and presented. Another category of information are structural information includes 3D modeling, secondary structure prediction and descriptors for QSAR (quantitative structure–activity relationship) modeling. In order to facilitate the browsing process, three types of user-friendly searching tools are provided: top categories browser, simple search and advanced search. Overall ApInAPDB as the first database presenting apoptosis-inducing anticancer peptides can be useful in the field of peptide design and especially cancer therapy. Researchers can freely access the database at http://bioinf.modares.ac.ir/software/ApInAPDB/.",
+                "authors": [
+                    {
+                        "name": "Arab S.S."
+                    },
+                    {
+                        "name": "Daly N.L."
+                    },
+                    {
+                        "name": "Doustmohammadi A."
+                    },
+                    {
+                        "name": "Faraji N."
+                    },
+                    {
+                        "name": "Khosroushahi A.Y."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "ApInAPDB: a database of apoptosis-inducing anticancer peptides"
+            },
+            "pmcid": "PMC9734560",
+            "pmid": "36494486"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Protein secondary structure",
+            "uri": "http://edamontology.org/topic_3542"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/apis-wings-eu/apis-wings-eu.biotools.json b/data/apis-wings-eu/apis-wings-eu.biotools.json
new file mode 100644
index 0000000000000..27b6d6bd68f05
--- /dev/null
+++ b/data/apis-wings-eu/apis-wings-eu.biotools.json
@@ -0,0 +1,58 @@
+{
+    "additionDate": "2022-12-30T06:46:43.806950Z",
+    "biotoolsCURIE": "biotools:apis-wings-eu",
+    "biotoolsID": "apis-wings-eu",
+    "description": "Collection of wing images for conservation of honey bees (Apis mellifera) biodiversity in Europe.\nWe provide 26,481 forewing images of honey bee workers. They represent 1,725 samples from 13 European countries. The shape of the wings was described using the coordinates for 19 landmarks at wing veins’ intersections. The whole dataset, including the wing images, landmark coordinates, geographic coordinates of sampling locations, and other data, is available on the Zenodo website under a Public Domain licence.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Statistical calculation",
+                    "uri": "http://edamontology.org/operation_2238"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://zenodo.org/record/7244070",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-25T08:20:22.981177Z",
+    "license": "CC0-1.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://zenodo.org/record/7244070"
+        }
+    ],
+    "name": "Apis-wings-EU",
+    "owner": "tofilski",
+    "publication": [
+        {
+            "doi": "10.5281/zenodo.7244070",
+            "version": "2"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Biological databases",
+            "uri": "http://edamontology.org/topic_3071"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/apl_voro/apl_voro.biotools.json b/data/apl_voro/apl_voro.biotools.json
new file mode 100644
index 0000000000000..8de05ada3c327
--- /dev/null
+++ b/data/apl_voro/apl_voro.biotools.json
@@ -0,0 +1,80 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-03T17:17:44.516390Z",
+    "biotoolsCURIE": "biotools:apl_voro",
+    "biotoolsID": "apl_voro",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "bjoern@cellmicrocosmos.org",
+            "name": "Falk Schreiber",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "falk.schreiber@uni-konstanz.de",
+            "name": "Bjorn Sommer",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "APL@VORO is a software designed to aid in analyzing membrane simulations made with GROMACS.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://cellmicrocosmos.org/aplvoro/documentation/"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Simulation analysis",
+                    "uri": "http://edamontology.org/operation_0244"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://aplvoro.com",
+    "lastUpdate": "2023-03-03T17:17:44.519054Z",
+    "license": "Other",
+    "name": "APL_voro",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD083",
+            "pmid": "36752505"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Lipids",
+            "uri": "http://edamontology.org/topic_0153"
+        },
+        {
+            "term": "Membrane and lipoproteins",
+            "uri": "http://edamontology.org/topic_0820"
+        }
+    ]
+}
diff --git a/data/appinetwork/appinetwork.biotools.json b/data/appinetwork/appinetwork.biotools.json
new file mode 100644
index 0000000000000..8f4eda828e882
--- /dev/null
+++ b/data/appinetwork/appinetwork.biotools.json
@@ -0,0 +1,110 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T13:20:25.399305Z",
+    "biotoolsCURIE": "biotools:appinetwork",
+    "biotoolsID": "appinetwork",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "simon.gosset1@universite-paris-saclay.fr",
+            "name": "Simon Gosset",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An R package for building and computational analysis of protein-protein interaction networks.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Protein-protein interaction analysis",
+                    "uri": "http://edamontology.org/operation_2949"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://forgemia.inra.fr/GNet/appinetwork",
+    "language": [
+        "C",
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-01-25T13:20:25.403441Z",
+    "license": "BSD-3-Clause",
+    "name": "APPINetwork",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.7717/PEERJ.14204",
+            "metadata": {
+                "abstract": "Copyright 2022 Gosset et al.Background. Protein–protein interactions (PPIs) are essential to almost every process in a cell. Analysis of PPI networks gives insights into the functional relationships among proteins and may reveal important hub proteins and sub-networks corresponding to functional modules. Several good tools have been developed for PPI network analysis but they have certain limitations. Most tools are suited for studying PPI in only a small number of model species, and do not allow second-order networks to be built, or offer relevant functions for their analysis. To overcome these limitations, we have developed APPINetwork (Analysis of Protein–protein Interaction Networks). The aim was to produce a generic and user-friendly package for building and analyzing a PPI network involving proteins of interest from any species as long they are stored in a database. Methods. APPINetwork is an open-source R package. It can be downloaded and installed on the collaborative development platform GitLab (https://forgemia.inra.fr/ GNet/appinetwork). A graphical user interface facilitates its use. Graphical windows, buttons, and scroll bars allow the user to select or enter an organism name, choose data files and network parameters or methods dedicated to network analysis. All functions are implemented in R, except for the script identifying all proteins involved in the same biological process (developed in C) and the scripts formatting the BioGRID data file and generating the IDs correspondence file (implemented in Python 3). PPI information comes from private resources or different public databases (such as IntAct, BioGRID, and iRefIndex). The package can be deployed on Linux and macOS operating systems (OS). Deployment on Windows is possible but it requires the prior installation of Rtools and Python 3. Results. APPINetwork allows the user to build a PPI network from selected public databases and add their own PPI data. In this network, the proteins have unique identifiers resulting from the standardization of the different identifiers specific to each database. In addition to the construction of the first-order network, APPINetwork offers the possibility of building a second-order network centered on the proteins of interest (proteins known for their role in the biological process studied or subunits of a complex protein) and provides the number and type of experiments that have highlighted each PPI, as well as references to articles containing experimental evidence. Conclusion. More than a tool for PPI network building, APPINetwork enables the analysis of the resultant network, by searching either for the community of proteins involved in the same biological process or for the assembly intermediates of a protein complex. Results of these analyses are provided in easily exportable files. Examples files and a user manual describing each step of the process come with the package.",
+                "authors": [
+                    {
+                        "name": "Gallopin M."
+                    },
+                    {
+                        "name": "Glatigny A."
+                    },
+                    {
+                        "name": "Gosset S."
+                    },
+                    {
+                        "name": "Mucchielli-Giorgi M.-H."
+                    },
+                    {
+                        "name": "Sale M."
+                    },
+                    {
+                        "name": "Yi Z."
+                    }
+                ],
+                "date": "2022-11-04T00:00:00Z",
+                "journal": "PeerJ",
+                "title": "APPINetwork: an R package for building and computational analysis of protein–protein interaction networks"
+            },
+            "pmcid": "PMC9639416",
+            "pmid": "36353604"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Protein interaction experiment",
+            "uri": "http://edamontology.org/topic_3957"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/appris/appris.biotools.json b/data/appris/appris.biotools.json
index 24698074b35af..3a6b0cd9dd5b2 100644
--- a/data/appris/appris.biotools.json
+++ b/data/appris/appris.biotools.json
@@ -73,14 +73,14 @@
             "type": [
                 "General"
             ],
-            "url": "http://appris-tools.org/#/help/intro"
+            "url": "https://appris.bioinfo.cnio.es/#/help/intro"
         }
     ],
     "download": [
         {
             "note": "The annotations of the following species are available.",
             "type": "Downloads page",
-            "url": "http://appris-tools.org/#/downloads"
+            "url": "https://appris.bioinfo.cnio.es/#/downloads"
         }
     ],
     "editPermission": {
@@ -109,15 +109,15 @@
             ]
         }
     ],
-    "homepage": "http://appris-tools.org",
-    "lastUpdate": "2022-04-20T12:02:56.999141Z",
+    "homepage": "https://appris.bioinfo.cnio.es",
+    "lastUpdate": "2023-02-01T16:31:41.772063Z",
     "link": [
         {
             "note": "Access annotations for the species annotated in the database via gene name or Ensembl id.",
             "type": [
                 "Other"
             ],
-            "url": "http://appris-tools.org/#/downloads"
+            "url": "https://appris.bioinfo.cnio.es/#/downloads"
         },
         {
             "note": "Annotate genes and transcripts automatically and access queries through RESTful web services.",
@@ -131,7 +131,7 @@
             "type": [
                 "Other"
             ],
-            "url": "http://appris-tools.org/#/server"
+            "url": "https://appris.bioinfo.cnio.es/#/server"
         },
         {
             "type": [
@@ -148,6 +148,42 @@
     ],
     "owner": "tdido",
     "publication": [
+        {
+            "doi": "10.1093/nar/gkab1058",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.APPRIS (https://appris.bioinfo.cnio.es) is a well-established database housing annotations for protein isoforms for a range of species. APPRIS selects principal isoforms based on protein structure and function features and on cross-species conservation. Most coding genes produce a single main protein isoform and the principal isoforms chosen by the APPRIS database best represent this main cellular isoform. Human genetic data, experimental protein evidence and the distribution of clinical variants all support the relevance of APPRIS principal isoforms. APPRIS annotations and principal isoforms have now been expanded to 10 model organisms. In this paper we highlight the most recent updates to the database. APPRIS annotations have been generated for two new species, cow and chicken, the protein structural information has been augmented with reliable models from the EMBL-EBI AlphaFold database, and we have substantially expanded the confirmatory proteomics evidence available for the human genome. The most significant change in APPRIS has been the implementation of TRIFID functional isoform scores. TRIFID functional scores are assigned to all splice isoforms, and APPRIS uses the TRIFID functional scores and proteomics evidence to determine principal isoforms when core methods cannot.",
+                "authors": [
+                    {
+                        "name": "Cerdan-Velez D."
+                    },
+                    {
+                        "name": "Di Domenico T."
+                    },
+                    {
+                        "name": "Pozo F."
+                    },
+                    {
+                        "name": "Rodriguez J.M."
+                    },
+                    {
+                        "name": "Tress M.L."
+                    },
+                    {
+                        "name": "Vazquez J."
+                    }
+                ],
+                "citationCount": 6,
+                "date": "2022-01-07T00:00:00Z",
+                "journal": "Nucleic Acids Research",
+                "title": "APPRIS: Selecting functionally important isoforms"
+            },
+            "note": "Rodriguez JM, Pozo F, Cerdán-Vélez D, Di Domenico T, Vázquez J, Tress ML. APPRIS: selecting functionally important isoforms. Nucleic Acids Res. 2022;50(D1):D54-D59.",
+            "pmcid": "PMC8728124",
+            "pmid": "34755885",
+            "type": [
+                "Primary"
+            ]
+        },
         {
             "doi": "10.1093/nar/gks1058",
             "metadata": {
@@ -178,7 +214,7 @@
                         "name": "Wesselink J.J."
                     }
                 ],
-                "citationCount": 116,
+                "citationCount": 128,
                 "date": "2013-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "APPRIS: Annotation of principal and alternative splice isoforms"
@@ -207,7 +243,7 @@
                         "name": "Valencia A."
                     }
                 ],
-                "citationCount": 15,
+                "citationCount": 16,
                 "date": "2015-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "APPRIS WebServer and WebServices"
@@ -242,7 +278,7 @@
                         "name": "Vazquez J."
                     }
                 ],
-                "citationCount": 52,
+                "citationCount": 69,
                 "date": "2018-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "APPRIS 2017: Principal isoforms for multiple gene sets"
@@ -284,6 +320,6 @@
     ],
     "validated": 1,
     "version": [
-        "202011_v37"
+        "2022_07.v47"
     ]
 }
diff --git a/data/aptamat/aptamat.biotools.json b/data/aptamat/aptamat.biotools.json
new file mode 100644
index 0000000000000..b3ed005cc7be6
--- /dev/null
+++ b/data/aptamat/aptamat.biotools.json
@@ -0,0 +1,116 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T13:42:25.343432Z",
+    "biotoolsCURIE": "biotools:aptamat",
+    "biotoolsID": "aptamat",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "irene.maffucci@utc.fr",
+            "name": "Irene Maffucci",
+            "orcidid": "https://orcid.org/0000-0002-4524-1137",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "miraine.davila-felipe@utc.fr",
+            "name": "Miraine Dávila Felipe",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "AptaMat is a simple script which aims to measure differences between DNA or RNA secondary structures. The method is based on the comparison of the matrices representing the two secondary structures to analyze, assimilable to dotplots.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Nucleic acid structure comparison",
+                    "uri": "http://edamontology.org/operation_2518"
+                },
+                {
+                    "term": "Protein secondary structure comparison",
+                    "uri": "http://edamontology.org/operation_2488"
+                },
+                {
+                    "term": "RNA inverse folding",
+                    "uri": "http://edamontology.org/operation_0483"
+                },
+                {
+                    "term": "RNA secondary structure alignment",
+                    "uri": "http://edamontology.org/operation_0502"
+                },
+                {
+                    "term": "RNA secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0278"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/GEC-git/AptaMat.git",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T13:42:25.346038Z",
+    "license": "MIT",
+    "name": "AptaMat",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC752",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Comparing single-stranded nucleic acids (ssNAs) secondary structures is fundamental when investigating their function and evolution and predicting the effect of mutations on their structures. Many comparison metrics exist, although they are either too elaborate or not sensitive enough to distinguish close ssNAs structures. RESULTS: In this context, we developed AptaMat, a simple and sensitive algorithm for ssNAs secondary structures comparison based on matrices representing the ssNAs secondary structures and a metric built upon the Manhattan distance in the plane. We applied AptaMat to several examples and compared the results to those obtained by the most frequently used metrics, namely the Hamming distance and the RNAdistance, and by a recently developed image-based approach. We showed that AptaMat is able to discriminate between similar sequences, outperforming all the other here considered metrics. In addition, we showed that AptaMat was able to correctly classify 14 RFAM families within a clustering procedure. AVAILABILITY AND IMPLEMENTATION: The python code for AptaMat is available at https://github.com/GEC-git/AptaMat.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Avalle B."
+                    },
+                    {
+                        "name": "Binet T."
+                    },
+                    {
+                        "name": "Davila Felipe M."
+                    },
+                    {
+                        "name": "Maffucci I."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "AptaMat: a matrix-based algorithm to compare single-stranded oligonucleotides secondary structures"
+            },
+            "pmcid": "PMC9805580",
+            "pmid": "36440922"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Nucleic acid structure analysis",
+            "uri": "http://edamontology.org/topic_0097"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        }
+    ]
+}
diff --git a/data/artiax/artiax.biotools.json b/data/artiax/artiax.biotools.json
new file mode 100644
index 0000000000000..be68584b7404e
--- /dev/null
+++ b/data/artiax/artiax.biotools.json
@@ -0,0 +1,104 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-28T19:31:01.614534Z",
+    "biotoolsCURIE": "biotools:artiax",
+    "biotoolsID": "artiax",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "achilleas.frangakis@biophysik.org",
+            "name": "Achilleas S. Frangakis",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Serena M. Arghittu"
+        },
+        {
+            "name": "Utz H. Ermel",
+            "orcidid": "http://orcid.org/0000-0003-4685-037X"
+        }
+    ],
+    "description": "An Electron Tomography Toolbox for the Interactive Handling of Sub-Tomograms in UCSF ChimeraX.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/FrangakisLab/ArtiaX",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-28T19:31:01.617101Z",
+    "license": "GPL-3.0",
+    "name": "ArtiaX",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/pro.4472",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.Cryo-electron tomography analysis involves the selection of macromolecular complexes to be used for subsequent sub-tomogram averaging and structure determination. Here, we describe a plugin developed for UCSF ChimeraX that allows for the display, selection, and editing of particles within tomograms. Positions and orientations of selected particles can be manually set, modified and inspected in real time, both on screen and in virtual reality, and exported to various file formats. The plugin allows for the parallel visualization of particles stored in several meta data lists, in the context of any three-dimensional image that can be opened with UCSF ChimeraX. The particles are rendered in user-defined colors or using colormaps, such that individual classes or groups of particles, cross-correlation coefficients, or other types of information can be highlighted to the user. The implemented functions are fast, reliable, and intuitive, exploring the broad range of features in UCSF ChimeraX. They allow for a fluent human–machine interaction, which enables an effective understanding of the sub-tomogram processing pipeline, even for non-specialist users.",
+                "authors": [
+                    {
+                        "name": "Arghittu S.M."
+                    },
+                    {
+                        "name": "Ermel U.H."
+                    },
+                    {
+                        "name": "Frangakis A.S."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Protein Science",
+                "title": "ArtiaX: An electron tomography toolbox for the interactive handling of sub-tomograms in UCSF ChimeraX"
+            },
+            "pmcid": "PMC9667824",
+            "pmid": "36251681"
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "chimerax",
+            "type": "includedIn"
+        }
+    ],
+    "toolType": [
+        "Plug-in"
+    ],
+    "topic": [
+        {
+            "term": "Electron microscopy",
+            "uri": "http://edamontology.org/topic_0611"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Tomography",
+            "uri": "http://edamontology.org/topic_3452"
+        }
+    ]
+}
diff --git a/data/as-cmc/as-cmc.biotools.json b/data/as-cmc/as-cmc.biotools.json
new file mode 100644
index 0000000000000..a84d6ff2ab64f
--- /dev/null
+++ b/data/as-cmc/as-cmc.biotools.json
@@ -0,0 +1,99 @@
+{
+    "additionDate": "2023-02-11T07:19:29.175176Z",
+    "biotoolsCURIE": "biotools:as-cmc",
+    "biotoolsID": "as-cmc",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "yejun@catholic.ac.kr",
+            "name": "Yeun-Jun Chung",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A pan-cancer database of alternative splicing for molecular classification of cancer.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Alternative splicing prediction",
+                    "uri": "http://edamontology.org/operation_0264"
+                },
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.pmrc.re.kr/ASCMC/",
+    "lastUpdate": "2023-02-11T07:19:29.178496Z",
+    "license": "Not licensed",
+    "name": "AS-CMC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41598-022-25584-6",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Alternative splicing (AS) is a post-transcriptional regulation that leads to the complexity of the transcriptome. Despite the growing importance of AS in cancer research, the role of AS has not been systematically studied, especially in understanding cancer molecular classification. Herein, we analyzed the molecular subtype-specific regulation of AS using The Cancer Genome Atlas data and constructed a web-based database, named Alternative Splicing for Cancer Molecular Classification (AS-CMC). Our system harbors three analysis modules for exploring subtype-specific AS events, evaluating their phenotype association, and performing pan-cancer comparison. The number of subtype-specific AS events was found to be diverse across cancer types, and some differentially regulated AS events were recurrently found in multiple cancer types. We analyzed a subtype-specific AS in exon 11 of mitogen-activated protein kinase kinase 7 (MAP3K7) as an example of a pan-cancer AS biomarker. This AS marker showed significant association with the survival of patients with stomach adenocarcinoma. Our analysis revealed AS as an important determinant for cancer molecular classification. AS-CMC is the first web-based resource that provides a comprehensive tool to explore the biological implications of AS events, facilitating the discovery of novel AS biomarkers.",
+                "authors": [
+                    {
+                        "name": "Chung Y.-J."
+                    },
+                    {
+                        "name": "Lee J.-O."
+                    },
+                    {
+                        "name": "Lee M."
+                    },
+                    {
+                        "name": "Park J."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "AS-CMC: a pan-cancer database of alternative splicing for molecular classification of cancer"
+            },
+            "pmcid": "PMC9726986",
+            "pmid": "36473963"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        }
+    ]
+}
diff --git a/data/asc_g4/asc_g4.biotools.json b/data/asc_g4/asc_g4.biotools.json
new file mode 100644
index 0000000000000..d89e2c3159a57
--- /dev/null
+++ b/data/asc_g4/asc_g4.biotools.json
@@ -0,0 +1,88 @@
+{
+    "additionDate": "2023-03-09T07:50:18.999851Z",
+    "biotoolsCURIE": "biotools:asc_g4",
+    "biotoolsID": "asc_g4",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "liliane.mouawad@curie.fr",
+            "name": "Liliane Mouawad",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A website to calculate advanced structural characteristics of G-quadruplexes.ssr (X3DNA.ORG)",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Protein geometry calculation",
+                    "uri": "http://edamontology.org/operation_0249"
+                },
+                {
+                    "term": "Protein structure assignment",
+                    "uri": "http://edamontology.org/operation_0320"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://tiny.cc/ASC-G4",
+    "lastUpdate": "2023-03-09T07:50:19.004619Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "http://g4.x3dna.org"
+        }
+    ],
+    "name": "ASC-G4",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAD060",
+            "pmid": "36794725"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        },
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "X-ray diffraction",
+            "uri": "http://edamontology.org/topic_2828"
+        }
+    ]
+}
diff --git a/data/ascancer_atlas/ascancer_atlas.biotools.json b/data/ascancer_atlas/ascancer_atlas.biotools.json
new file mode 100644
index 0000000000000..b19a2c4326355
--- /dev/null
+++ b/data/ascancer_atlas/ascancer_atlas.biotools.json
@@ -0,0 +1,136 @@
+{
+    "additionDate": "2023-01-25T13:47:12.706159Z",
+    "biotoolsCURIE": "biotools:ascancer_atlas",
+    "biotoolsID": "ascancer_atlas",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "baoym@big.ac.cn",
+            "name": "Yiming Bao",
+            "orcidid": "https://orcid.org/0000-0002-9922-9723",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "lirj@big.ac.cn",
+            "name": "Zhaoqi Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "liuzq@big.ac.cn",
+            "name": "Rujiao Li",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A comprehensive knowledgebase of alternative splicing in human cancers.",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://ngdc.cncb.ac.cn/ascancer/download"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Alternative splicing prediction",
+                    "uri": "http://edamontology.org/operation_0264"
+                },
+                {
+                    "term": "Exonic splicing enhancer prediction",
+                    "uri": "http://edamontology.org/operation_0446"
+                },
+                {
+                    "term": "Splice site prediction",
+                    "uri": "http://edamontology.org/operation_0433"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ngdc.cncb.ac.cn/ascancer",
+    "lastUpdate": "2023-01-25T13:47:12.708582Z",
+    "license": "CC-BY-NC-3.0",
+    "name": "ASCancer Atlas",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC955",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Alternative splicing (AS) is a fundamental process that governs almost all aspects of cellular functions, and dysregulation in this process has been implicated in tumor initiation, progression and treatment resistance. With accumulating studies of carcinogenic mis-splicing in cancers, there is an urgent demand to integrate cancer-associated splicing changes to better understand their internal cross-talks and functional consequences from a global view. However, a resource of key functional AS events in human cancers is still lacking. To fill the gap, we developed ASCancer Atlas (https://ngdc.cncb.ac.cn/ascancer), a comprehensive knowledgebase of aberrant splicing in human cancers. Compared to extant databases, ASCancer Atlas features a high-confidence collection of 2006 cancer-associated splicing events experimentally proved to promote tumorigenesis, a systematic splicing regulatory network, and a suit of multi-scale online analysis tools. For each event, we manually curated the functional axis including upstream splicing regulators, splicing event annotations, downstream oncogenic effects, and possible therapeutic strategies. ASCancer Atlas also houses about 2 million computationally putative splicing events. Additionally, a user-friendly web interface was built to enable users to easily browse, search, visualize, analyze, and download all splicing events. Overall, ASCancer Atlas provides a unique resource to study the functional roles of splicing dysregulation in human cancers.",
+                "authors": [
+                    {
+                        "name": "Bao Y."
+                    },
+                    {
+                        "name": "Gong Z."
+                    },
+                    {
+                        "name": "Huang Y."
+                    },
+                    {
+                        "name": "Li R."
+                    },
+                    {
+                        "name": "Liu Z."
+                    },
+                    {
+                        "name": "Wang G."
+                    },
+                    {
+                        "name": "Wu S."
+                    },
+                    {
+                        "name": "Xing P."
+                    },
+                    {
+                        "name": "Zhang M."
+                    },
+                    {
+                        "name": "Zhao W."
+                    },
+                    {
+                        "name": "Zheng X."
+                    },
+                    {
+                        "name": "Zong W."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "ASCancer Atlas: a comprehensive knowledgebase of alternative splicing in human cancers"
+            },
+            "pmcid": "PMC9825479",
+            "pmid": "36318242"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/ascept/ascept.biotools.json b/data/ascept/ascept.biotools.json
new file mode 100644
index 0000000000000..365c0596b5cfe
--- /dev/null
+++ b/data/ascept/ascept.biotools.json
@@ -0,0 +1,91 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T13:54:24.587002Z",
+    "biotoolsCURIE": "biotools:ascept",
+    "biotoolsID": "ascept",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "kimberly.glass@channing.harvard.edu",
+            "name": "Kimberly Glass",
+            "orcidid": "https://orcid.org/0000-0003-4394-5779",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Automated Selection of Changepoints using Empirical P-values and Trimming (ASCEPT), to select an optimal set of changepoints in mobile health data",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/matthewquinn1/changepointSelect",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-25T13:54:24.589589Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/matthewquinn1/changepointSelect.<h4>Results</h4"
+        }
+    ],
+    "name": "ASCEPT",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/JAMIAOPEN/OOAC090",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press on behalf of the American Medical Informatics Association.Objectives: One challenge that arises when analyzing mobile health (mHealth) data is that updates to the proprietary algorithms that process these data can change apparent patterns. Since the timings of these updates are not publicized, an analytic approach is necessary to determine whether changes in mHealth data are due to lifestyle behaviors or algorithmic updates. Existing methods for identifying changepoints do not consider multiple types of changepoints, may require prespecifying the number of changepoints, and often involve nonintuitive parameters. We propose a novel approach, Automated Selection of Changepoints using Empirical P-values and Trimming (ASCEPT), to select an optimal set of changepoints in mHealth data. Materials and Methods: ASCEPT involves 2 stages: (1) identification of a statistically significant set of changepoints from sequential iterations of a changepoint detection algorithm; and (2) trimming changepoints within linear and seasonal trends. ASCEPT is available at https://github.com/matthewquinn1/changepointSelect. Results: We demonstrate ASCEPT's utility using real-world mHealth data collected through the Precision VISSTA study. We also demonstrate that ASCEPT outperforms a comparable method, circular binary segmentation, and illustrate the impact when adjusting for changepoints in downstream analysis. Discussion: ASCEPT offers a practical approach for identifying changepoints in mHealth data that result from algorithmic updates. ASCEPT's only required parameters are a significance level and goodness-of-fit threshold, offering a more intuitive option compared to other approaches. Conclusion: ASCEPT provides an intuitive and useful way to identify which changepoints in mHealth data are likely the result of updates to the underlying algorithms that process the data.",
+                "authors": [
+                    {
+                        "name": "Chung A."
+                    },
+                    {
+                        "name": "Glass K."
+                    },
+                    {
+                        "name": "Quinn M."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "JAMIA Open",
+                "title": "Automated selection of changepoints using empirical P-values and trimming"
+            },
+            "pmcid": "PMC9617685",
+            "pmid": "36325307"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Bioengineering",
+            "uri": "http://edamontology.org/topic_3398"
+        },
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        }
+    ]
+}
diff --git a/data/ase-net/ase-net.biotools.json b/data/ase-net/ase-net.biotools.json
new file mode 100644
index 0000000000000..13d7b90b87e81
--- /dev/null
+++ b/data/ase-net/ase-net.biotools.json
@@ -0,0 +1,74 @@
+{
+    "additionDate": "2023-01-25T13:58:33.377715Z",
+    "biotoolsCURIE": "biotools:ase-net",
+    "biotoolsID": "ase-net",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Tao Lei"
+        }
+    ],
+    "description": "Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/SUST-reynole/ASE-Net",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T13:58:33.381264Z",
+    "license": "Not licensed",
+    "name": "ASE-Net",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1109/TMI.2022.3225687",
+            "metadata": {
+                "abstract": "AuthorPopular semi-supervised medical image segmentation networks often suffer from error supervision from unlabeled data since they usually use consistency learning under different data perturbations to regularize model training. These networks ignore the relationship between labeled and unlabeled data, and only compute single pixel-level consistency leading to uncertain prediction results. Besides, these networks often require a large number of parameters since their backbone networks are designed depending on supervised image segmentation tasks. Moreover, these networks often face a high over-fitting risk since a small number of training samples are popular for semi-supervised image segmentation. To address the above problems, in this paper, we propose a novel adversarial self-ensembling network using dynamic convolution (ASE-Net) for semi-supervised medical image segmentation. First, we use an adversarial consistency training strategy (ACTS) that employs two discriminators based on consistency learning to obtain prior relationships between labeled and unlabeled data. The ACTS can simultaneously compute pixel-level and image-level consistency of unlabeled data under different data perturbations to improve the prediction quality of labels. Second, we design a dynamic convolution-based bidirectional attention component (DyBAC) that can be embedded in any segmentation network, aiming at adaptively adjusting the weights of ASE-Net based on the structural information of input samples. This component effectively improves the feature representation ability of ASE-Net and reduces the overfitting risk of the network. The proposed ASE-Net has been extensively tested on three publicly available datasets, and experiments indicate that ASE-Net is superior to state-of-the-art networks, and reduces computational costs and memory overhead. The code is available at: https://github.com/SUST-reynole/ASE-Net.",
+                "authors": [
+                    {
+                        "name": "Du X."
+                    },
+                    {
+                        "name": "Lei T."
+                    },
+                    {
+                        "name": "Nandi A.K."
+                    },
+                    {
+                        "name": "Wan Y."
+                    },
+                    {
+                        "name": "Wang X."
+                    },
+                    {
+                        "name": "Zhang D."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "IEEE Transactions on Medical Imaging",
+                "title": "Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network"
+            },
+            "pmid": "36449588"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        }
+    ]
+}
diff --git a/data/asgard/asgard.biotools.json b/data/asgard/asgard.biotools.json
new file mode 100644
index 0000000000000..42c6812851f37
--- /dev/null
+++ b/data/asgard/asgard.biotools.json
@@ -0,0 +1,115 @@
+{
+    "additionDate": "2023-03-09T07:56:34.198040Z",
+    "biotoolsCURIE": "biotools:asgard",
+    "biotoolsID": "asgard",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "lgarmire@med.umich.edu",
+            "name": "Lana X. Garmire",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Using scRNA-seq data, Asgard repurposes drugs and predicts personalized drug combinations to address the cellular heterogeneity of patients.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/lanagarmire/ASGARD",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-09T07:56:34.202997Z",
+    "license": "Other",
+    "name": "ASGARD",
+    "operatingSystem": [
+        "Linux",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41467-023-36637-3",
+            "metadata": {
+                "abstract": "Single-cell RNA sequencing technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD) that defines a drug score to recommend drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. ASGARD shows significantly better average accuracy on single-drug therapy compared to two bulk-cell-based drug repurposing methods. We also demonstrated that it performs considerably better than other cell cluster-level predicting methods. In addition, we validate ASGARD using the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. We find that many top-ranked drugs are either approved by the Food and Drug Administration or in clinical trials treating corresponding diseases. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD.",
+                "authors": [
+                    {
+                        "name": "Du Y."
+                    },
+                    {
+                        "name": "Garmire D."
+                    },
+                    {
+                        "name": "Garmire L.X."
+                    },
+                    {
+                        "name": "He B."
+                    },
+                    {
+                        "name": "Huang Q."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Liang H."
+                    },
+                    {
+                        "name": "Sun D."
+                    },
+                    {
+                        "name": "Xiao Y."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs"
+            },
+            "pmcid": "PMC9945835",
+            "pmid": "36813801"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Drug development",
+            "uri": "http://edamontology.org/topic_3373"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/associationsubgraphs/associationsubgraphs.biotools.json b/data/associationsubgraphs/associationsubgraphs.biotools.json
new file mode 100644
index 0000000000000..f6877f9f19748
--- /dev/null
+++ b/data/associationsubgraphs/associationsubgraphs.biotools.json
@@ -0,0 +1,159 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-11T07:48:23.766272Z",
+    "biotoolsCURIE": "biotools:associationsubgraphs",
+    "biotoolsID": "associationsubgraphs",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yaomin.xu@vumc.org",
+            "name": "Yaomin Xu",
+            "orcidid": "https://orcid.org/0000-0002-3752-4006",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs.",
+    "documentation": [
+        {
+            "type": [
+                "Training material"
+            ],
+            "url": "https://prod.tbilab.org/associationsubgraphs-example/"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Network visualisation",
+                    "uri": "http://edamontology.org/operation_3925"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://prod.tbilab.org/associationsubgraphs_info/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-11T07:48:23.768942Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/tbilab/associationsubgraphs"
+        }
+    ],
+    "name": "associationSubgraphs",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC780",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Making sense of networked multivariate association patterns is vitally important to many areas of high-dimensional analysis. Unfortunately, as the data-space dimensions grow, the number of association pairs increases in O(n2); this means that traditional visualizations such as heatmaps quickly become too complicated to parse effectively. RESULTS: Here, we present associationSubgraphs: a new interactive visualization method to quickly and intuitively explore high-dimensional association datasets using network percolation and clustering. The goal is to provide an efficient investigation of association subgraphs, each containing a subset of variables with stronger and more frequent associations among themselves than the remaining variables outside the subset, by showing the entire clustering dynamics and providing subgraphs under all possible cutoff values at once. Particularly, we apply associationSubgraphs to a phenome-wide multimorbidity association matrix generated from an electronic health record and provide an online, interactive demonstration for exploring multimorbidity subgraphs. AVAILABILITY AND IMPLEMENTATION: An R package implementing both the algorithm and visualization components of associationSubgraphs is available at https://github.com/tbilab/associationsubgraphs. Online documentation is available at https://prod.tbilab.org/associationsubgraphs_info/. A demo using a multimorbidity association matrix is available at https://prod.tbilab.org/associationsubgraphs-example/.",
+                "authors": [
+                    {
+                        "name": "Balko J.M."
+                    },
+                    {
+                        "name": "Bejan C.A."
+                    },
+                    {
+                        "name": "Bick A."
+                    },
+                    {
+                        "name": "Cox N.J."
+                    },
+                    {
+                        "name": "Edwards T.L."
+                    },
+                    {
+                        "name": "Hsi R.S."
+                    },
+                    {
+                        "name": "Johnson D.B."
+                    },
+                    {
+                        "name": "Phillips E.J."
+                    },
+                    {
+                        "name": "Pulley J.M."
+                    },
+                    {
+                        "name": "Roden D.M."
+                    },
+                    {
+                        "name": "Ruderfer D.M."
+                    },
+                    {
+                        "name": "Savona M.R."
+                    },
+                    {
+                        "name": "Shirey-Rice J.K."
+                    },
+                    {
+                        "name": "Strayer N."
+                    },
+                    {
+                        "name": "Velez Edwards D.R."
+                    },
+                    {
+                        "name": "Vessels T."
+                    },
+                    {
+                        "name": "Wells Q.S."
+                    },
+                    {
+                        "name": "Xu Y."
+                    },
+                    {
+                        "name": "Yao L."
+                    },
+                    {
+                        "name": "Zhang S."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs"
+            },
+            "pmcid": "PMC9825768",
+            "pmid": "36472455"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Phenomics",
+            "uri": "http://edamontology.org/topic_3298"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/aster/aster.biotools.json b/data/aster/aster.biotools.json
new file mode 100644
index 0000000000000..e644d6d47e8f0
--- /dev/null
+++ b/data/aster/aster.biotools.json
@@ -0,0 +1,105 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-11T07:52:50.516893Z",
+    "biotoolsCURIE": "biotools:aster",
+    "biotoolsID": "aster",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chenshengquan@nankai.edu.cn",
+            "name": "Shengquan Chen",
+            "orcidid": "https://orcid.org/0000-0002-3503-9306",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "ruijiang@tsinghua.edu.cn",
+            "name": "Rui Jiang",
+            "orcidid": "https://orcid.org/0000-0002-7533-3753",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A package for accurately estimating the number of cell types in single-cell chromatin accessibility data.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://aster.readthedocs.io/"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/biox-nku/aster",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-11T07:52:50.519585Z",
+    "license": "MIT",
+    "name": "ASTER",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC842",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: Recent innovations in single-cell chromatin accessibility sequencing (scCAS) have revolutionized the characterization of epigenomic heterogeneity. Estimation of the number of cell types is a crucial step for downstream analyses and biological implications. However, efforts to perform estimation specifically for scCAS data are limited. Here, we propose ASTER, an ensemble learning-based tool for accurately estimating the number of cell types in scCAS data. ASTER outperformed baseline methods in systematic evaluation on 27 datasets of various protocols, sizes, numbers of cell types, degrees of cell-type imbalance, cell states and qualities, providing valuable guidance for scCAS data analysis. AVAILABILITY AND IMPLEMENTATION: ASTER along with detailed documentation is freely accessible at https://aster.readthedocs.io/ under the MIT License. It can be seamlessly integrated into existing scCAS analysis workflows. The source code is available at https://github.com/biox-nku/aster. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Chen S."
+                    },
+                    {
+                        "name": "Jiang R."
+                    },
+                    {
+                        "name": "Long W."
+                    },
+                    {
+                        "name": "Wang R."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "ASTER: accurately estimating the number of cell types in single-cell chromatin accessibility data"
+            },
+            "pmcid": "PMC9825259",
+            "pmid": "36573319"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Chromosome conformation capture",
+            "uri": "http://edamontology.org/topic_3940"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/asteroid/asteroid.biotools.json b/data/asteroid/asteroid.biotools.json
new file mode 100644
index 0000000000000..5208174c5eb48
--- /dev/null
+++ b/data/asteroid/asteroid.biotools.json
@@ -0,0 +1,93 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-28T19:23:11.248289Z",
+    "biotoolsCURIE": "biotools:asteroid",
+    "biotoolsID": "asteroid",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "benomorel@gmail.com",
+            "name": "Benoit Morel",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alexandros Stamatakis"
+        },
+        {
+            "name": "Tom A Williams"
+        }
+    ],
+    "description": "A new minimum balanced evolution supertree algorithm robust to missing data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phylogenetic reconstruction",
+                    "uri": "http://edamontology.org/operation_3478"
+                },
+                {
+                    "term": "Phylogenetic tree reconciliation",
+                    "uri": "http://edamontology.org/operation_3947"
+                },
+                {
+                    "term": "Sorting",
+                    "uri": "http://edamontology.org/operation_3802"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/BenoitMorel/Asteroid",
+    "language": [
+        "C++"
+    ],
+    "lastUpdate": "2023-02-28T19:23:11.250824Z",
+    "license": "AGPL-3.0",
+    "name": "Asteroid",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac832",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Missing data and incomplete lineage sorting (ILS) are two major obstacles to accurate species tree inference. Gene tree summary methods such as ASTRAL and ASTRID have been developed to account for ILS. However, they can be severely affected by high levels of missing data. RESULTS: We present Asteroid, a novel algorithm that infers an unrooted species tree from a set of unrooted gene trees. We show on both empirical and simulated datasets that Asteroid is substantially more accurate than ASTRAL and ASTRID for very high proportions (>80%) of missing data. Asteroid is several orders of magnitude faster than ASTRAL for datasets that contain thousands of genes. It offers advanced features such as parallelization, support value computation and support for multi-copy and multifurcating gene trees. AVAILABILITY AND IMPLEMENTATION: Asteroid is freely available at https://github.com/BenoitMorel/Asteroid. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Morel B."
+                    },
+                    {
+                        "name": "Stamatakis A."
+                    },
+                    {
+                        "name": "Williams T.A."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing data"
+            },
+            "pmcid": "PMC9838317",
+            "pmid": "36576010"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Phylogenomics",
+            "uri": "http://edamontology.org/topic_0194"
+        }
+    ]
+}
diff --git a/data/atgo/atgo.biotools.json b/data/atgo/atgo.biotools.json
new file mode 100644
index 0000000000000..cb99aae0d8f76
--- /dev/null
+++ b/data/atgo/atgo.biotools.json
@@ -0,0 +1,117 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-28T19:17:24.628319Z",
+    "biotoolsCURIE": "biotools:atgo",
+    "biotoolsID": "atgo",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Dong-Jun Yu"
+        },
+        {
+            "name": "Yi-Heng Zhu"
+        },
+        {
+            "name": "Chengxin Zhang",
+            "orcidid": "http://orcid.org/0000-0001-7290-1324"
+        },
+        {
+            "name": "Yang Zhang",
+            "orcidid": "http://orcid.org/0000-0002-2739-1916"
+        }
+    ],
+    "description": "Integrating Self-Attention Transformer with Triplet Neural Networks for Protein Gene Ontology Prediction.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Protein feature detection",
+                    "uri": "http://edamontology.org/operation_3092"
+                },
+                {
+                    "term": "Protein function prediction",
+                    "uri": "http://edamontology.org/operation_1777"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://zhanggroup.org/ATGO/",
+    "lastUpdate": "2023-02-28T19:17:24.630863Z",
+    "name": "ATGO",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/journal.pcbi.1010793",
+            "metadata": {
+                "abstract": "© 2022 Zhu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Accurate identification of protein function is critical to elucidate life mechanisms and design new drugs. We proposed a novel deep-learning method, ATGO, to predict Gene Ontology (GO) attributes of proteins through a triplet neural-network architecture embedded with pre-trained language models from protein sequences. The method was systematically tested on 1068 non-redundant benchmarking proteins and 3328 targets from the third Critical Assessment of Protein Function Annotation (CAFA) challenge. Experimental results showed that ATGO achieved a significant increase of the GO prediction accuracy compared to the state-of-the-art approaches in all aspects of molecular function, biological process, and cellular component. Detailed data analyses showed that the major advantage of ATGO lies in the utilization of pre-trained transformer language models which can extract discriminative functional pattern from the feature embeddings. Meanwhile, the proposed triplet network helps enhance the association of functional similarity with feature similarity in the sequence embedding space. In addition, it was found that the combination of the network scores with the complementary homology-based inferences could further improve the accuracy of the predicted models. These results demonstrated a new avenue for high-accuracy deep-learning function prediction that is applicable to large-scale protein function annotations from sequence alone.",
+                "authors": [
+                    {
+                        "name": "Yu D.-J."
+                    },
+                    {
+                        "name": "Zhang C."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zhu Y.-H."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction"
+            },
+            "pmcid": "PMC9822105",
+            "pmid": "36548439"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Function analysis",
+            "uri": "http://edamontology.org/topic_1775"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/atlas-ohdsi/atlas-ohdsi.biotools.json b/data/atlas-ohdsi/atlas-ohdsi.biotools.json
new file mode 100644
index 0000000000000..8c6297f817714
--- /dev/null
+++ b/data/atlas-ohdsi/atlas-ohdsi.biotools.json
@@ -0,0 +1,54 @@
+{
+    "additionDate": "2023-01-31T08:27:05.543545Z",
+    "biotoolsCURIE": "biotools:atlas-ohdsi",
+    "biotoolsID": "atlas-ohdsi",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "Observational Health Data Sciences and Informatics",
+            "url": "https://www.ohdsi.org/"
+        }
+    ],
+    "description": "ATLAS is a free, publicly available, web-based tool developed by the OHDSI community that facilitates the design and execution of analyses on standardized, patient-level, observational data in the CDM format.",
+    "documentation": [
+        {
+            "type": [
+                "Installation instructions"
+            ],
+            "url": "https://github.com/OHDSI/Atlas/wiki/Atlas-Setup-Guide"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://atlas-demo.ohdsi.org/"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.ohdsi.org/software-tools/",
+    "lastUpdate": "2023-02-01T13:22:56.573585Z",
+    "link": [
+        {
+            "type": [
+                "Software catalogue"
+            ],
+            "url": "https://ohdsi.github.io/TheBookOfOhdsi/OhdsiAnalyticsTools.html#atlas"
+        }
+    ],
+    "name": "Atlas-Ohdsi",
+    "owner": "iacs-biocomputacion"
+}
diff --git a/data/atlasgrabber/atlasgrabber.biotools.json b/data/atlasgrabber/atlasgrabber.biotools.json
new file mode 100644
index 0000000000000..0e2933f0259e9
--- /dev/null
+++ b/data/atlasgrabber/atlasgrabber.biotools.json
@@ -0,0 +1,100 @@
+{
+    "additionDate": "2023-02-11T07:59:16.897703Z",
+    "biotoolsCURIE": "biotools:atlasgrabber",
+    "biotoolsID": "atlasgrabber",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "benedek.bozoky@ki.se",
+            "name": "Benedek Bozoky",
+            "orcidid": "https://orcid.org/0000-0003-3388-7210",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A software facilitating the high throughput analysis of the human protein atlas online database.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "Sorting",
+                    "uri": "http://edamontology.org/operation_3802"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/b3nb0z/AtlasGrabber",
+    "language": [
+        "C#"
+    ],
+    "lastUpdate": "2023-02-11T07:59:16.900261Z",
+    "license": "GPL-3.0",
+    "name": "AtlasGrabber",
+    "operatingSystem": [
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05097-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: The human protein atlas (HPA) is an online database containing large sets of protein expression data in normal and cancerous tissues in image form from immunohistochemically (IHC) stained tissue microarrays. In these, the tissue architecture is preserved and thus provides information on the spatial distribution and localization of protein expression at the cellular and extracellular levels. The database is freely available online through the HPA website but currently without support for large-scale screening and analysis of the images in the database. Features like spatial information are typically lacking in gene expression datasets from homogenized tissues or single-cell analysis. To enable high throughput analysis of the HPA database, we developed the AtlasGrabber software. It is available freely under an open-source license. Based on a predefined gene list, the software fetches the images from the database and displays them for the user. Several filters for specific antibodies or images enable the user to customize her/his image analysis. Up to four images can be displayed simultaneously, which allows for the comparison of protein expression between different tissues and between normal and cancerous tissues. An additional feature is the XML parser that allows the extraction of a list of available antibodies, images, and genes for specific tissues or cancer types from the HPA’s database file. Results: Compared to existing software designed for a similar purpose, ours provide more functionality and is easier to use. To demonstrate the software’s usability, we identified six new markers of basal cells of the prostate. A comparison to prostate cancer showed that five of them are absent in prostate cancer. Conclusions: The HPA is a uniquely valuable database. By facilitating its usefulness with the AtlasGrabber, we enable researchers to exploit its full capacity. The loss of basal cell markers is diagnostic for prostate cancer and can help refine the histopathological diagnosis of prostate cancer. As proof of concept, with the AtlasGrabber we identified five new potential biomarkers specific for prostate basal cells which are lost in prostate cancer and thus can be used for prostate cancer diagnostics.",
+                "authors": [
+                    {
+                        "name": "Bozoky B."
+                    },
+                    {
+                        "name": "Ernberg I."
+                    },
+                    {
+                        "name": "Savchenko A."
+                    },
+                    {
+                        "name": "Szekely L."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "AtlasGrabber: a software facilitating the high throughput analysis of the human protein atlas online database"
+            },
+            "pmcid": "PMC9758778",
+            "pmid": "36526955"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        }
+    ]
+}
diff --git a/data/autodeconj/autodeconj.biotools.json b/data/autodeconj/autodeconj.biotools.json
new file mode 100644
index 0000000000000..ad292075d18fa
--- /dev/null
+++ b/data/autodeconj/autodeconj.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T14:10:37.470847Z",
+    "biotoolsCURIE": "biotools:autodeconj",
+    "biotoolsID": "autodeconj",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "boxiong11@outlook.com",
+            "name": "Bo Xiong",
+            "orcidid": "https://orcid.org/0000-0001-7815-3603",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A GPU-accelerated ImageJ plugin for 3D light-field deconvolution with optimal iteration numbers predicting.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deisotoping",
+                    "uri": "http://edamontology.org/operation_3629"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Onetism/AutoDeconJ.git",
+    "language": [
+        "Java"
+    ],
+    "lastUpdate": "2023-01-25T14:10:37.473354Z",
+    "license": "MIT",
+    "name": "AutoDeconJ",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC760",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Light-field microscopy (LFM) is a compact solution to high-speed 3D fluorescence imaging. Usually, we need to do 3D deconvolution to the captured raw data. Although there are deep neural network methods that can accelerate the reconstruction process, the model is not universally applicable for all system parameters. Here, we develop AutoDeconJ, a GPU-accelerated ImageJ plugin for 4.4× faster and more accurate deconvolution of LFM data. We further propose an image quality metric for the deconvolution process, aiding in automatically determining the optimal number of iterations with higher reconstruction accuracy and fewer artifacts. RESULTS: Our proposed method outperforms state-of-the-art light-field deconvolution methods in reconstruction time and optimal iteration numbers prediction capability. It shows better universality of different light-field point spread function (PSF) parameters than the deep learning method. The fast, accurate and general reconstruction performance for different PSF parameters suggests its potential for mass 3D reconstruction of LFM data. AVAILABILITY AND IMPLEMENTATION: The codes, the documentation and example data are available on an open source at: https://github.com/Onetism/AutoDeconJ.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Gao Y."
+                    },
+                    {
+                        "name": "Su C."
+                    },
+                    {
+                        "name": "Sun Y."
+                    },
+                    {
+                        "name": "Xiong B."
+                    },
+                    {
+                        "name": "Yan C."
+                    },
+                    {
+                        "name": "Yin H."
+                    },
+                    {
+                        "name": "Zhou Y."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "AutoDeconJ: a GPU-accelerated ImageJ plugin for 3D light-field deconvolution with optimal iteration numbers predicting"
+            },
+            "pmcid": "PMC9805591",
+            "pmid": "36440906"
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "imagej",
+            "type": "uses"
+        }
+    ],
+    "toolType": [
+        "Plug-in"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/automorph/automorph.biotools.json b/data/automorph/automorph.biotools.json
new file mode 100644
index 0000000000000..f40dfd90f93f6
--- /dev/null
+++ b/data/automorph/automorph.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-20T02:31:15.864655Z",
+    "biotoolsCURIE": "biotools:automorph",
+    "biotoolsID": "automorph",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Daniel C. Alexander"
+        },
+        {
+            "name": "Pearse A. Keane"
+        },
+        {
+            "name": "Siegfried K. Wagner"
+        },
+        {
+            "name": "Yukun Zhou"
+        }
+    ],
+    "description": "Automated Retinal Vascular Morphology Quantification via a Deep Learning Pipeline.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image annotation",
+                    "uri": "http://edamontology.org/operation_3553"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/rmaphoh/AutoMorph",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-20T02:31:15.867106Z",
+    "license": "Apache-2.0",
+    "name": "AutoMorph",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1167/tvst.11.7.12",
+            "metadata": {
+                "abstract": "© 2022 The Authors.Purpose: To externally validate a deep learning pipeline (AutoMorph) for automated analysis of retinal vascular morphology on fundus photographs. AutoMorph has been made publicly available, facilitating widespread research in ophthalmic and systemic diseases. Methods: AutoMorph consists of four functional modules: image preprocessing, image quality grading, anatomical segmentation (including binary vessel, artery/vein, and optic disc/cup segmentation), and vascular morphology feature measurement. Image quality grading and anatomical segmentation use the most recent deep learning techniques. We employ a model ensemble strategy to achieve robust results and analyze the prediction confidence to rectify false gradable cases in image quality grading. We externally validate the performance of each module on several independent publicly available datasets. Results: The EfficientNet-b4 architecture used in the image grading module achieves performance comparable to that of the state of the art for EyePACS-Q, with an F1-score of 0.86. The confidence analysis reduces the number of images incorrectly assessed as gradable by 76%. Binary vessel segmentation achieves an F1-score of 0.73 on AV-WIDE and 0.78 on DR HAGIS. Artery/vein scores are 0.66 on IOSTAR-AV, and disc segmentation achieves 0.94 in IDRID. Vascular morphology features measured from the AutoMorph segmentation map and expert annotation show good to excellent agreement. Conclusions: AutoMorph modules perform well even when external validation data show domain differences from training data (e.g., with different imaging devices). This fully automated pipeline can thus allow detailed, efficient, and comprehensive analysis of retinal vascular morphology on color fundus photographs. Translational Relevance: By making AutoMorph publicly available and open source, we hope to facilitate ophthalmic and systemic disease research, particularly in the emerging field of oculomics.",
+                "authors": [
+                    {
+                        "name": "Alexander D.C."
+                    },
+                    {
+                        "name": "Chia M.A."
+                    },
+                    {
+                        "name": "Keane P.A."
+                    },
+                    {
+                        "name": "Struyven R."
+                    },
+                    {
+                        "name": "Wagner S.K."
+                    },
+                    {
+                        "name": "Woodward-Court P."
+                    },
+                    {
+                        "name": "Xu M."
+                    },
+                    {
+                        "name": "Zhao A."
+                    },
+                    {
+                        "name": "Zhou Y."
+                    }
+                ],
+                "date": "2022-07-01T00:00:00Z",
+                "journal": "Translational Vision Science and Technology",
+                "title": "AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline"
+            },
+            "pmcid": "PMC9290317",
+            "pmid": "35833885"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/autoscore_ordinal/autoscore_ordinal.biotools.json b/data/autoscore_ordinal/autoscore_ordinal.biotools.json
new file mode 100644
index 0000000000000..24a08d1702520
--- /dev/null
+++ b/data/autoscore_ordinal/autoscore_ordinal.biotools.json
@@ -0,0 +1,108 @@
+{
+    "additionDate": "2023-01-25T14:18:37.861096Z",
+    "biotoolsCURIE": "biotools:autoscore_ordinal",
+    "biotoolsID": "autoscore_ordinal",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "liu.nan@duke-nus.edu.sg",
+            "name": "Nan Liu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "AutoScore-Ordinal is a novel machine learning framework to automate the development of interpretable clinical scoring models for ordinal outcomes, which expands the original AutoScore framework for binary outcomes.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature selection",
+                    "uri": "http://edamontology.org/operation_3936"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/nliulab/AutoScore-Ordinal",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-25T14:18:37.863765Z",
+    "license": "Not licensed",
+    "name": "AutoScore-Ordinal",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12874-022-01770-Y",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Risk prediction models are useful tools in clinical decision-making which help with risk stratification and resource allocations and may lead to a better health care for patients. AutoScore is a machine learning–based automatic clinical score generator for binary outcomes. This study aims to expand the AutoScore framework to provide a tool for interpretable risk prediction for ordinal outcomes. Methods: The AutoScore-Ordinal framework is generated using the same 6 modules of the original AutoScore algorithm including variable ranking, variable transformation, score derivation (from proportional odds models), model selection, score fine-tuning, and model evaluation. To illustrate the AutoScore-Ordinal performance, the method was conducted on electronic health records data from the emergency department at Singapore General Hospital over 2008 to 2017. The model was trained on 70% of the data, validated on 10% and tested on the remaining 20%. Results: This study included 445,989 inpatient cases, where the distribution of the ordinal outcome was 80.7% alive without 30-day readmission, 12.5% alive with 30-day readmission, and 6.8% died inpatient or by day 30 post discharge. Two point-based risk prediction models were developed using two sets of 8 predictor variables identified by the flexible variable selection procedure. The two models indicated reasonably good performance measured by mean area under the receiver operating characteristic curve (0.758 and 0.793) and generalized c-index (0.737 and 0.760), which were comparable to alternative models. Conclusion: AutoScore-Ordinal provides an automated and easy-to-use framework for development and validation of risk prediction models for ordinal outcomes, which can systematically identify potential predictors from high-dimensional data.",
+                "authors": [
+                    {
+                        "name": "Chakraborty B."
+                    },
+                    {
+                        "name": "Liu N."
+                    },
+                    {
+                        "name": "Ning Y."
+                    },
+                    {
+                        "name": "Ong M.E.H."
+                    },
+                    {
+                        "name": "Saffari S.E."
+                    },
+                    {
+                        "name": "Vaughan R."
+                    },
+                    {
+                        "name": "Volovici V."
+                    },
+                    {
+                        "name": "Xie F."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Medical Research Methodology",
+                "title": "AutoScore-Ordinal: an interpretable machine learning framework for generating scoring models for ordinal outcomes"
+            },
+            "pmcid": "PMC9636613",
+            "pmid": "36333672"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical informatics",
+            "uri": "http://edamontology.org/topic_3063"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        }
+    ]
+}
diff --git a/data/avp/avp.biotools.json b/data/avp/avp.biotools.json
new file mode 100644
index 0000000000000..9ddee1ee7ceab
--- /dev/null
+++ b/data/avp/avp.biotools.json
@@ -0,0 +1,133 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T22:40:06.256128Z",
+    "biotoolsCURIE": "biotools:avp",
+    "biotoolsID": "avp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "georgios.koutsovoulos@inrae.fr",
+            "name": "Georgios D. Koutsovoulos",
+            "orcidid": "http://orcid.org/0000-0003-3406-3715",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Solène Granjeon Noriot"
+        },
+        {
+            "name": "Corinne Rancurel",
+            "orcidid": "http://orcid.org/0000-0001-9355-5491"
+        },
+        {
+            "name": "Etienne G. J. Danchin",
+            "orcidid": "http://orcid.org/0000-0003-4146-5608"
+        },
+        {
+            "name": "Marc Bailly-Bechet",
+            "orcidid": "http://orcid.org/0000-0002-6032-1127"
+        }
+    ],
+    "description": "A software package for automatic phylogenetic detection of candidate horizontal gene transfers.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/GDKO/AvP/wiki"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene tree construction",
+                    "uri": "http://edamontology.org/operation_0553"
+                },
+                {
+                    "term": "Phylogenetic reconstruction",
+                    "uri": "http://edamontology.org/operation_3478"
+                },
+                {
+                    "term": "Phylogenetic tree annotation",
+                    "uri": "http://edamontology.org/operation_0558"
+                },
+                {
+                    "term": "Phylogenetic tree reconciliation",
+                    "uri": "http://edamontology.org/operation_3947"
+                },
+                {
+                    "term": "Phylogenetic tree visualisation",
+                    "uri": "http://edamontology.org/operation_0567"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/GDKO/AvP",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T22:40:06.258574Z",
+    "license": "GPL-3.0",
+    "name": "AvP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/journal.pcbi.1010686",
+            "metadata": {
+                "abstract": "© 2022 Koutsovoulos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Horizontal gene transfer (HGT) is the transfer of genes between species outside the transmission from parent to offspring. Due to their impact on the genome and biology of various species, HGTs have gained broader attention, but high-throughput methods to robustly identify them are lacking. One rapid method to identify HGT candidates is to calculate the difference in similarity between the most similar gene in closely related species and the most similar gene in distantly related species. Although metrics on similarity associated with taxonomic information can rapidly detect putative HGTs, these methods are hampered by false positives that are difficult to track. Furthermore, they do not inform on the evolutionary trajectory and events such as duplications. Hence, phylogenetic analysis is necessary to confirm HGT candidates and provide a more comprehensive view of their origin and evolutionary history. However, phylogenetic reconstruction requires several time-consuming manual steps to retrieve the homologous sequences, produce a multiple alignment, construct the phylogeny and analyze the topology to assess whether it supports the HGT hypothesis. Here, we present AvP which automatically performs all these steps and detects candidate HGTs within a phylogenetic framework.",
+                "authors": [
+                    {
+                        "name": "Bailly-Bechet M."
+                    },
+                    {
+                        "name": "Danchin E.G.J."
+                    },
+                    {
+                        "name": "Koutsovoulos G.D."
+                    },
+                    {
+                        "name": "Noriot S.G."
+                    },
+                    {
+                        "name": "Rancurel C."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "AvP: A software package for automatic phylogenetic detection of candidate horizontal gene transfers"
+            },
+            "pmcid": "PMC9678320",
+            "pmid": "36350852"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cladistics",
+            "uri": "http://edamontology.org/topic_3944"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Phylogenomics",
+            "uri": "http://edamontology.org/topic_0194"
+        },
+        {
+            "term": "Taxonomy",
+            "uri": "http://edamontology.org/topic_0637"
+        }
+    ]
+}
diff --git a/data/bakta/bakta.biotools.json b/data/bakta/bakta.biotools.json
index 0febf9746a21e..55a4e5e60af88 100644
--- a/data/bakta/bakta.biotools.json
+++ b/data/bakta/bakta.biotools.json
@@ -58,8 +58,8 @@
         {
             "note": "Mandatory annotation database",
             "type": "Other",
-            "url": "https://zenodo.org/record/7025248",
-            "version": "v4.0"
+            "url": "https://zenodo.org/record/7669534",
+            "version": "v5.0"
         }
     ],
     "editPermission": {
@@ -165,7 +165,7 @@
     "language": [
         "Python"
     ],
-    "lastUpdate": "2022-11-15T08:45:08.838885Z",
+    "lastUpdate": "2023-02-28T13:37:28.434982Z",
     "license": "GPL-3.0",
     "link": [
         {
@@ -225,7 +225,7 @@
                         "name": "Schwengers O."
                     }
                 ],
-                "citationCount": 10,
+                "citationCount": 21,
                 "date": "2021-01-01T00:00:00Z",
                 "journal": "Microbial Genomics",
                 "title": "Bakta: Rapid and standardized annotation of bacterial genomes via alignment-free sequence identification"
@@ -291,6 +291,6 @@
         }
     ],
     "version": [
-        "v1.5.1"
+        "v1.7.0"
     ]
 }
diff --git a/data/bcftools/bcftools.biotools.json b/data/bcftools/bcftools.biotools.json
index e4fdfd1609181..d7c9acf90729b 100644
--- a/data/bcftools/bcftools.biotools.json
+++ b/data/bcftools/bcftools.biotools.json
@@ -106,7 +106,7 @@
     "language": [
         "C"
     ],
-    "lastUpdate": "2022-08-18T14:23:49.930158Z",
+    "lastUpdate": "2023-02-21T14:53:01.845478Z",
     "license": "MIT",
     "link": [
         {
@@ -170,7 +170,7 @@
                         "name": "Wysoker A."
                     }
                 ],
-                "citationCount": 29149,
+                "citationCount": 31575,
                 "date": "2009-08-01T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "The Sequence Alignment/Map format and SAMtools"
@@ -185,7 +185,7 @@
         {
             "doi": "10.1093/gigascience/giab008",
             "metadata": {
-                "abstract": "© The Author(s) 2021. Published by Oxford University Press GigaScience.BACKGROUND: SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. FINDINGS: The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. CONCLUSION: Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.",
+                "abstract": "© 2021 The Author(s). Published by Oxford University Press GigaScience.Background: SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. Findings: The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. Conclusion: Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.",
                 "authors": [
                     {
                         "name": "Bonfield J.K."
@@ -199,9 +199,6 @@
                     {
                         "name": "Keane T."
                     },
-                    {
-                        "name": "Li H."
-                    },
                     {
                         "name": "Liddle J."
                     },
@@ -221,8 +218,8 @@
                         "name": "Whitwham A."
                     }
                 ],
-                "citationCount": 482,
-                "date": "2021-02-16T00:00:00Z",
+                "citationCount": 978,
+                "date": "2021-02-01T00:00:00Z",
                 "journal": "GigaScience",
                 "title": "Twelve years of SAMtools and BCFtools"
             },
@@ -276,6 +273,7 @@
         "1.15",
         "1.15.1",
         "1.16",
+        "1.17",
         "1.2",
         "1.3",
         "1.3.1",
diff --git a/data/benchdamic/benchdamic.biotools.json b/data/benchdamic/benchdamic.biotools.json
new file mode 100644
index 0000000000000..c5c13da203d91
--- /dev/null
+++ b/data/benchdamic/benchdamic.biotools.json
@@ -0,0 +1,76 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-11T08:04:24.883378Z",
+    "biotoolsCURIE": "biotools:benchdamic",
+    "biotoolsID": "benchdamic",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "davide.risso@unipd.it",
+            "name": "Davide Risso",
+            "orcidid": "https://orcid.org/0000-0001-8508-5012",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "nicola.vitulo@univr.it",
+            "name": "Nicola Vitulo",
+            "orcidid": "https://orcid.org/0000-0002-9571-0747",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Benchmark of differential abundance methods on microbiome data.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://bioconductor.org/packages/benchdamic/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-11T08:04:24.886081Z",
+    "license": "Artistic-2.0",
+    "name": "benchdamic",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC778",
+            "pmcid": "PMC9825737",
+            "pmid": "36477500"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        }
+    ]
+}
diff --git a/data/bepipred-3.0/bepipred-3.0.biotools.json b/data/bepipred-3.0/bepipred-3.0.biotools.json
new file mode 100644
index 0000000000000..7766bcf7fb255
--- /dev/null
+++ b/data/bepipred-3.0/bepipred-3.0.biotools.json
@@ -0,0 +1,139 @@
+{
+    "additionDate": "2023-01-25T14:34:10.847516Z",
+    "biotoolsCURIE": "biotools:bepipred-3.0",
+    "biotoolsID": "bepipred-3.0",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "cliffordjoakim@gmail.com",
+            "name": "Joakim Nøddeskov Clifford",
+            "orcidid": "https://orcid.org/0000-0002-8126-9209",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Bjoern Peters"
+        },
+        {
+            "name": "Magnus Haraldson Høie"
+        },
+        {
+            "name": "Morten Nielsen"
+        },
+        {
+            "name": "Paolo Marcatili"
+        },
+        {
+            "name": "Sebastian Deleuran"
+        }
+    ],
+    "description": "Improved B-cell epitope prediction using protein language models.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Backbone modelling",
+                    "uri": "http://edamontology.org/operation_0479"
+                },
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                },
+                {
+                    "term": "Side chain modelling",
+                    "uri": "http://edamontology.org/operation_0480"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://services.healthtech.dtu.dk/service.php?BepiPred-3.0",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T14:34:10.850141Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://biolib.com/DTU/BepiPred-3/"
+        }
+    ],
+    "name": "BepiPred-3.0",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1002/PRO.4497",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.B-cell epitope prediction tools are of great medical and commercial interest due to their practical applications in vaccine development and disease diagnostics. The introduction of protein language models (LMs), trained on unprecedented large datasets of protein sequences and structures, tap into a powerful numeric representation that can be exploited to accurately predict local and global protein structural features from amino acid sequences only. In this paper, we present BepiPred-3.0, a sequence-based epitope prediction tool that, by exploiting LM embeddings, greatly improves the prediction accuracy for both linear and conformational epitope prediction on several independent test sets. Furthermore, by carefully selecting additional input variables and epitope residue annotation strategy, performance was further improved, thus achieving unprecedented predictive power. Our tool can predict epitopes across hundreds of sequences in minutes. It is freely available as a web server and a standalone package at https://services.healthtech.dtu.dk/service.php?BepiPred-3.0 with a user-friendly interface to navigate the results.",
+                "authors": [
+                    {
+                        "name": "Clifford J.N."
+                    },
+                    {
+                        "name": "Deleuran S."
+                    },
+                    {
+                        "name": "Hoie M.H."
+                    },
+                    {
+                        "name": "Marcatili P."
+                    },
+                    {
+                        "name": "Nielsen M."
+                    },
+                    {
+                        "name": "Peters B."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Protein Science",
+                "title": "BepiPred-3.0: Improved B-cell epitope prediction using protein language models"
+            },
+            "pmcid": "PMC9679979",
+            "pmid": "36366745"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Immunogenetics",
+            "uri": "http://edamontology.org/topic_3930"
+        },
+        {
+            "term": "Immunoinformatics",
+            "uri": "http://edamontology.org/topic_3948"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Vaccinology",
+            "uri": "http://edamontology.org/topic_3966"
+        }
+    ],
+    "version": [
+        "3.0"
+    ]
+}
diff --git a/data/best_bam-error-stats-tool/best_bam-error-stats-tool.biotools.json b/data/best_bam-error-stats-tool/best_bam-error-stats-tool.biotools.json
new file mode 100644
index 0000000000000..f354db8f3a76b
--- /dev/null
+++ b/data/best_bam-error-stats-tool/best_bam-error-stats-tool.biotools.json
@@ -0,0 +1,67 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-06T14:15:37.670188Z",
+    "biotoolsCURIE": "biotools:best_Bam-Error-Stats-Tool",
+    "biotoolsID": "best_Bam-Error-Stats-Tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Daniel Liu, Daniel E. Cook"
+        }
+    ],
+    "description": "Bam Error Stats Tool (best): analysis of error types in aligned reads.\nbest is used to assess the quality of reads after aligning them to a reference assembly.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/google/best/blob/main/Usage.md"
+        }
+    ],
+    "download": [
+        {
+            "note": "Github page",
+            "type": "Source code",
+            "url": "https://github.com/google/best",
+            "version": "0.1.0"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Sequence alignment validation",
+                    "uri": "http://edamontology.org/operation_0447"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/google/best",
+    "language": [
+        "Other"
+    ],
+    "lastUpdate": "2023-01-06T14:15:37.673685Z",
+    "license": "MIT",
+    "maturity": "Emerging",
+    "name": "best",
+    "owner": "pauffret",
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ],
+    "version": [
+        "0.1.0"
+    ]
+}
diff --git a/data/bestdeg/bestdeg.biotools.json b/data/bestdeg/bestdeg.biotools.json
new file mode 100644
index 0000000000000..13894e965efba
--- /dev/null
+++ b/data/bestdeg/bestdeg.biotools.json
@@ -0,0 +1,102 @@
+{
+    "additionDate": "2023-01-25T14:41:28.047137Z",
+    "biotoolsCURIE": "biotools:bestdeg",
+    "biotoolsID": "bestdeg",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "unitsa.s@psu.ac.th",
+            "name": "Unitsa Sangket",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A web-based application automatically combines various tools to precisely predict differentially expressed genes (DEGs) from RNA-Seq data.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "RNA-Seq analysis",
+                    "uri": "http://edamontology.org/operation_3680"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://unitsa.shinyapps.io/bestDEG",
+    "language": [
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-25T14:41:28.049477Z",
+    "license": "Other",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/unitsa-sangket/bestDEG"
+        }
+    ],
+    "name": "bestDEG",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.7717/PEERJ.14344",
+            "metadata": {
+                "abstract": "Copyright © 2022 Sangket et al.Background. Differential gene expression analysis using RNA sequencing technology (RNA-Seq) has become the most popular technique in transcriptome research. Although many R packages have been developed to analyze differentially expressed genes (DEGs), several evaluations have shown that no single DEG analysis method outperforms all others. The validity of DEG identification could be increased by using multiple methods and producing the consensus results. However, DEG analysis methods are complex and most of them require prior knowledge of a programming language or command-line shell. Users who do not have this knowledge need to invest time and effort to acquire it. Methods. We developed a novel web application called ''bestDEG'' to automatically analyze DEGs with different tools and compare the results. A differential expression (DE) analysis pipeline was created combining the edgeR, DESeq2, NOISeq, and EBSeq packages; selected because they use different statistical methods to identify DEGs. bestDEG was evaluated on human datasets from the MicroArray Quality Control (MAQC) project. Results. The performance of the bestDEG web application with the human datasets showed excellent results, and the consensus method outperformed the other DE analysis methods in terms of precision (94.71%) and specificity (97.01%). bestDEG is a rapid and efficient tool to analyze DEGs. With bestDEG, users can select DE analysis methods and parameters in the user-friendly web interface. bestDEG also provides a Venn diagram and a table of results. Moreover, the consensus method of this tool can maximize the precision or minimize the false discovery rate (FDR), which reduces the cost of gene expression validation by minimizing wet-lab experiments.",
+                "authors": [
+                    {
+                        "name": "Nuanpirom J."
+                    },
+                    {
+                        "name": "Sangket U."
+                    },
+                    {
+                        "name": "Sathapondecha P."
+                    },
+                    {
+                        "name": "Yodsawat P."
+                    }
+                ],
+                "date": "2022-11-10T00:00:00Z",
+                "journal": "PeerJ",
+                "title": "bestDEG: a web-based application automatically combines various tools to precisely predict differentially expressed genes (DEGs) from RNA-Seq data"
+            },
+            "pmcid": "PMC9657178",
+            "pmid": "36389403"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/beta_psmc/beta_psmc.biotools.json b/data/beta_psmc/beta_psmc.biotools.json
new file mode 100644
index 0000000000000..079f8470deec1
--- /dev/null
+++ b/data/beta_psmc/beta_psmc.biotools.json
@@ -0,0 +1,74 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-12T15:12:16.318388Z",
+    "biotoolsCURIE": "biotools:beta_psmc",
+    "biotoolsID": "beta_psmc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chenh@big.ac.cn",
+            "name": "Hua Chen",
+            "orcidid": "https://orcid.org/0000-0002-9829-6561",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A new method called Beta-PSMC, which introduces the probability density function of a beta distribution with a broad variety of shapes into the Pairwise Sequentially Markovian Coalescent (PSMC) model to refine the population history in each discretized time interval in place of the assumption that the population size is unchanged.",
+    "editPermission": {
+        "type": "public"
+    },
+    "homepage": "https://github.com/chenh-big/Beta-PSMC",
+    "language": [
+        "C",
+        "Perl"
+    ],
+    "lastUpdate": "2023-02-12T15:12:16.321225Z",
+    "license": "MIT",
+    "name": "Beta-PSMC",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12864-022-09021-6",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Inferring the demographic history of a population is essential in population genetic studies. Though the inference methods based on the sequentially Markov coalescent can present the population history in detail, these methods assume that the population size remains unchanged in each time interval during discretizing the hidden state in the hidden Markov model. Therefore, these methods fail to uncover the detailed population history in each time interval. Results: We present a new method called Beta-PSMC, which introduces the probability density function of a beta distribution with a broad variety of shapes into the Pairwise Sequentially Markovian Coalescent (PSMC) model to refine the population history in each discretized time interval in place of the assumption that the population size is unchanged. Using simulation, we demonstrate that Beta-PSMC can uncover more detailed population history, and improve the accuracy and resolution of the recent population history inference. We also apply Beta-PSMC to infer the population history of Adélie penguin and find that the fluctuation in population size is contrary to the temperature change 15–27 thousand years ago. Conclusions: Beta-PSMC extends PSMC by allowing more detailed fluctuation of population size in each discretized time interval with the probability density function of beta distribution and will serve as a useful tool for population genetics.",
+                "authors": [
+                    {
+                        "name": "Chen H."
+                    },
+                    {
+                        "name": "Ji X."
+                    },
+                    {
+                        "name": "Liu J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Genomics",
+                "title": "Beta-PSMC: uncovering more detailed population history using beta distribution"
+            },
+            "pmcid": "PMC9710181",
+            "pmid": "36451098"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Population genetics",
+            "uri": "http://edamontology.org/topic_3056"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/bglm/bglm.biotools.json b/data/bglm/bglm.biotools.json
new file mode 100644
index 0000000000000..4d5faa899da05
--- /dev/null
+++ b/data/bglm/bglm.biotools.json
@@ -0,0 +1,94 @@
+{
+    "additionDate": "2023-01-25T14:47:00.642604Z",
+    "biotoolsCURIE": "biotools:bglm",
+    "biotoolsID": "bglm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "david.chesla@spectrumhealth.org",
+            "name": "Dave Chesla",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Big data-guided LOINC code mapper (BGLM), which leverages the large amount of patient data stored in EHR systems to perform LOINC coding mapping",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Bin-Chen-Lab/BGLM",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-25T14:47:00.645233Z",
+    "license": "Not licensed",
+    "name": "BGLM",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/JAMIAOPEN/OOAC099",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press on behalf of the American Medical Informatics Association.Motivation: Mapping internal, locally used lab test codes to standardized logical observation identifiers names and codes (LOINC) terminology has become an essential step in harmonizing electronic health record (EHR) data across different institutions. However, most existing LOINC code mappers are based on text-mining technology and do not provide robust multi-language support. Materials and methods: We introduce a simple, yet effective tool called big data-guided LOINC code mapper (BGLM), which leverages the large amount of patient data stored in EHR systems to perform LOINC coding mapping. Distinguishing from existing methods, BGLM conducts mapping based on distributional similarity. Results: We validated the performance of BGLM with real-world datasets and showed that high mapping precision could be achieved under proper false discovery rate control. In addition, we showed that the mapping results of BGLM could be used to boost the performance of Regenstrief LOINC Mapping Assistant (RELMA), one of the most widely used LOINC code mappers. Conclusions: BGLM paves a new way for LOINC code mapping and therefore could be applied to EHR systems without the restriction of languages. BGLM is freely available at https://github.com/Bin-Chen-Lab/BGLM.",
+                "authors": [
+                    {
+                        "name": "Chekalin E."
+                    },
+                    {
+                        "name": "Chen B."
+                    },
+                    {
+                        "name": "Chesla D."
+                    },
+                    {
+                        "name": "Glicksberg B.S."
+                    },
+                    {
+                        "name": "Kulkarni O."
+                    },
+                    {
+                        "name": "Liu K."
+                    },
+                    {
+                        "name": "Paithankar S."
+                    },
+                    {
+                        "name": "Shankar R."
+                    },
+                    {
+                        "name": "Witteveen-Lane M."
+                    },
+                    {
+                        "name": "Yang J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "JAMIA Open",
+                "title": "BGLM: big data-guided LOINC mapping with multi-language support"
+            },
+            "pmcid": "PMC9696745",
+            "pmid": "36448022"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        }
+    ]
+}
diff --git a/data/bgnet/bgnet.biotools.json b/data/bgnet/bgnet.biotools.json
new file mode 100644
index 0000000000000..6aa64e0e1c81c
--- /dev/null
+++ b/data/bgnet/bgnet.biotools.json
@@ -0,0 +1,124 @@
+{
+    "additionDate": "2023-01-26T14:56:04.484932Z",
+    "biotoolsCURIE": "biotools:bgnet",
+    "biotoolsID": "bgnet",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "hliu@ict.ac.cn",
+            "name": "Hong Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "huishuy@vip.163.com",
+            "name": "Hui-Shu Yuan",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "jiangliang@bjmu.edu.cn",
+            "name": "Liang Jiang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Classification of benign and malignant tumors with MRI multi-plane attention learning.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Sorting",
+                    "uri": "http://edamontology.org/operation_3802"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/research-med/BgNet",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-26T14:56:04.487732Z",
+    "license": "Not licensed",
+    "name": "BgNet",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FONC.2022.971871",
+            "metadata": {
+                "abstract": "Copyright © 2022 Liu, Jiao, Xing, Ou-Yang, Yuan, Liu, Li, Wang, Lang, Qian, Jiang, Yuan and Wang.Objectives: To propose a deep learning-based classification framework, which can carry out patient-level benign and malignant tumors classification according to the patient’s multi-plane images and clinical information. Methods: A total of 430 cases of spinal tumor, including axial and sagittal plane images by MRI, of which 297 cases for training (14072 images), and 133 cases for testing (6161 images) were included. Based on the bipartite graph and attention learning, this study proposed a multi-plane attention learning framework, BgNet, for benign and malignant tumor diagnosis. In a bipartite graph structure, the tumor area in each plane is used as the vertex of the graph, and the matching between different planes is used as the edge of the graph. The tumor areas from different plane images are spliced at the input layer. And based on the convolutional neural network ResNet and visual attention learning model Swin-Transformer, this study proposed a feature fusion model named ResNetST for combining both global and local information to extract the correlation features of multiple planes. The proposed BgNet consists of five modules including a multi-plane fusion module based on the bipartite graph, input layer fusion module, feature layer fusion module, decision layer fusion module, and output module. These modules are respectively used for multi-level fusion of patient multi-plane image data to realize the comprehensive diagnosis of benign and malignant tumors at the patient level. Results: The accuracy (ACC: 79.7%) of the proposed BgNet with multi-plane was higher than that with a single plane, and higher than or equal to the four doctors’ ACC (D1: 70.7%, p=0.219; D2: 54.1%, p<0.005; D3: 79.7%, p=0.006; D4: 72.9%, p=0.178). Moreover, the diagnostic accuracy and speed of doctors can be further improved with the aid of BgNet, the ACC of D1, D2, D3, and D4 improved by 4.5%, 21.8%, 0.8%, and 3.8%, respectively. Conclusions: The proposed deep learning framework BgNet can classify benign and malignant tumors effectively, and can help doctors improve their diagnostic efficiency and accuracy. The code is available at https://github.com/research-med/BgNet.",
+                "authors": [
+                    {
+                        "name": "Jiang L."
+                    },
+                    {
+                        "name": "Jiao M.-L."
+                    },
+                    {
+                        "name": "Lang N."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Liu H."
+                    },
+                    {
+                        "name": "Liu J.-F."
+                    },
+                    {
+                        "name": "Ou-Yang H.-Q."
+                    },
+                    {
+                        "name": "Qian Y.-L."
+                    },
+                    {
+                        "name": "Wang C.-J."
+                    },
+                    {
+                        "name": "Wang X.-D."
+                    },
+                    {
+                        "name": "Xing X.-Y."
+                    },
+                    {
+                        "name": "Yuan H.-S."
+                    },
+                    {
+                        "name": "Yuan Y."
+                    }
+                ],
+                "date": "2022-10-27T00:00:00Z",
+                "journal": "Frontiers in Oncology",
+                "title": "BgNet: Classification of benign and malignant tumors with MRI multi-plane attention learning"
+            },
+            "pmcid": "PMC9646829",
+            "pmid": "36387085"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        }
+    ]
+}
diff --git a/data/bigknock/bigknock.biotools.json b/data/bigknock/bigknock.biotools.json
new file mode 100644
index 0000000000000..d85e21e93835b
--- /dev/null
+++ b/data/bigknock/bigknock.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-09T08:02:41.256296Z",
+    "biotoolsCURIE": "biotools:bigknock",
+    "biotoolsID": "bigknock",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ii2135@columbia.edu",
+            "name": "Iuliana Ionita-Laza",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "R package of performing biobank-scale gene-based association test via knockoffs.",
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://zenodo.org/record/7524304"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Variant prioritisation",
+                    "uri": "http://edamontology.org/operation_3226"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Iuliana-Ionita-Laza/BIGKnock",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-09T08:02:41.260326Z",
+    "license": "GPL-3.0",
+    "name": "BIGKnock",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S13059-023-02864-6",
+            "metadata": {
+                "abstract": "We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gene-based tests, BIGKnock produces smaller sets of significant genes that contain the causal gene(s) with high probability. We further illustrate its ability to pinpoint potential causal genes at ∼ 80 % of the associated loci.",
+                "authors": [
+                    {
+                        "name": "Dalgleish J."
+                    },
+                    {
+                        "name": "He Z."
+                    },
+                    {
+                        "name": "Ionita-Laza I."
+                    },
+                    {
+                        "name": "Khan A."
+                    },
+                    {
+                        "name": "Kiryluk K."
+                    },
+                    {
+                        "name": "Liu L."
+                    },
+                    {
+                        "name": "Ma S."
+                    },
+                    {
+                        "name": "Wang C."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Genome Biology",
+                "title": "BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data"
+            },
+            "pmcid": "PMC9926792",
+            "pmid": "36782330"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/biobygans/biobygans.biotools.json b/data/biobygans/biobygans.biotools.json
new file mode 100644
index 0000000000000..d7885df4ab28d
--- /dev/null
+++ b/data/biobygans/biobygans.biotools.json
@@ -0,0 +1,99 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T15:01:17.774269Z",
+    "biotoolsCURIE": "biotools:biobygans",
+    "biotoolsID": "biobygans",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dszhao@bmi.ac.cn",
+            "name": "Dongsheng Zhao",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Named-entity and concept recognition",
+                    "uri": "http://edamontology.org/operation_3280"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/zxw1995shawn/BioByGANS",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-26T15:01:17.776965Z",
+    "license": "Not licensed",
+    "name": "BioByGANS",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05051-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Automatic and accurate recognition of various biomedical named entities from literature is an important task of biomedical text mining, which is the foundation of extracting biomedical knowledge from unstructured texts into structured formats. Using the sequence labeling framework and deep neural networks to implement biomedical named entity recognition (BioNER) is a common method at present. However, the above method often underutilizes syntactic features such as dependencies and topology of sentences. Therefore, it is an urgent problem to be solved to integrate semantic and syntactic features into the BioNER model. Results: In this paper, we propose a novel biomedical named entity recognition model, named BioByGANS (BioBERT/SpaCy-Graph Attention Network-Softmax), which uses a graph to model the dependencies and topology of a sentence and formulate the BioNER task as a node classification problem. This formulation can introduce more topological features of language and no longer be only concerned about the distance between words in the sequence. First, we use periods to segment sentences and spaces and symbols to segment words. Second, contextual features are encoded by BioBERT, and syntactic features such as part of speeches, dependencies and topology are preprocessed by SpaCy respectively. A graph attention network is then used to generate a fusing representation considering both the contextual features and syntactic features. Last, a softmax function is used to calculate the probabilities and get the results. We conduct experiments on 8 benchmark datasets, and our proposed model outperforms existing BioNER state-of-the-art methods on the BC2GM, JNLPBA, BC4CHEMD, BC5CDR-chem, BC5CDR-disease, NCBI-disease, Species-800, and LINNAEUS datasets, and achieves F1-scores of 85.15%, 78.16%, 92.97%, 94.74%, 87.74%, 91.57%, 75.01%, 90.99%, respectively. Conclusion: The experimental results on 8 biomedical benchmark datasets demonstrate the effectiveness of our model, and indicate that formulating the BioNER task into a node classification problem and combining syntactic features into the graph attention networks can significantly improve model performance.",
+                "authors": [
+                    {
+                        "name": "Du H."
+                    },
+                    {
+                        "name": "Luo X."
+                    },
+                    {
+                        "name": "Song W."
+                    },
+                    {
+                        "name": "Tong F."
+                    },
+                    {
+                        "name": "Zhao D."
+                    },
+                    {
+                        "name": "Zheng X."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework"
+            },
+            "pmcid": "PMC9682683",
+            "pmid": "36418937"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/biomthermdb/biomthermdb.biotools.json b/data/biomthermdb/biomthermdb.biotools.json
new file mode 100644
index 0000000000000..179225a2592ea
--- /dev/null
+++ b/data/biomthermdb/biomthermdb.biotools.json
@@ -0,0 +1,109 @@
+{
+    "additionDate": "2023-02-12T15:14:56.229603Z",
+    "biotoolsCURIE": "biotools:biomthermdb",
+    "biotoolsID": "biomthermdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "barbara.hribar@fkkt.uni-lj.si",
+            "name": "Barbara Hribar-Lee",
+            "orcidid": "https://orcid.org/0000-0002-9029-588X",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "BioMThermDB is a collection of thermodynamic and dynamic properties of various proteins and their (aqueous) solutions, extracted from the vast and mostly scattered scientific literature.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Nucleic acid structure prediction",
+                    "uri": "http://edamontology.org/operation_0475"
+                },
+                {
+                    "term": "Nucleic acid thermodynamic property calculation",
+                    "uri": "http://edamontology.org/operation_0455"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://phys-biol-modeling.fkkt.uni-lj.si/biomthermdb.html",
+    "lastUpdate": "2023-02-12T15:14:56.232133Z",
+    "license": "Not licensed",
+    "name": "BioMThermDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3390/IJMS232315371",
+            "metadata": {
+                "abstract": "© 2022 by the authors.We present here a freely available web-based database, called BioMThermDB 1.0, of thermophysical and dynamic properties of various proteins and their aqueous solutions. It contains the hydrodynamic radius, electrophoretic mobility, zeta potential, self-diffusion coefficient, solution viscosity, and cloud-point temperature, as well as the conditions for those determinations and details of the experimental method. It can facilitate the meta-analysis and visualization of data, can enable comparisons, and may be useful for comparing theoretical model predictions with experiments.",
+                "authors": [
+                    {
+                        "name": "Brudar S."
+                    },
+                    {
+                        "name": "Coutsias E."
+                    },
+                    {
+                        "name": "Dill K.A."
+                    },
+                    {
+                        "name": "Hribar-Lee B."
+                    },
+                    {
+                        "name": "Luksic M."
+                    },
+                    {
+                        "name": "Nikolic M."
+                    },
+                    {
+                        "name": "Simmerling C."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "International Journal of Molecular Sciences",
+                "title": "BioMThermDB 1.0: Thermophysical Database of Proteins in Solutions"
+            },
+            "pmcid": "PMC9741033",
+            "pmid": "36499696"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Membrane and lipoproteins",
+            "uri": "http://edamontology.org/topic_0820"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        },
+        {
+            "term": "Protein properties",
+            "uri": "http://edamontology.org/topic_0123"
+        }
+    ],
+    "version": [
+        "1.0"
+    ]
+}
diff --git a/data/bioplex/bioplex.biotools.json b/data/bioplex/bioplex.biotools.json
new file mode 100644
index 0000000000000..cd48a12cd686e
--- /dev/null
+++ b/data/bioplex/bioplex.biotools.json
@@ -0,0 +1,439 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-09T08:16:32.218358Z",
+    "biotoolsCURIE": "biotools:bioplex",
+    "biotoolsID": "bioplex",
+    "collectionID": [
+        "Proteomics"
+    ],
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "wade_harper@hms.harvard.edu",
+            "name": "Wade Harper",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ]
+        },
+        {
+            "name": "Edward L Huttlin"
+        }
+    ],
+    "description": "A repository of protein interaction data from mass spectrometry experiments.",
+    "download": [
+        {
+            "note": "Link to download MS data",
+            "type": "Biological data",
+            "url": "http://bioplex.hms.harvard.edu/downloadData.php"
+        },
+        {
+            "note": "Link to download interactions data",
+            "type": "Biological data",
+            "url": "http://bioplex.hms.harvard.edu/downloadInteractions.php"
+        }
+    ],
+    "editPermission": {
+        "authors": [
+            "proteomics.bio.tools"
+        ],
+        "type": "group"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene identifier",
+                        "uri": "http://edamontology.org/data_1025"
+                    },
+                    "format": [
+                        {
+                            "term": "Textual format",
+                            "uri": "http://edamontology.org/format_2330"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Pathway or network visualisation",
+                    "uri": "http://edamontology.org/operation_3083"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Pathway or network",
+                        "uri": "http://edamontology.org/data_2600"
+                    },
+                    "format": [
+                        {
+                            "term": "Map format",
+                            "uri": "http://edamontology.org/format_2060"
+                        }
+                    ]
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bioplex.hms.harvard.edu/",
+    "lastUpdate": "2023-03-09T08:34:58.777202Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/ccb-hms/BioPlexAnalysis"
+        }
+    ],
+    "maturity": "Mature",
+    "name": "BioPlex",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "otherID": [
+        {
+            "type": "rrid",
+            "value": "RRID:SCR_016144"
+        }
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/j.cell.2015.06.043",
+            "metadata": {
+                "abstract": "Summary Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80%-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally related proteins. Finally, BioPlex, in combination with other approaches, can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial amyotrophic lateral sclerosis perturb a defined community of interactors.",
+                "authors": [
+                    {
+                        "name": "Artavanis-Tsakonas S."
+                    },
+                    {
+                        "name": "Baltier K."
+                    },
+                    {
+                        "name": "Bruckner R.J."
+                    },
+                    {
+                        "name": "Chick J."
+                    },
+                    {
+                        "name": "Colby G."
+                    },
+                    {
+                        "name": "De Camilli P."
+                    },
+                    {
+                        "name": "Dong R."
+                    },
+                    {
+                        "name": "Erickson B.K."
+                    },
+                    {
+                        "name": "Gebreab F."
+                    },
+                    {
+                        "name": "Guarani V."
+                    },
+                    {
+                        "name": "Gygi M.P."
+                    },
+                    {
+                        "name": "Gygi S.P."
+                    },
+                    {
+                        "name": "Harper J.W."
+                    },
+                    {
+                        "name": "Harris T."
+                    },
+                    {
+                        "name": "Huttlin E.L."
+                    },
+                    {
+                        "name": "Kolippakkam D."
+                    },
+                    {
+                        "name": "Mintseris J."
+                    },
+                    {
+                        "name": "Obar R.A."
+                    },
+                    {
+                        "name": "Ordureau A."
+                    },
+                    {
+                        "name": "Paulo J.A."
+                    },
+                    {
+                        "name": "Rad R."
+                    },
+                    {
+                        "name": "Sowa M.E."
+                    },
+                    {
+                        "name": "Szpyt J."
+                    },
+                    {
+                        "name": "Tam S."
+                    },
+                    {
+                        "name": "Ting L."
+                    },
+                    {
+                        "name": "Vaites L.P."
+                    },
+                    {
+                        "name": "Wuhr M."
+                    },
+                    {
+                        "name": "Zarraga G."
+                    },
+                    {
+                        "name": "Zhai B."
+                    }
+                ],
+                "citationCount": 864,
+                "date": "2015-07-18T00:00:00Z",
+                "journal": "Cell",
+                "title": "The BioPlex Network: A Systematic Exploration of the Human Interactome"
+            },
+            "pmcid": "PMC4617211",
+            "pmid": "26186194"
+        },
+        {
+            "doi": "10.1038/nature22366",
+            "metadata": {
+                "abstract": "The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidating how genome variation contributes to disease. Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification-mass spectrometry methodology to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein-coding genes from the human genome, and constitutes, to our knowledge, the largest such network so far. With more than 56,000 candidate interactions, BioPlex 2.0 contains more than 29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2,000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.",
+                "authors": [
+                    {
+                        "name": "Artavanis-Tsakonas S."
+                    },
+                    {
+                        "name": "Baltier K."
+                    },
+                    {
+                        "name": "Bruckner R.J."
+                    },
+                    {
+                        "name": "Cannon J.R."
+                    },
+                    {
+                        "name": "Colby G."
+                    },
+                    {
+                        "name": "Erickson B.K."
+                    },
+                    {
+                        "name": "Gebreab F."
+                    },
+                    {
+                        "name": "Guruharsha K.G."
+                    },
+                    {
+                        "name": "Gygi M.P."
+                    },
+                    {
+                        "name": "Gygi S.P."
+                    },
+                    {
+                        "name": "Huttlin E.L."
+                    },
+                    {
+                        "name": "Li K."
+                    },
+                    {
+                        "name": "Obar R.A."
+                    },
+                    {
+                        "name": "Parzen H."
+                    },
+                    {
+                        "name": "Paulo J.A."
+                    },
+                    {
+                        "name": "Pontano-Vaites L."
+                    },
+                    {
+                        "name": "Rad R."
+                    },
+                    {
+                        "name": "Schweppe D.K."
+                    },
+                    {
+                        "name": "Swarup S."
+                    },
+                    {
+                        "name": "Szpyt J."
+                    },
+                    {
+                        "name": "Tam S."
+                    },
+                    {
+                        "name": "Ting L."
+                    },
+                    {
+                        "name": "Wade Harper J."
+                    },
+                    {
+                        "name": "White A.E."
+                    },
+                    {
+                        "name": "Zarraga G."
+                    }
+                ],
+                "citationCount": 807,
+                "date": "2017-05-25T00:00:00Z",
+                "journal": "Nature",
+                "title": "Architecture of the human interactome defines protein communities and disease networks"
+            },
+            "pmcid": "PMC5531611",
+            "pmid": "28514442"
+        },
+        {
+            "doi": "10.1016/j.cell.2021.04.011",
+            "metadata": {
+                "abstract": "Thousands of interactions assemble proteins into modules that impart spatial and functional organization to the cellular proteome. Through affinity-purification mass spectrometry, we have created two proteome-scale, cell-line-specific interaction networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins—half the proteome—in 293T cells and includes 118,162 interactions among 14,586 proteins. The second results from 5,522 immunoprecipitations in HCT116 cells. These networks model the interactome whose structure encodes protein function, localization, and complex membership. Comparison across cell lines validates thousands of interactions and reveals extensive customization. Whereas shared interactions reside in core complexes and involve essential proteins, cell-specific interactions link these complexes, “rewiring” subnetworks within each cell's interactome. Interactions covary among proteins of shared function as the proteome remodels to produce each cell's phenotype. Viewable interactively online through BioPlexExplorer, these networks define principles of proteome organization and enable unknown protein characterization.",
+                "authors": [
+                    {
+                        "name": "Baltier K."
+                    },
+                    {
+                        "name": "Bruckner R.J."
+                    },
+                    {
+                        "name": "Cannon J.R."
+                    },
+                    {
+                        "name": "Fu S."
+                    },
+                    {
+                        "name": "Gassaway B.M."
+                    },
+                    {
+                        "name": "Gebreab F."
+                    },
+                    {
+                        "name": "Golbazi A."
+                    },
+                    {
+                        "name": "Guha Thakurta S."
+                    },
+                    {
+                        "name": "Gygi M.P."
+                    },
+                    {
+                        "name": "Gygi S.P."
+                    },
+                    {
+                        "name": "Harper J.W."
+                    },
+                    {
+                        "name": "Huttlin E.L."
+                    },
+                    {
+                        "name": "Maenpaa E."
+                    },
+                    {
+                        "name": "Navarrete-Perea J."
+                    },
+                    {
+                        "name": "Nusinow D.P."
+                    },
+                    {
+                        "name": "Pan J."
+                    },
+                    {
+                        "name": "Panov A."
+                    },
+                    {
+                        "name": "Parzen H."
+                    },
+                    {
+                        "name": "Paulo J.A."
+                    },
+                    {
+                        "name": "Rad R."
+                    },
+                    {
+                        "name": "Schweppe D.K."
+                    },
+                    {
+                        "name": "Stricker K."
+                    },
+                    {
+                        "name": "Szpyt J."
+                    },
+                    {
+                        "name": "Tam S."
+                    },
+                    {
+                        "name": "Thornock A."
+                    },
+                    {
+                        "name": "Vaites L.P."
+                    },
+                    {
+                        "name": "Zarraga G."
+                    },
+                    {
+                        "name": "Zhang T."
+                    }
+                ],
+                "citationCount": 144,
+                "date": "2021-05-27T00:00:00Z",
+                "journal": "Cell",
+                "title": "Dual proteome-scale networks reveal cell-specific remodeling of the human interactome"
+            },
+            "pmcid": "PMC8165030",
+            "pmid": "33961781",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "bioplex_r",
+            "type": "usedBy"
+        },
+        {
+            "biotoolsID": "bioplexpy",
+            "type": "usedBy"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Data integration and warehousing",
+            "uri": "http://edamontology.org/topic_3366"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        }
+    ],
+    "version": [
+        "3.0"
+    ]
+}
diff --git a/data/bioplex_2.0/bioplex_2.0.biotools.json b/data/bioplex_2.0/bioplex_2.0.biotools.json
deleted file mode 100644
index 00eaec982bf21..0000000000000
--- a/data/bioplex_2.0/bioplex_2.0.biotools.json
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-    "accessibility": "Open access",
-    "additionDate": "2018-06-04T16:14:35Z",
-    "biotoolsCURIE": "biotools:bioplex_2.0",
-    "biotoolsID": "bioplex_2.0",
-    "collectionID": [
-        "Proteomics"
-    ],
-    "cost": "Free of charge",
-    "credit": [
-        {
-            "email": "wade_harper@hms.harvard.edu",
-            "name": "Wade Harper",
-            "typeEntity": "Person",
-            "typeRole": [
-                "Primary contact"
-            ]
-        }
-    ],
-    "description": "A repository of protein interaction data from mass spectrometry experiments.",
-    "download": [
-        {
-            "note": "Link to download MS data",
-            "type": "Biological data",
-            "url": "http://bioplex.hms.harvard.edu/downloadData.php"
-        },
-        {
-            "note": "Link to download interactions data",
-            "type": "Biological data",
-            "url": "http://bioplex.hms.harvard.edu/downloadInteractions.php"
-        }
-    ],
-    "editPermission": {
-        "authors": [
-            "proteomics.bio.tools"
-        ],
-        "type": "group"
-    },
-    "function": [
-        {
-            "input": [
-                {
-                    "data": {
-                        "term": "Gene identifier",
-                        "uri": "http://edamontology.org/data_1025"
-                    },
-                    "format": [
-                        {
-                            "term": "Textual format",
-                            "uri": "http://edamontology.org/format_2330"
-                        }
-                    ]
-                }
-            ],
-            "operation": [
-                {
-                    "term": "Data retrieval",
-                    "uri": "http://edamontology.org/operation_2422"
-                },
-                {
-                    "term": "Database search",
-                    "uri": "http://edamontology.org/operation_2421"
-                },
-                {
-                    "term": "Pathway or network visualisation",
-                    "uri": "http://edamontology.org/operation_3083"
-                }
-            ],
-            "output": [
-                {
-                    "data": {
-                        "term": "Pathway or network",
-                        "uri": "http://edamontology.org/data_2600"
-                    },
-                    "format": [
-                        {
-                            "term": "Map format",
-                            "uri": "http://edamontology.org/format_2060"
-                        }
-                    ]
-                }
-            ]
-        }
-    ],
-    "homepage": "http://bioplex.hms.harvard.edu/",
-    "lastUpdate": "2021-04-15T18:36:43Z",
-    "maturity": "Mature",
-    "name": "BioPlex 2.0",
-    "operatingSystem": [
-        "Linux",
-        "Mac",
-        "Windows"
-    ],
-    "otherID": [
-        {
-            "type": "rrid",
-            "value": "RRID:SCR_016144"
-        }
-    ],
-    "owner": "ELIXIR-EE",
-    "publication": [
-        {
-            "doi": "10.1038/nature22366",
-            "metadata": {
-                "abstract": "© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidating how genome variation contributes to disease. Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification-mass spectrometry methodology to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein-coding genes from the human genome, and constitutes, to our knowledge, the largest such network so far. With more than 56,000 candidate interactions, BioPlex 2.0 contains more than 29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2,000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.",
-                "authors": [
-                    {
-                        "name": "Artavanis-Tsakonas S."
-                    },
-                    {
-                        "name": "Baltier K."
-                    },
-                    {
-                        "name": "Bruckner R.J."
-                    },
-                    {
-                        "name": "Cannon J.R."
-                    },
-                    {
-                        "name": "Colby G."
-                    },
-                    {
-                        "name": "Erickson B.K."
-                    },
-                    {
-                        "name": "Gebreab F."
-                    },
-                    {
-                        "name": "Guruharsha K.G."
-                    },
-                    {
-                        "name": "Gygi M.P."
-                    },
-                    {
-                        "name": "Gygi S.P."
-                    },
-                    {
-                        "name": "Huttlin E.L."
-                    },
-                    {
-                        "name": "Li K."
-                    },
-                    {
-                        "name": "Obar R.A."
-                    },
-                    {
-                        "name": "Parzen H."
-                    },
-                    {
-                        "name": "Paulo J.A."
-                    },
-                    {
-                        "name": "Pontano-Vaites L."
-                    },
-                    {
-                        "name": "Rad R."
-                    },
-                    {
-                        "name": "Schweppe D.K."
-                    },
-                    {
-                        "name": "Swarup S."
-                    },
-                    {
-                        "name": "Szpyt J."
-                    },
-                    {
-                        "name": "Tam S."
-                    },
-                    {
-                        "name": "Ting L."
-                    },
-                    {
-                        "name": "Wade Harper J."
-                    },
-                    {
-                        "name": "White A.E."
-                    },
-                    {
-                        "name": "Zarraga G."
-                    }
-                ],
-                "citationCount": 541,
-                "date": "2017-05-25T00:00:00Z",
-                "journal": "Nature",
-                "title": "Architecture of the human interactome defines protein communities and disease networks"
-            }
-        }
-    ],
-    "toolType": [
-        "Database portal"
-    ],
-    "topic": [
-        {
-            "term": "Data integration and warehousing",
-            "uri": "http://edamontology.org/topic_3366"
-        },
-        {
-            "term": "Protein interactions",
-            "uri": "http://edamontology.org/topic_0128"
-        },
-        {
-            "term": "Proteomics",
-            "uri": "http://edamontology.org/topic_0121"
-        },
-        {
-            "term": "Proteomics experiment",
-            "uri": "http://edamontology.org/topic_3520"
-        }
-    ],
-    "validated": 1
-}
diff --git a/data/bioplex_r/bioplex_r.biotools.json b/data/bioplex_r/bioplex_r.biotools.json
new file mode 100644
index 0000000000000..4a5184270f73f
--- /dev/null
+++ b/data/bioplex_r/bioplex_r.biotools.json
@@ -0,0 +1,86 @@
+{
+    "additionDate": "2023-03-09T08:26:48.275862Z",
+    "biotoolsCURIE": "biotools:bioplex_R",
+    "biotoolsID": "bioplex_R",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ludwig_geistlinger@hms.harvard.edu",
+            "name": "Ludwig Geistlinger",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "The BioPlex package implements access to the BioPlex protein-protein interaction networks and related resources from within R",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein structure analysis",
+                    "uri": "http://edamontology.org/operation_2406"
+                },
+                {
+                    "term": "Protein-protein interaction analysis",
+                    "uri": "http://edamontology.org/operation_2949"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bioconductor.org/packages/BioPlex",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-09T08:27:22.483521Z",
+    "license": "Artistic-2.0",
+    "name": "BioPlex",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD091",
+            "pmid": "36794911"
+        },
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD091",
+            "pmid": "36794911"
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "bioplex",
+            "type": "uses"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/bioplexpy/bioplexpy.biotools.json b/data/bioplexpy/bioplexpy.biotools.json
new file mode 100644
index 0000000000000..7b4508647c623
--- /dev/null
+++ b/data/bioplexpy/bioplexpy.biotools.json
@@ -0,0 +1,100 @@
+{
+    "additionDate": "2023-03-09T08:30:59.796446Z",
+    "biotoolsCURIE": "biotools:bioplexpy",
+    "biotoolsID": "bioplexpy",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ludwig_geistlinger@hms.harvard.edu",
+            "name": "Ludwig Geistlinger",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "The BioplexPy package provides access to PPI data from Gygi lab. It fetches protein interaction data and offers a simple and intuitive API for Analysis.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://bioplexpy.readthedocs.io/en/latest/"
+        },
+        {
+            "type": [
+                "Training material"
+            ],
+            "url": "https://github.com/ccb-hms/BioPlexPy/blob/main/docs/BioPlex_Examples.ipynb"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein structure analysis",
+                    "uri": "http://edamontology.org/operation_2406"
+                },
+                {
+                    "term": "Protein-protein interaction analysis",
+                    "uri": "http://edamontology.org/operation_2949"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pypi.org/project/bioplexpy/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-09T08:31:55.155386Z",
+    "license": "MIT",
+    "name": "BioPlexPy",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD091",
+            "pmid": "36794911"
+        },
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD091",
+            "pmid": "36794911"
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "bioplex",
+            "type": "uses"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/bioscitools/bioscitools.biotools.json b/data/bioscitools/bioscitools.biotools.json
new file mode 100644
index 0000000000000..44f99a02c6bf3
--- /dev/null
+++ b/data/bioscitools/bioscitools.biotools.json
@@ -0,0 +1,73 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-05T10:26:21.069202Z",
+    "biotoolsCURIE": "biotools:bioscitools",
+    "biotoolsID": "bioscitools",
+    "cost": "Free of charge",
+    "description": "①Software: BioSciTools (Bioinformatics Science Tools), a desktop program developed based on Java 11, aims to make new exploration and contribution to the development of bioinformatics, and realize data analysis and visualization in the fields of sequence analysis, multi-omics (genomics, transcriptomics, proteomics, metabomics, single cell), microbiology, clinical, etc. The program inherits the excellent interactive components, perfect analysis function and beautiful operation interface of HiPlot (https://hiplot.com.cn), which I developed based VUE.js and Node.js, etc.\n②Author: benben-miao (github: https://github.com/benben-miao), Doctor in Xiamen University.",
+    "download": [
+        {
+            "type": "Software package",
+            "url": "https://bioscitools.github.io",
+            "version": "1.3.4"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Alignment",
+                    "uri": "http://edamontology.org/operation_2928"
+                },
+                {
+                    "term": "Analysis",
+                    "uri": "http://edamontology.org/operation_2945"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://bioscitools.github.io",
+    "language": [
+        "Java",
+        "JavaScript",
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-03-05T10:35:50.669871Z",
+    "license": "MIT",
+    "maturity": "Emerging",
+    "name": "BioSciTools",
+    "operatingSystem": [
+        "Windows"
+    ],
+    "owner": "benben.miao",
+    "toolType": [
+        "Bioinformatics portal",
+        "Desktop application",
+        "Suite"
+    ],
+    "topic": [
+        {
+            "term": "Biology",
+            "uri": "http://edamontology.org/topic_3070"
+        },
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "Omics",
+            "uri": "http://edamontology.org/topic_3391"
+        }
+    ],
+    "version": [
+        "1.3.4"
+    ]
+}
diff --git a/data/biotoolspathogenfinder/biotoolspathogenfinder.biotools.json b/data/biotoolspathogenfinder/biotoolspathogenfinder.biotools.json
new file mode 100644
index 0000000000000..c51594b20f1f1
--- /dev/null
+++ b/data/biotoolspathogenfinder/biotoolspathogenfinder.biotools.json
@@ -0,0 +1,13 @@
+{
+    "additionDate": "2023-03-14T00:47:23.734276Z",
+    "biotoolsCURIE": "biotools:biotoolspathogenfinder",
+    "biotoolsID": "biotoolspathogenfinder",
+    "description": "biotools:pathogenfinder",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "http://cge.cbs.dtu.dk/services/PathogenFinder/",
+    "lastUpdate": "2023-03-14T00:47:23.743919Z",
+    "name": "biotools:pathogenfinder",
+    "owner": "ethanbogo"
+}
diff --git a/data/bipotentr/bipotentr.biotools.json b/data/bipotentr/bipotentr.biotools.json
new file mode 100644
index 0000000000000..68e6478083c1b
--- /dev/null
+++ b/data/bipotentr/bipotentr.biotools.json
@@ -0,0 +1,92 @@
+{
+    "additionDate": "2023-03-09T08:43:00.365638Z",
+    "biotoolsCURIE": "biotools:bipotentr",
+    "biotoolsID": "bipotentr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "KFLAHERTY@mgh.harvard.edu",
+            "name": "Keith T. Flaherty"
+        },
+        {
+            "name": "Avinash D. Sahu"
+        },
+        {
+            "name": "David E. Fisher"
+        }
+    ],
+    "description": "Discovery of Targets for Immune-Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bipotentr.dfci.harvard.edu/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-09T08:43:00.369666Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/vinash85/TRIM"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://zenodo.org/record/7043369"
+        }
+    ],
+    "name": "BipotentR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1158/2159-8290.CD-22-0244",
+            "pmid": "36745048"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/bips_phage/bips_phage.biotools.json b/data/bips_phage/bips_phage.biotools.json
new file mode 100644
index 0000000000000..998aebd92a341
--- /dev/null
+++ b/data/bips_phage/bips_phage.biotools.json
@@ -0,0 +1,104 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T15:06:12.202613Z",
+    "biotoolsCURIE": "biotools:bips_phage",
+    "biotoolsID": "bips_phage",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "eran.segal@weizmann.ac.il",
+            "name": "Eran Segal",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "BIPS (Build Phage ImmunoPrecipitation Sequencing library) is a software that converts a list of proteins into a custom DNA oligonucleotide library for the PhIP-Seq system. The tool creates constant-length oligonucleotides with internal barcodes, while maintaining the original length of the peptide.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Formatting",
+                    "uri": "http://edamontology.org/operation_0335"
+                },
+                {
+                    "term": "Primer and probe design",
+                    "uri": "http://edamontology.org/operation_2419"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/kalkairis/BuildPhIPSeqLibrary",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-26T15:06:12.205213Z",
+    "license": "GPL-3.0",
+    "name": "BIPS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PCBI.1010663",
+            "metadata": {
+                "abstract": "© 2022 Leviatan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.BIPS (Build Phage ImmunoPrecipitation Sequencing library) is a software that converts a list of proteins into a custom DNA oligonucleotide library for the PhIP-Seq system. The tool creates constant-length oligonucleotides with internal barcodes, while maintaining the original length of the peptide. This allows using large libraries, of hundreds of thousands of oligonucleotides, while saving on the costs of sequencing and maintaining the accuracy of oligonucleotide reads identification. BIPS is available under GNU public license from: https://github.com/kalkairis/BuildPhIPSeqLibrary.",
+                "authors": [
+                    {
+                        "name": "Kalka I.N."
+                    },
+                    {
+                        "name": "Klompas S."
+                    },
+                    {
+                        "name": "Leviatan S."
+                    },
+                    {
+                        "name": "Segal E."
+                    },
+                    {
+                        "name": "Vogl T."
+                    },
+                    {
+                        "name": "Weinberger A."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "BIPS - A code base for designing and coding of a Phage ImmunoPrecipitation Oligo Library"
+            },
+            "pmcid": "PMC9681064",
+            "pmid": "36355866"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Probes and primers",
+            "uri": "http://edamontology.org/topic_0632"
+        },
+        {
+            "term": "Protein interaction experiment",
+            "uri": "http://edamontology.org/topic_3957"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/blmm/blmm.biotools.json b/data/blmm/blmm.biotools.json
new file mode 100644
index 0000000000000..d9b0912f09d76
--- /dev/null
+++ b/data/blmm/blmm.biotools.json
@@ -0,0 +1,68 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T00:36:31.619263Z",
+    "biotoolsCURIE": "biotools:blmm",
+    "biotoolsID": "blmm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Thomas E. Nichols",
+            "orcidid": "http://orcid.org/0000-0002-4516-5103"
+        },
+        {
+            "name": "Thomas Maullin-Sapey",
+            "orcidid": "http://orcid.org/0000-0002-1890-330X"
+        }
+    ],
+    "description": "Parallelised Computing for Big Linear Mixed Models.\n\nWithin neuroimaging large-scale, shared datasets are becoming increasingly commonplace, challenging existing tools both in terms of overall scale and complexity of the study designs. As sample sizes grow, researchers are presented with new opportunities to detect and account for grouping factors and covariance structure present in large experimental designs. In particular, standard linear model methods cannot account for the covariance and grouping structures present in large datasets, and the existing linear mixed models (LMM) tools are neither scalable nor exploit the computational speed-ups afforded by vectorisation of computations over voxels. Further, nearly all existing tools for imaging (fixed or mixed effect) do not account for variability in the patterns of missing data near cortical boundaries and the edge of the brain, and instead omit any voxels with any missing data",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/TomMaullin/BLMM",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-17T00:36:31.621728Z",
+    "license": "Not licensed",
+    "name": "BLMM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.neuroimage.2022.119729",
+            "metadata": {
+                "abstract": "© 2022Within neuroimaging large-scale, shared datasets are becoming increasingly commonplace, challenging existing tools both in terms of overall scale and complexity of the study designs. As sample sizes grow, researchers are presented with new opportunities to detect and account for grouping factors and covariance structure present in large experimental designs. In particular, standard linear model methods cannot account for the covariance and grouping structures present in large datasets, and the existing linear mixed models (LMM) tools are neither scalable nor exploit the computational speed-ups afforded by vectorisation of computations over voxels. Further, nearly all existing tools for imaging (fixed or mixed effect) do not account for variability in the patterns of missing data near cortical boundaries and the edge of the brain, and instead omit any voxels with any missing data. Yet in the large-n setting, such a voxel-wise deletion missing data strategy leads to severe shrinkage of the final analysis mask. To counter these issues, we describe the “Big” Linear Mixed Models (BLMM) toolbox, an efficient Python package for large-scale fMRI LMM analyses. BLMM is designed for use on high performance computing clusters and utilizes a Fisher Scoring procedure made possible by derivations for the LMM Fisher information matrix and score vectors derived in our previous work, Maullin-Sapey and Nichols (2021).",
+                "authors": [
+                    {
+                        "name": "Maullin-Sapey T."
+                    },
+                    {
+                        "name": "Nichols T.E."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "NeuroImage",
+                "title": "BLMM: Parallelised computing for big linear mixed models"
+            },
+            "pmid": "36336314"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Experimental design and studies",
+            "uri": "http://edamontology.org/topic_3678"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        }
+    ]
+}
diff --git a/data/bloodnet/bloodnet.biotools.json b/data/bloodnet/bloodnet.biotools.json
new file mode 100644
index 0000000000000..68ef435aaae0a
--- /dev/null
+++ b/data/bloodnet/bloodnet.biotools.json
@@ -0,0 +1,134 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-12T15:21:34.671882Z",
+    "biotoolsCURIE": "biotools:bloodnet",
+    "biotoolsID": "bloodnet",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chunfeng.lian@xjtu.edu.cn",
+            "name": "Fan Wang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "fan.wang@xjtu.edu.cn",
+            "name": "Chunfeng Lian",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "wzy218@xjtu.edu.cn",
+            "name": "Zhenyuan Wang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/shenxiaochenn/BloodNet",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-12T15:21:34.674827Z",
+    "license": "MIT",
+    "link": [
+        {
+            "note": "Satasets and pre-trained models can be freely accessed via",
+            "type": [
+                "Other"
+            ],
+            "url": "https://figshare.com/articles/dataset/BloodNet_An_attention-based_deep_network_for_accurate_efficient_and_costless_bloodstain_time_since_deposition_inference/21291825"
+        }
+    ],
+    "name": "BloodNet",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC557",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.The time since deposition (TSD) of a bloodstain, i.e., the time of a bloodstain formation is an essential piece of biological evidence in crime scene investigation. The practical usage of some existing microscopic methods (e.g., spectroscopy or RNA analysis technology) is limited, as their performance strongly relies on high-end instrumentation and/or rigorous laboratory conditions. This paper presents a practically applicable deep learning-based method (i.e., BloodNet) for efficient, accurate, and costless TSD inference from a macroscopic view, i.e., by using easily accessible bloodstain photos. To this end, we established a benchmark database containing around 50,000 photos of bloodstains with varying TSDs. Capitalizing on such a large-scale database, BloodNet adopted attention mechanisms to learn from relatively high-resolution input images the localized fine-grained feature representations that were highly discriminative between different TSD periods. Also, the visual analysis of the learned deep networks based on the Smooth Grad-CAM tool demonstrated that our BloodNet can stably capture the unique local patterns of bloodstains with specific TSDs, suggesting the efficacy of the utilized attention mechanism in learning fine-grained representations for TSD inference. As a paired study for BloodNet, we further conducted a microscopic analysis using Raman spectroscopic data and a machine learning method based on Bayesian optimization. Although the experimental results show that such a new microscopic-level approach outperformed the state-of-the-art by a large margin, its inference accuracy is significantly lower than BloodNet, which further justifies the efficacy of deep learning techniques in the challenging task of bloodstain TSD inference. Our code is publically accessible via https://github.com/shenxiaochenn/BloodNet. Our datasets and pre-trained models can be freely accessed via https://figshare.com/articles/dataset/21291825.",
+                "authors": [
+                    {
+                        "name": "Chen R."
+                    },
+                    {
+                        "name": "Li H."
+                    },
+                    {
+                        "name": "Li Z."
+                    },
+                    {
+                        "name": "Lian C."
+                    },
+                    {
+                        "name": "Liang X."
+                    },
+                    {
+                        "name": "Shen C."
+                    },
+                    {
+                        "name": "Sun Q."
+                    },
+                    {
+                        "name": "Wang F."
+                    },
+                    {
+                        "name": "Wang G."
+                    },
+                    {
+                        "name": "Wang Z."
+                    },
+                    {
+                        "name": "Wu H."
+                    },
+                    {
+                        "name": "Yu K."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference"
+            },
+            "pmid": "36572655"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        }
+    ]
+}
diff --git a/data/bluelight/bluelight.biotools.json b/data/bluelight/bluelight.biotools.json
new file mode 100644
index 0000000000000..9317b3461f6ea
--- /dev/null
+++ b/data/bluelight/bluelight.biotools.json
@@ -0,0 +1,84 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-12T15:25:34.027904Z",
+    "biotoolsCURIE": "biotools:bluelight",
+    "biotoolsID": "bluelight",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chungyueh@ntunhs.edu.tw",
+            "name": "Chung-Yueh Lien",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Blue Light is a browser-based medical image viewer is primarily maintained by the Imaging Informatics Labs.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image annotation",
+                    "uri": "http://edamontology.org/operation_3553"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/cylab-tw/bluelight",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-02-12T15:25:34.030362Z",
+    "license": "MIT",
+    "name": "BlueLight",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1007/S10278-022-00746-0",
+            "metadata": {
+                "abstract": "© 2022, The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.Recently, WebGL has been widely used in numerous web-based medical image viewers to present advanced imaging visualization. However, in the scenario of medical imaging, there are many challenges of computation time and memory consumption that limit the use of advanced image renderings, such as volume rendering and multiplanar reformation/reconstruction, in low-cost mobile devices. In this study, we propose a client-side rendering low-cost computation algorithm for common two- and three-dimensional medical imaging visualization implemented by pure JavaScript. Particularly, we used the functions of cascading style sheet transform and combinate with Digital Imaging and Communications in Medicine (DICOM)-related imaging to replace the application programming interface with high computation to reduce the computation time and save memory consumption while launching medical imaging interpretation on web browsers. The results show the proposed algorithm significantly reduced the consumption of central and graphics processing units on various web browsers. The proposed algorithm was implemented in an open-source web-based DICOM viewer BlueLight; the results show that it has sufficient rendering performance to display 3D medical images with DICOM-compliant annotations and has the ability to connect to image archive via DICOMweb as well.Keywords: WebGL, DICOMweb, Multiplanar reconstruction, Volume rendering, DICOM, JavaScript, Zero-footprint.",
+                "authors": [
+                    {
+                        "name": "Chen T.-T."
+                    },
+                    {
+                        "name": "Chu W.-C."
+                    },
+                    {
+                        "name": "Lien C.-Y."
+                    },
+                    {
+                        "name": "Sun Y.-C."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Journal of Digital Imaging",
+                "title": "BlueLight: An Open Source DICOM Viewer Using Low-Cost Computation Algorithm Implemented with JavaScript Using Advanced Medical Imaging Visualization"
+            },
+            "pmid": "36538245"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Medical informatics",
+            "uri": "http://edamontology.org/topic_3063"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        }
+    ]
+}
diff --git a/data/bmat/bmat.biotools.json b/data/bmat/bmat.biotools.json
new file mode 100644
index 0000000000000..2f245ff2f9b07
--- /dev/null
+++ b/data/bmat/bmat.biotools.json
@@ -0,0 +1,110 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-12T15:30:14.330161Z",
+    "biotoolsCURIE": "biotools:bmat",
+    "biotoolsID": "bmat",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "colin.vandenbulcke@uclouvain.be",
+            "name": "Colin Vanden Bulcke",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "pietro.maggi@saintluc.uclouvain.be",
+            "name": "Pietro Maggi",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "The BMAT software is a complete and easy-to-use local open-source neuroimaging analysis tool with a graphical user interface (GUI) that uses the BIDS format to organize and process brain MRI data for MS imaging research studies.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ColinVDB/BMAT",
+    "lastUpdate": "2023-02-12T15:30:14.332883Z",
+    "license": "GPL-3.0",
+    "name": "BMAT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.NICL.2022.103252",
+            "metadata": {
+                "abstract": "© 2022 The Author(s)Magnetic Resonance Imaging (MRI) is an established technique to study in vivo neurological disorders such as Multiple Sclerosis (MS). To avoid errors on MRI data organization and automated processing, a standard called Brain Imaging Data Structure (BIDS) has been recently proposed. The BIDS standard eases data sharing and processing within or between centers by providing guidelines for their description and organization. However, the transformation from the complex unstructured non-open file data formats coming directly from the MRI scanner to a correct BIDS structure can be cumbersome and time consuming. This hinders a wider adoption of the BIDS format across different study centers. To solve this problem and ease the day-to-day use of BIDS for the neuroimaging scientific community, we present the BIDS Managing and Analysis Tool (BMAT). The BMAT software is a complete and easy-to-use local open-source neuroimaging analysis tool with a graphical user interface (GUI) that uses the BIDS format to organize and process brain MRI data for MS imaging research studies. BMAT provides the possibility to translate data from MRI scanners to the BIDS structure, create and manage BIDS datasets as well as develop and run automated processing pipelines, and is faster than its competitor. BMAT software propose the possibility to download useful analysis apps, especially applied to MS research with lesion segmentation and processing of imaging contrasts for novel disease biomarkers such as the central vein sign and the paramagnetic rim lesions.",
+                "authors": [
+                    {
+                        "name": "Absinta M."
+                    },
+                    {
+                        "name": "Bach Cuadra M."
+                    },
+                    {
+                        "name": "Detobel J."
+                    },
+                    {
+                        "name": "Dricot L."
+                    },
+                    {
+                        "name": "La Rosa F."
+                    },
+                    {
+                        "name": "Macq B."
+                    },
+                    {
+                        "name": "Maggi P."
+                    },
+                    {
+                        "name": "Vanden Bulcke C."
+                    },
+                    {
+                        "name": "Wynen M."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "NeuroImage: Clinical",
+                "title": "BMAT: An open-source BIDS managing and analysis tool"
+            },
+            "pmcid": "PMC9723304",
+            "pmid": "36451357"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Neurology",
+            "uri": "http://edamontology.org/topic_3334"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/bmvae/bmvae.biotools.json b/data/bmvae/bmvae.biotools.json
new file mode 100644
index 0000000000000..16ea634583bc0
--- /dev/null
+++ b/data/bmvae/bmvae.biotools.json
@@ -0,0 +1,99 @@
+{
+    "additionDate": "2023-02-12T15:33:15.619899Z",
+    "biotoolsCURIE": "biotools:bmvae",
+    "biotoolsID": "bmvae",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhyu@nxu.edu.cn",
+            "name": "Zhenhua Yu",
+            "orcidid": "https://orcid.org/0000-0001-6526-6991",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A variational autoencoder method for clustering single-cell mutation data.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/zhyu-lab/bmvae",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-12T15:33:15.622454Z",
+    "license": "GPL-3.0",
+    "name": "bmVAE",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC790",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Genetic intra-tumor heterogeneity (ITH) characterizes the differences in genomic variations between tumor clones, and accurately unmasking ITH is important for personalized cancer therapy. Single-cell DNA sequencing now emerges as a powerful means for deciphering underlying ITH based on point mutations of single cells. However, detecting tumor clones from single-cell mutation data remains challenging due to the error-prone and discrete nature of the data. RESULTS: We introduce bmVAE, a bioinformatics tool for learning low-dimensional latent representation of single cell based on a variational autoencoder and then clustering cells into subpopulations in the latent space. bmVAE takes single-cell binary mutation data as inputs, and outputs inferred cell subpopulations as well as their genotypes. To achieve this, the bmVAE framework is designed to consist of three modules including dimensionality reduction, cell clustering and genotype estimation. We assess the method on various synthetic datasets where different factors including false negative rate, data size and data heterogeneity are considered in simulation, and further demonstrate its effectiveness on two real datasets. The results suggest bmVAE is highly effective in reasoning ITH, and performs competitive to existing methods. AVAILABILITY AND IMPLEMENTATION: bmVAE is freely available at https://github.com/zhyu-lab/bmvae. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Ma M."
+                    },
+                    {
+                        "name": "Yan J."
+                    },
+                    {
+                        "name": "Yu Z."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "bmVAE: a variational autoencoder method for clustering single-cell mutation data"
+            },
+            "pmcid": "PMC9825778",
+            "pmid": "36478203"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Phylogeny",
+            "uri": "http://edamontology.org/topic_0084"
+        }
+    ]
+}
diff --git a/data/boss_data_virtulization/boss_data_virtulization.biotools.json b/data/boss_data_virtulization/boss_data_virtulization.biotools.json
new file mode 100644
index 0000000000000..43dc4445bb286
--- /dev/null
+++ b/data/boss_data_virtulization/boss_data_virtulization.biotools.json
@@ -0,0 +1,61 @@
+{
+    "additionDate": "2023-01-26T13:25:21.295298Z",
+    "biotoolsCURIE": "biotools:boss_data_virtulization",
+    "biotoolsID": "boss_data_virtulization",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "Red Had Developer",
+            "typeEntity": "Consortium",
+            "url": "https://developers.redhat.com/products/datavirt/overview"
+        }
+    ],
+    "description": "JBoss Data Virtualization is a data integration solution that sits in front of multiple data sources and allows them to be treated as a single source, delivering the right data, in the required form, at the right time to any application and/or user.",
+    "documentation": [
+        {
+            "type": [
+                "API documentation",
+                "Installation instructions"
+            ],
+            "url": "https://developers.redhat.com/products/datavirt/docs-and-apis"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://developers.redhat.com/products/datavirt/download",
+            "version": "6.4.0"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://developers.redhat.com/products/datavirt/overview",
+    "lastUpdate": "2023-02-01T13:17:59.324740Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Service"
+            ],
+            "url": "https://developers.redhat.com/products/datavirt/overview"
+        }
+    ],
+    "name": "JBoss Data Virtulization",
+    "owner": "iacs-biocomputacion",
+    "version": [
+        "6.4.0"
+    ]
+}
diff --git a/data/bp/bp.biotools.json b/data/bp/bp.biotools.json
new file mode 100644
index 0000000000000..b89431aec74ab
--- /dev/null
+++ b/data/bp/bp.biotools.json
@@ -0,0 +1,103 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-20T02:37:29.648861Z",
+    "biotoolsCURIE": "biotools:bp",
+    "biotoolsID": "bp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "irinag@stat.tamu.edu",
+            "name": "Irina Gaynanova",
+            "orcidid": "http://orcid.org/0000-0002-4116-0268",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Naresh M. Punjabi"
+        },
+        {
+            "name": "John Schwenck",
+            "orcidid": "http://orcid.org/0000-0003-0821-9333"
+        }
+    ],
+    "description": "Blood Pressure Analysis in R.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://cran.r-project.org/web/packages/bp/bp.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/johnschwenck/bp",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-20T02:37:29.651454Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://cran.r-project.org/web/packages/bp/index.html"
+        }
+    ],
+    "name": "bp",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/journal.pone.0268934",
+            "metadata": {
+                "abstract": "© 2022 Schwenck et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Despite the world-wide prevalence of hypertension, there is a lack in open-source software for analyzing blood pressure data. The R package bp fills this gap by providing functionality for blood pressure data processing, visualization, and feature extraction. In addition to the comprehensive functionality, the package includes six sample data sets covering continuous arterial pressure data (AP), home blood pressure monitoring data (HBPM) and ambulatory blood pressure monitoring data (ABPM), making it easier for researchers to get started.",
+                "authors": [
+                    {
+                        "name": "Gaynanova I."
+                    },
+                    {
+                        "name": "Punjabi N.M."
+                    },
+                    {
+                        "name": "Schwenck J."
+                    }
+                ],
+                "date": "2022-09-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "bp: Blood pressure analysis in R"
+            },
+            "pmcid": "PMC9462781",
+            "pmid": "36083882"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Haematology",
+            "uri": "http://edamontology.org/topic_3408"
+        }
+    ]
+}
diff --git a/data/bphunter/bphunter.biotools.json b/data/bphunter/bphunter.biotools.json
new file mode 100644
index 0000000000000..5eb9a405f30f7
--- /dev/null
+++ b/data/bphunter/bphunter.biotools.json
@@ -0,0 +1,153 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-18T22:21:50.061492Z",
+    "biotoolsCURIE": "biotools:bphunter",
+    "biotoolsID": "bphunter",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "pzhang@rockefeller.edu",
+            "name": "Peng Zhang",
+            "orcidid": "http://orcid.org/0000-0002-6129-567X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jean-Laurent Casanova"
+        },
+        {
+            "name": "Laurent Abel"
+        },
+        {
+            "name": "Quentin Philippot"
+        }
+    ],
+    "description": "Genome-wide detection of human variants that disrupt intronic branchpoints.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein feature detection",
+                    "uri": "http://edamontology.org/operation_3092"
+                },
+                {
+                    "term": "Sequence motif recognition",
+                    "uri": "http://edamontology.org/operation_0239"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/casanova-lab/BPHunter",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-18T22:21:50.064032Z",
+    "license": "CC-BY-NC-ND-4.0",
+    "name": "BPHunter",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1073/pnas.2211194119",
+            "metadata": {
+                "abstract": "Copyright © 2022 the Author(s). Published by PNAS.Pre-messenger RNA splicing is initiated with the recognition of a single-nucleotide intronic branchpoint (BP) within a BP motif by spliceosome elements. Forty-eight rare variants in 43 human genes have been reported to alter splicing and cause disease by disrupting BP. However, until now, no computational approach was available to efficiently detect such variants in massively parallel sequencing data. We established a comprehensive human genome-wide BP database by integrating existing BP data and generating new BP data from RNA sequencing of lariat debranching enzyme DBR1-mutated patients and from machine-learning predictions. We characterized multiple features of BP in major and minor introns and found that BP and BP-2 (two nucleotides upstream of BP) positions exhibit a lower rate of variation in human populations and higher evolutionary conservation than the intronic background, while being comparable to the exonic background. We developed BPHunter as a genome-wide computational approach to systematically and efficiently detect intronic variants that may disrupt BP recognition. BPHunter retrospectively identified 40 of the 48 known pathogenic BP variants, in which we summarized a strategy for prioritizing BP variant candidates. The remaining eight variants all create AG-dinucleotides between the BP and acceptor site, which is the likely reason for missplicing. We demonstrated the practical utility of BPHunter prospectively by using it to identify a novel germline heterozygous BP variant of STAT2 in a patient with critical COVID-19 pneumonia and a novel somatic intronic 59-nucleotide deletion of ITPKB in a lymphoma patient, both of which were validated experimentally. BPHunter is publicly available from https://hgidsoft.rockefeller.edu/BPHunter and https://github.com/casanova-lab/BPHunter.",
+                "authors": [
+                    {
+                        "name": "Abel L."
+                    },
+                    {
+                        "name": "Boisson B."
+                    },
+                    {
+                        "name": "Casanova J.-L."
+                    },
+                    {
+                        "name": "Colobran R."
+                    },
+                    {
+                        "name": "Cooper D.N."
+                    },
+                    {
+                        "name": "Lei W.-T."
+                    },
+                    {
+                        "name": "Li J."
+                    },
+                    {
+                        "name": "Palacin P.S."
+                    },
+                    {
+                        "name": "Pan-Hammarstrom Q."
+                    },
+                    {
+                        "name": "Philippot Q."
+                    },
+                    {
+                        "name": "Puel A."
+                    },
+                    {
+                        "name": "Ren W."
+                    },
+                    {
+                        "name": "Stenson P.D."
+                    },
+                    {
+                        "name": "Zhang P."
+                    },
+                    {
+                        "name": "Zhang Q."
+                    },
+                    {
+                        "name": "Zhang S.-Y."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Proceedings of the National Academy of Sciences of the United States of America",
+                "title": "Genome-wide detection of human variants that disrupt intronic branchpoints"
+            },
+            "pmcid": "PMC9636908",
+            "pmid": "36306325"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/braingb/braingb.biotools.json b/data/braingb/braingb.biotools.json
new file mode 100644
index 0000000000000..54554bc787033
--- /dev/null
+++ b/data/braingb/braingb.biotools.json
@@ -0,0 +1,113 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-11T07:24:09.350495Z",
+    "biotoolsCURIE": "biotools:braingb",
+    "biotoolsID": "braingb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Hejie Cui",
+            "orcidid": "https://orcid.org/0000-0001-6388-2619"
+        }
+    ],
+    "description": "BrainGB is a unified, modular, scalable, and reproducible framework established for brain network analysis with GNNs.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://braingb.us",
+    "language": [
+        "MATLAB",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-11T07:24:09.353925Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/HennyJie/BrainGB"
+        }
+    ],
+    "name": "BrainGB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1109/TMI.2022.3218745",
+            "metadata": {
+                "abstract": "IEEEMapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by (1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and (2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we host the BrainGB website at https://braingb.us with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.",
+                "authors": [
+                    {
+                        "name": "Cui H."
+                    },
+                    {
+                        "name": "Dai W."
+                    },
+                    {
+                        "name": "Gu A.A.C."
+                    },
+                    {
+                        "name": "Guo Y."
+                    },
+                    {
+                        "name": "He L."
+                    },
+                    {
+                        "name": "Kan X."
+                    },
+                    {
+                        "name": "Lukemire J."
+                    },
+                    {
+                        "name": "Yang C."
+                    },
+                    {
+                        "name": "Zhan L."
+                    },
+                    {
+                        "name": "Zhu Y."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "IEEE Transactions on Medical Imaging",
+                "title": "BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks"
+            },
+            "pmid": "36318557"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Experimental design and studies",
+            "uri": "http://edamontology.org/topic_3678"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        }
+    ]
+}
diff --git a/data/brainstorm/brainstorm.biotools.json b/data/brainstorm/brainstorm.biotools.json
new file mode 100644
index 0000000000000..68a194da1f877
--- /dev/null
+++ b/data/brainstorm/brainstorm.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-23T15:59:58.699094Z",
+    "biotoolsCURIE": "biotools:brainstorm",
+    "biotoolsID": "brainstorm",
+    "cost": "Free of charge",
+    "description": "Brainstorm is a collaborative, open-source application dedicated to the analysis of brain recordings:\nMEG, EEG, fNIRS, ECoG, depth electrodes and multiunit electrophysiology. Our objective is to share a comprehensive set of user-friendly tools with the scientific community using MEG/EEG as an experimental technique. For physicians and researchers, the main advantage of Brainstorm is its rich and intuitive graphic interface, which does not require any programming knowledge. We are also putting the emphasis on practical aspects of data analysis (e.g., with scripting for batch analysis and intuitive design of analysis pipelines) to promote reproducibility and productivity in MEG/EEG research. Finally, although Brainstorm is developed with Matlab (and Java), it does not require users to own a Matlab license: an executable, platform-independent (Windows, MacOS, Linux) version is made available in the downloadable package.",
+    "documentation": [
+        {
+            "type": [
+                "Citation instructions"
+            ],
+            "url": "https://neuroimage.usc.edu/brainstorm/#How_to_cite_Brainstorm"
+        },
+        {
+            "type": [
+                "Training material"
+            ],
+            "url": "https://neuroimage.usc.edu/brainstorm/Tutorials"
+        }
+    ],
+    "download": [
+        {
+            "note": "You have to be a registered user in order to download Brainstorm",
+            "type": "Downloads page",
+            "url": "https://neuroimage.usc.edu/bst/download.php"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://neuroimage.usc.edu/brainstorm",
+    "language": [
+        "Java",
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-01-23T15:59:58.701649Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Discussion forum"
+            ],
+            "url": "https://neuroimage.usc.edu/forums/"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/brainstorm-tools/brainstorm3"
+        },
+        {
+            "type": [
+                "Social media"
+            ],
+            "url": "https://twitter.com/brainstorm2day"
+        },
+        {
+            "type": [
+                "Social media"
+            ],
+            "url": "https://www.facebook.com/BrainstormSoftware/"
+        }
+    ],
+    "maturity": "Mature",
+    "name": "Brainstorm",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "rcassani",
+    "publication": [
+        {
+            "doi": "10.1155/2011/879716",
+            "metadata": {
+                "abstract": "Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI). © 2011 Franois Tadel et al.",
+                "authors": [
+                    {
+                        "name": "Baillet S."
+                    },
+                    {
+                        "name": "Leahy R.M."
+                    },
+                    {
+                        "name": "Mosher J.C."
+                    },
+                    {
+                        "name": "Pantazis D."
+                    },
+                    {
+                        "name": "Tadel F."
+                    }
+                ],
+                "citationCount": 1824,
+                "date": "2011-06-22T00:00:00Z",
+                "journal": "Computational Intelligence and Neuroscience",
+                "title": "Brainstorm: A user-friendly application for MEG/EEG analysis"
+            },
+            "pmcid": "PMC3090754",
+            "pmid": "21584256",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Desktop application"
+    ]
+}
diff --git a/data/branemf/branemf.biotools.json b/data/branemf/branemf.biotools.json
new file mode 100644
index 0000000000000..433658c1a3e07
--- /dev/null
+++ b/data/branemf/branemf.biotools.json
@@ -0,0 +1,80 @@
+{
+    "additionDate": "2023-01-26T15:17:31.179933Z",
+    "biotoolsCURIE": "biotools:branemf",
+    "biotoolsID": "branemf",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "fragkiskos.malliaros@centralesupelec.fr",
+            "name": "Fragkiskos D Malliaros",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "BraneMF: Integration of Biological Networks for Functional Analysis of Proteins",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Protein function prediction",
+                    "uri": "http://edamontology.org/operation_1777"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Surabhivj/BraneMF",
+    "language": [
+        "MATLAB",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-28T23:46:52.913214Z",
+    "license": "Unlicense",
+    "name": "BraneMF",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC691",
+            "note": "The cellular system of a living organism is composed of interacting bio-molecules that control cellular processes at multiple levels. Their correspondences are represented by tightly regulated molecular networks. The increase of omics technologies has favored the generation of large-scale disparate data and the consequent demand for simultaneously using molecular and functional interaction networks: gene co-expression, protein–protein interaction (PPI), genetic interaction and metabolic networks. They are rich sources of information at different molecular levels, and their effective integration is essential to understand cell functioning and their building blocks (proteins). \n\nWe test BraneMF with PPI networks of Saccharomyces cerevisiae, a well-studied yeast model organism. We demonstrate the applicability of the learned features for essential multi-omics inference tasks: clustering, function and PPI prediction",
+            "pmid": "36321881",
+            "type": [
+                "Benchmarking study"
+            ]
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Proteins",
+            "uri": "http://edamontology.org/topic_0078"
+        }
+    ]
+}
diff --git a/data/bridgedb/bridgedb.biotools.json b/data/bridgedb/bridgedb.biotools.json
index 69aa4cf641252..ba0ff57ef2b29 100644
--- a/data/bridgedb/bridgedb.biotools.json
+++ b/data/bridgedb/bridgedb.biotools.json
@@ -152,11 +152,11 @@
             ]
         }
     ],
-    "homepage": "http://www.bridgedb.org/",
+    "homepage": "http://bridgedb.github.io/",
     "language": [
         "Java"
     ],
-    "lastUpdate": "2022-04-20T05:51:55.558045Z",
+    "lastUpdate": "2023-01-12T10:40:58.210403Z",
     "license": "Apache-2.0",
     "maturity": "Mature",
     "name": "BridgeDb",
@@ -170,7 +170,13 @@
         {
             "doi": "10.1007/978-3-319-11964-9_7",
             "type": [
-                "Other"
+                "Primary"
+            ]
+        },
+        {
+            "doi": "10.3897/rio.8.e83031",
+            "type": [
+                "Primary"
             ]
         },
         {
@@ -202,7 +208,7 @@
                         "name": "van Iersel M.P."
                     }
                 ],
-                "citationCount": 105,
+                "citationCount": 113,
                 "date": "2010-01-04T00:00:00Z",
                 "journal": "BMC Bioinformatics",
                 "title": "The BridgeDb framework: Standardized access to gene, protein and metabolite identifier mapping services"
@@ -236,6 +242,6 @@
     ],
     "validated": 1,
     "version": [
-        "3.0.13"
+        "3.0.18"
     ]
 }
diff --git a/data/bsatos/bsatos.biotools.json b/data/bsatos/bsatos.biotools.json
new file mode 100644
index 0000000000000..c4f1ec6df07e3
--- /dev/null
+++ b/data/bsatos/bsatos.biotools.json
@@ -0,0 +1,134 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-12T15:38:45.371953Z",
+    "biotoolsCURIE": "biotools:bsatos",
+    "biotoolsID": "bsatos",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "paolo.fontana@fmach.it",
+            "name": "Paolo Fontana",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "rschan@cau.edu.cn",
+            "name": "Zhenhai Han",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhangxinzhong999@126.com",
+            "name": "Xinzhong Zhang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A bulked segregant analysis tool for out-crossing species (BSATOS) and QTL-based genomics-assisted prediction of complex traits in apple.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genetic mapping",
+                    "uri": "http://edamontology.org/operation_0282"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "PCR primer design",
+                    "uri": "http://edamontology.org/operation_0308"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/maypoleflyn/BSATOS",
+    "language": [
+        "Perl"
+    ],
+    "lastUpdate": "2023-02-12T15:38:45.374486Z",
+    "license": "MIT",
+    "name": "BSATOS",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.JARE.2022.03.013",
+            "metadata": {
+                "abstract": "© 2022Introduction: Genomic heterozygosity, self-incompatibility, and rich-in somatic mutations hinder the molecular breeding efficiency of outcrossing plants. Objectives: We attempted to develop an efficient integrated strategy to identify quantitative trait loci (QTLs) and trait-associated genes, to develop gene markers, and to construct genomics-assisted prediction (GAP) modes. Methods: A novel protocol, bulked segregant analysis tool for out-crossing species (BSATOS), is presented here, which is characterized by taking full advantage of all segregation patterns (including AB × AB markers) and haplotype information. To verify the effectiveness of the protocol in dealing with the complex traits of outbreeding species, three apple cross populations with 9,654 individuals were adopted. Results: By using BSATOS, 90, 60, and 77 significant QTLs were identified successfully and candidate genes were predicted for apple fruit weight (FW), fruit ripening date (FRD), and fruit soluble solid content (SSC), respectively. The gene-based markers were developed and genotyped for 1,396 individuals in a training population, including 145 Malus accessions and 1,251 F1 plants of the three full-sib families. GAP models were trained using marker genotype effect estimates of the training population. The prediction accuracy was 0.7658, 0.6455, and 0.3758 for FW, FRD, and SSC, respectively. Conclusion: The BSATOS and GAP models provided a convenient and efficient methodology for candidate gene mining and molecular breeding in out-crossing plant species. The BSATOS pipeline can be freely downloaded from: https://github.com/maypoleflyn/BSATOS.",
+                "authors": [
+                    {
+                        "name": "Bianco L."
+                    },
+                    {
+                        "name": "Fontana P."
+                    },
+                    {
+                        "name": "Han Z."
+                    },
+                    {
+                        "name": "Shen F."
+                    },
+                    {
+                        "name": "Tian Z."
+                    },
+                    {
+                        "name": "Velasco R."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Wu B."
+                    },
+                    {
+                        "name": "Wu T."
+                    },
+                    {
+                        "name": "Xu X."
+                    },
+                    {
+                        "name": "Zhang X."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Journal of Advanced Research",
+                "title": "A bulked segregant analysis tool for out-crossing species (BSATOS) and QTL-based genomics-assisted prediction of complex traits in apple"
+            },
+            "pmcid": "PMC9788957",
+            "pmid": "36513410"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        }
+    ]
+}
diff --git a/data/bus_set/bus_set.biotools.json b/data/bus_set/bus_set.biotools.json
new file mode 100644
index 0000000000000..96de400b66aa4
--- /dev/null
+++ b/data/bus_set/bus_set.biotools.json
@@ -0,0 +1,48 @@
+{
+    "additionDate": "2023-03-09T13:42:04.047913Z",
+    "biotoolsCURIE": "biotools:bus_set",
+    "biotoolsID": "bus_set",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Reyer Zwiggelaar"
+        }
+    ],
+    "description": "A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/corcor27/BUS-Set",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-09T13:42:04.052584Z",
+    "license": "Not licensed",
+    "name": "BUS-Set",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1002/MP.16287",
+            "pmid": "36794706"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Echography",
+            "uri": "http://edamontology.org/topic_3954"
+        }
+    ]
+}
diff --git a/data/bv-brc/bv-brc.biotools.json b/data/bv-brc/bv-brc.biotools.json
new file mode 100644
index 0000000000000..d3904503ea4a3
--- /dev/null
+++ b/data/bv-brc/bv-brc.biotools.json
@@ -0,0 +1,239 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T15:29:45.571587Z",
+    "biotoolsCURIE": "biotools:bv-brc",
+    "biotoolsID": "bv-brc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jjdavis@anl.gov",
+            "name": "James J Davis",
+            "orcidid": "https://orcid.org/0000-0003-0104-5852",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Bacterial and Viral Bioinformatics Resource Center (BV-BRC). A resource combining PATRIC, IRD and ViPRA with a suite of command line tools and a web application.",
+    "documentation": [
+        {
+            "type": [
+                "API documentation"
+            ],
+            "url": "https://www.bv-brc.org/api/doc/"
+        },
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://www.bv-brc.org/docs/"
+        }
+    ],
+    "download": [
+        {
+            "type": "Software package",
+            "url": "https://github.com/BV-BRC/BV-BRC-CLI/releases"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Service discovery",
+                    "uri": "http://edamontology.org/operation_3761"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.bv-brc.org/",
+    "language": [
+        "JavaScript",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-26T15:29:45.573958Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/BV-BRC"
+        }
+    ],
+    "name": "BV-BRC",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1003",
+            "metadata": {
+                "abstract": "Published by Oxford University Press on behalf of Nucleic Acids Research 2022.The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Center (BRC) program to assist researchers with analyzing the growing body of genome sequence and other omics-related data. In this report, we describe the merger of the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD) and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) https://www.bv-brc.org/. The combined BV-BRC leverages the functionality of the bacterial and viral resources to provide a unified data model, enhanced web-based visualization and analysis tools, bioinformatics services, and a powerful suite of command line tools that benefit the bacterial and viral research communities.",
+                "authors": [
+                    {
+                        "name": "Assaf R."
+                    },
+                    {
+                        "name": "Brettin T."
+                    },
+                    {
+                        "name": "Conrad N."
+                    },
+                    {
+                        "name": "Cucinell C."
+                    },
+                    {
+                        "name": "Davis J.J."
+                    },
+                    {
+                        "name": "Dempsey D.M."
+                    },
+                    {
+                        "name": "Dickerman A."
+                    },
+                    {
+                        "name": "Dietrich E.M."
+                    },
+                    {
+                        "name": "Kenyon R.W."
+                    },
+                    {
+                        "name": "Kuscuoglu M."
+                    },
+                    {
+                        "name": "Lefkowitz E.J."
+                    },
+                    {
+                        "name": "Lu J."
+                    },
+                    {
+                        "name": "Machi D."
+                    },
+                    {
+                        "name": "Macken C."
+                    },
+                    {
+                        "name": "Mao C."
+                    },
+                    {
+                        "name": "Nguyen M."
+                    },
+                    {
+                        "name": "Niewiadomska A."
+                    },
+                    {
+                        "name": "Olsen G.J."
+                    },
+                    {
+                        "name": "Olson R.D."
+                    },
+                    {
+                        "name": "Overbeek J.C."
+                    },
+                    {
+                        "name": "Parrello B."
+                    },
+                    {
+                        "name": "Parrello V."
+                    },
+                    {
+                        "name": "Porter J.S."
+                    },
+                    {
+                        "name": "Pusch G.D."
+                    },
+                    {
+                        "name": "Scheuermann R.H."
+                    },
+                    {
+                        "name": "Shukla M."
+                    },
+                    {
+                        "name": "Singh I."
+                    },
+                    {
+                        "name": "Stevens R.L."
+                    },
+                    {
+                        "name": "Stewart L."
+                    },
+                    {
+                        "name": "Tan G."
+                    },
+                    {
+                        "name": "Thomas C."
+                    },
+                    {
+                        "name": "VanOeffelen M."
+                    },
+                    {
+                        "name": "Vonstein V."
+                    },
+                    {
+                        "name": "Wallace Z.S."
+                    },
+                    {
+                        "name": "Warren A.S."
+                    },
+                    {
+                        "name": "Wattam A.R."
+                    },
+                    {
+                        "name": "Xia F."
+                    },
+                    {
+                        "name": "Yoo H."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zmasek C.M."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR"
+            },
+            "pmcid": "PMC9825582",
+            "pmid": "36350631"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Database portal",
+        "Suite",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Allergy, clinical immunology and immunotherapeutics",
+            "uri": "http://edamontology.org/topic_3400"
+        },
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        }
+    ]
+}
diff --git a/data/bx2s-net/bx2s-net.biotools.json b/data/bx2s-net/bx2s-net.biotools.json
new file mode 100644
index 0000000000000..bc85b90abe4d4
--- /dev/null
+++ b/data/bx2s-net/bx2s-net.biotools.json
@@ -0,0 +1,86 @@
+{
+    "additionDate": "2023-03-09T13:46:09.540133Z",
+    "biotoolsCURIE": "biotools:bx2s-net",
+    "biotoolsID": "bx2s-net",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Jianhua Wang"
+        }
+    ],
+    "description": "Learning to reconstruct 3D spinal structures from bi-planar X-ray images.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/NBU-CVMI/bx2s-net",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-09T13:46:09.544184Z",
+    "license": "Not licensed",
+    "name": "BX2S-Net",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2023.106615",
+            "metadata": {
+                "abstract": "Grasping good understanding of the weight-bearing spatial structure of the spine of a human subject in a standing position is critical for the treatment of spinal disorders. Such disorders are commonly diagnosed via 2D X-ray imaging of the human subject in a standing position. However, 3D reconstruction techniques based on bi-planar X-ray imaging can enable better exploration and analysis of the spinal structure. In particular, compared to earlier deformable modeling approaches, the recently-developed deep-learning-based 3D reconstruction methods exhibit higher efficiency and generalizability. But these methods usually employ simple architectures with 2D encoders and 3D decoders. Thus, these methods have several drawbacks, namely, the existence of a semantic gap between dimensionally-inconsistent feature maps, the difficulty of jointly handling multi-view inputs, and the information source limitations for the decoding process. In order to better assist clinicians and tackle these problems, we propose a novel convolutional neural network framework, which we call BX2S-Net, to effectively achieve 3D spine reconstruction based on bi-planar X-ray images. In particular, a dimensionally-consistent encoder–decoder architecture is designed in conjunction with a dimensionality enhancement method in order to reduce the semantic gap between feature maps and achieve information fusion for multi-view inputs. A feature-guided progressive decoding process is developed on the decoder side, where a full-scale feature attention guidance (FFAG) module is introduced to efficiently aggregate image features and guide the decoding process at each level. In addition, a class augmentation method and a spatially-weighted cross-entropy loss function are used for network training with improved reconstruction quality for the vertebral edge region. The experimental results demonstrate the effectiveness of our model in reconstructing high-quality 3D spinal structures from bi-planar X-ray images. The code is available at https://github.com/NBU-CVMI/bx2s-net.",
+                "authors": [
+                    {
+                        "name": "Chen Z."
+                    },
+                    {
+                        "name": "Fang Z."
+                    },
+                    {
+                        "name": "Guo L."
+                    },
+                    {
+                        "name": "He X."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Zhang R."
+                    }
+                ],
+                "date": "2023-03-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "BX2S-Net: Learning to reconstruct 3D spinal structures from bi-planar X-ray images"
+            },
+            "pmid": "36739821"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        }
+    ]
+}
diff --git a/data/c_gwas/c_gwas.biotools.json b/data/c_gwas/c_gwas.biotools.json
new file mode 100644
index 0000000000000..ee654f9e85f78
--- /dev/null
+++ b/data/c_gwas/c_gwas.biotools.json
@@ -0,0 +1,140 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-12T15:42:32.671000Z",
+    "biotoolsCURIE": "biotools:c_gwas",
+    "biotoolsID": "c_gwas",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "f.liu@erasmusmc.nl",
+            "name": "Fan Liu",
+            "orcidid": "https://orcid.org/0000-0001-9241-8161",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "m.kayser@erasmusmc.nl",
+            "name": "Manfred Kayser",
+            "orcidid": "https://orcid.org/0000-0002-4958-847X",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "C-GWAS is a powerful method for combining GWAS summary statistics of multiple potentially related traits and detect SNPs with multi-trait effects.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Collapsing methods",
+                    "uri": "http://edamontology.org/operation_3791"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "SNP detection",
+                    "uri": "http://edamontology.org/operation_0484"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Fun-Gene/CGWAS",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-12T15:42:32.673527Z",
+    "license": "Not licensed",
+    "name": "C-GWAS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41467-022-35328-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Standard genome-wide association studies (GWASs) rely on analyzing a single trait at a time. However, many human phenotypes are complex and composed by multiple correlated traits. Here we introduce C-GWAS, a method for combining GWAS summary statistics of multiple potentially correlated traits. Extensive computer simulations demonstrated increased statistical power of C-GWAS compared to the minimal p-values of multiple single-trait GWASs (MinGWAS) and the current state-of-the-art method for combining single-trait GWASs (MTAG). Applying C-GWAS to a meta-analysis dataset of 78 single trait facial GWASs from 10,115 Europeans identified 56 study-wide suggestively significant loci with multi-trait effects on facial morphology of which 17 are novel loci. Using data from additional 13,622 European and Asian samples, 46 (82%) loci, including 9 (53%) novel loci, were replicated at nominal significance with consistent allele effects. Functional analyses further strengthen the reliability of our C-GWAS findings. Our study introduces the C-GWAS method and makes it available as computationally efficient open-source R package for widespread future use. Our work also provides insights into the genetic architecture of human facial appearance.",
+                "authors": [
+                    {
+                        "name": "Chen Y."
+                    },
+                    {
+                        "name": "Feng Z."
+                    },
+                    {
+                        "name": "Gao X."
+                    },
+                    {
+                        "name": "Ghanbari M."
+                    },
+                    {
+                        "name": "Ikram A."
+                    },
+                    {
+                        "name": "Kayser M."
+                    },
+                    {
+                        "name": "Liu F."
+                    },
+                    {
+                        "name": "Lu H."
+                    },
+                    {
+                        "name": "Nijsten T."
+                    },
+                    {
+                        "name": "Pan S."
+                    },
+                    {
+                        "name": "Rivadeneira F."
+                    },
+                    {
+                        "name": "Uitterlinden A.G."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Xiong Z."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "Combining genome-wide association studies highlight novel loci involved in human facial variation"
+            },
+            "pmcid": "PMC9767941",
+            "pmid": "36539420"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        }
+    ]
+}
diff --git a/data/caa-net/caa-net.biotools.json b/data/caa-net/caa-net.biotools.json
new file mode 100644
index 0000000000000..ec29a7c67da12
--- /dev/null
+++ b/data/caa-net/caa-net.biotools.json
@@ -0,0 +1,75 @@
+{
+    "additionDate": "2023-01-26T15:35:05.561875Z",
+    "biotoolsCURIE": "biotools:caa-net",
+    "biotoolsID": "caa-net",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Jinhao Li"
+        }
+    ],
+    "description": "a novel end-to-end fully convolutional network, named Class-Aware Attention Network (CAA-Net), for automatically diagnosing infectious keratitis (normal, viral keratitis, fungal keratitis, and bacterial keratitis) using corneal photographs",
+    "editPermission": {
+        "type": "public"
+    },
+    "homepage": "https://github.com/SWF-hao/CAA-Net_Pytorch",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-26T15:35:05.564270Z",
+    "license": "Not licensed",
+    "name": "CAA-Net",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106301",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdInfectious keratitis is one of the common ophthalmic diseases and also one of the main blinding eye diseases in China, hence rapid and accurate diagnosis and treatment for infectious keratitis are urgent to prevent the progression of the disease and limit the degree of corneal injury. Unfortunately, the traditional manual diagnosis accuracy is usually unsatisfactory due to the indistinguishable visual features. In this paper, we propose a novel end-to-end fully convolutional network, named Class-Aware Attention Network (CAA-Net), for automatically diagnosing infectious keratitis (normal, viral keratitis, fungal keratitis, and bacterial keratitis) using corneal photographs. In CAA-Net, a class-aware classification module is first trained to learn class-related discriminative features using separate branches for each class. Then, the learned class-aware discriminative features are fed into the main branch and fused with other feature maps using two attention strategies to assist the final multi-class classification performance. For the experiments, we have built a new corneal photograph dataset with 1886 images from 519 patients and conducted comprehensive experiments to verify the effectiveness of our proposed method. The code is available at https://github.com/SWF-hao/CAA-Net_Pytorch.",
+                "authors": [
+                    {
+                        "name": "Hu S."
+                    },
+                    {
+                        "name": "Li J."
+                    },
+                    {
+                        "name": "Sun Y."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Xu P."
+                    },
+                    {
+                        "name": "Ye J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "Class-Aware Attention Network for infectious keratitis diagnosis using corneal photographs"
+            },
+            "pmid": "36403354"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/cafeteriasa_corpus/cafeteriasa_corpus.biotools.json b/data/cafeteriasa_corpus/cafeteriasa_corpus.biotools.json
new file mode 100644
index 0000000000000..f3125a03df161
--- /dev/null
+++ b/data/cafeteriasa_corpus/cafeteriasa_corpus.biotools.json
@@ -0,0 +1,127 @@
+{
+    "additionDate": "2023-02-12T15:51:53.156980Z",
+    "biotoolsCURIE": "biotools:cafeteriasa_corpus",
+    "biotoolsID": "cafeteriasa_corpus",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "gjorgjina.cenikj@ijs.si",
+            "name": "Gjorgjina Cenikj",
+            "orcidid": "https://orcid.org/0000-0002-2723-0821",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Scientific abstracts annotated across different food semantic resources.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Relation extraction",
+                    "uri": "http://edamontology.org/operation_3625"
+                },
+                {
+                    "term": "Text annotation",
+                    "uri": "http://edamontology.org/operation_3778"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://foodviz.env4health.finki.ukim.mk/#/cafeteria",
+    "lastUpdate": "2023-02-12T15:51:53.159601Z",
+    "license": "CC-BY-4.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://repo.ijs.si/matevzog/cafeteriancbo"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://zenodo.org/record/6683798#.Y49wIezMJJF"
+        }
+    ],
+    "name": "CafeteriaSA corpus",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/DATABASE/BAAC107",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.In the last decades, a great amount of work has been done in predictive modeling of issues related to human and environmental health. Resolution of issues related to healthcare is made possible by the existence of several biomedical vocabularies and standards, which play a crucial role in understanding the health information, together with a large amount of health-related data. However, despite a large number of available resources and work done in the health and environmental domains, there is a lack of semantic resources that can be utilized in the food and nutrition domain, as well as their interconnections. For this purpose, in a European Food Safety Authority-funded project CAFETERIA, we have developed the first annotated corpus of 500 scientific abstracts that consists of 6407 annotated food entities with regard to Hansard taxonomy, 4299 for FoodOn and 3623 for SNOMED-CT. The CafeteriaSA corpus will enable the further development of natural language processing methods for food information extraction from textual data that will allow extracting food information from scientific textual data. Database URL: https://zenodo.org/record/6683798#.Y49wIezMJJF.",
+                "authors": [
+                    {
+                        "name": "Cavalli E."
+                    },
+                    {
+                        "name": "Cenikj G."
+                    },
+                    {
+                        "name": "Eftimov T."
+                    },
+                    {
+                        "name": "Ispirova G."
+                    },
+                    {
+                        "name": "Korosec P."
+                    },
+                    {
+                        "name": "Ogrinc M."
+                    },
+                    {
+                        "name": "Seljak B.K."
+                    },
+                    {
+                        "name": "Stojanov R."
+                    },
+                    {
+                        "name": "Valencic E."
+                    }
+                ],
+                "date": "2022-12-16T00:00:00Z",
+                "journal": "Database : the journal of biological databases and curation",
+                "title": "CafeteriaSA corpus: scientific abstracts annotated across different food semantic resources"
+            },
+            "pmcid": "PMC9757992",
+            "pmid": "36526439"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Allergy, clinical immunology and immunotherapeutics",
+            "uri": "http://edamontology.org/topic_3400"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Nutritional science",
+            "uri": "http://edamontology.org/topic_3390"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Taxonomy",
+            "uri": "http://edamontology.org/topic_0637"
+        }
+    ]
+}
diff --git a/data/cancereffectsizer/cancereffectsizer.biotools.json b/data/cancereffectsizer/cancereffectsizer.biotools.json
new file mode 100644
index 0000000000000..eb6a8324d1456
--- /dev/null
+++ b/data/cancereffectsizer/cancereffectsizer.biotools.json
@@ -0,0 +1,87 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-12T15:55:07.130908Z",
+    "biotoolsCURIE": "biotools:cancereffectsizer",
+    "biotoolsID": "cancereffectsizer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Jeffrey D Mandell"
+        }
+    ],
+    "description": "Estimation of neutral mutation rates and quantification of somatic variant selection using canceffectsizeR.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://townsend-lab-yale.github.io/cancereffectsizeR/articles/cancereffectsizeR.html"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://townsend-lab-yale.github.io/cancereffectsizeR",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-12T15:55:07.133277Z",
+    "license": "GPL-3.0",
+    "name": "cancereffectsizeR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1158/0008-5472.CAN-22-1508",
+            "pmid": "36469362"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Epistasis",
+            "uri": "http://edamontology.org/topic_3974"
+        },
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        }
+    ]
+}
diff --git a/data/canmethdb/canmethdb.biotools.json b/data/canmethdb/canmethdb.biotools.json
new file mode 100644
index 0000000000000..f82928a76ec05
--- /dev/null
+++ b/data/canmethdb/canmethdb.biotools.json
@@ -0,0 +1,156 @@
+{
+    "additionDate": "2023-02-12T15:58:45.139140Z",
+    "biotoolsCURIE": "biotools:canmethdb",
+    "biotoolsID": "canmethdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ghwang@nefu.edu.cn",
+            "name": "Guohua Wang",
+            "orcidid": "https://orcid.org/0000-0001-7381-2374",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Chunquan Li",
+            "orcidid": "https://orcid.org/0000-0002-4700-5496",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A database for genome-wide DNA methylation annotation in cancers.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Gene methylation analysis",
+                    "uri": "http://edamontology.org/operation_3207"
+                },
+                {
+                    "term": "Methylation calling",
+                    "uri": "http://edamontology.org/operation_3919"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://meth.liclab.net/CanMethdb/",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-02-12T15:58:45.141678Z",
+    "license": "Other",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/chunquanlipathway/CanMethdb"
+        }
+    ],
+    "name": "CanMethdb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC783",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: DNA methylation within gene body and promoters in cancer cells is well documented. An increasing number of studies showed that cytosine-phosphate-guanine (CpG) sites falling within other regulatory elements could also regulate target gene activation, mainly by affecting transcription factors (TFs) binding in human cancers. This led to the urgent need for comprehensively and effectively collecting distinct cis-regulatory elements and TF-binding sites (TFBS) to annotate DNA methylation regulation. RESULTS: We developed a database (CanMethdb, http://meth.liclab.net/CanMethdb/) that focused on the upstream and downstream annotations for CpG-genes in cancers. This included upstream cis-regulatory elements, especially those involving distal regions to genes, and TFBS annotations for the CpGs and downstream functional annotations for the target genes, computed through integrating abundant DNA methylation and gene expression profiles in diverse cancers. Users could inquire CpG-target gene pairs for a cancer type through inputting a genomic region, a CpG, a gene name, or select hypo/hypermethylated CpG sets. The current version of CanMethdb documented a total of 38 986 060 CpG-target gene pairs (with 6 769 130 unique pairs), involving 385 217 CpGs and 18 044 target genes, abundant cis-regulatory elements and TFs for 33 TCGA cancer types. CanMethdb might help biologists perform in-depth studies of target gene regulations based on DNA methylations in cancer. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://github.com/chunquanlipathway/CanMethdb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Chen J."
+                    },
+                    {
+                        "name": "Ding K."
+                    },
+                    {
+                        "name": "Li C."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Pan Q."
+                    },
+                    {
+                        "name": "Qian F."
+                    },
+                    {
+                        "name": "Song C."
+                    },
+                    {
+                        "name": "Wang G."
+                    },
+                    {
+                        "name": "Wang Q."
+                    },
+                    {
+                        "name": "Yang Y."
+                    },
+                    {
+                        "name": "Yu R."
+                    },
+                    {
+                        "name": "Yu Z."
+                    },
+                    {
+                        "name": "Zhang J."
+                    },
+                    {
+                        "name": "Zhao J."
+                    },
+                    {
+                        "name": "Zhao Y."
+                    },
+                    {
+                        "name": "Zhu J."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "CanMethdb: a database for genome-wide DNA methylation annotation in cancers"
+            },
+            "pmcid": "PMC9825769",
+            "pmid": "36477791"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Gene regulation",
+            "uri": "http://edamontology.org/topic_0204"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/capsnh_kcr/capsnh_kcr.biotools.json b/data/capsnh_kcr/capsnh_kcr.biotools.json
new file mode 100644
index 0000000000000..6cd7aa0cc4434
--- /dev/null
+++ b/data/capsnh_kcr/capsnh_kcr.biotools.json
@@ -0,0 +1,101 @@
+{
+    "additionDate": "2023-02-12T16:01:53.338676Z",
+    "biotoolsCURIE": "biotools:capsnh_kcr",
+    "biotoolsID": "capsnh_kcr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hilaltayara@jbnu.ac.kr",
+            "name": "Hilal Tayara",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "kitchong@jbnu.ac.kr",
+            "name": "Kil To Chong",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Capsule network-based prediction of lysine crotonylation sites in human non-histone proteins.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "PTM identification",
+                    "uri": "http://edamontology.org/operation_3645"
+                },
+                {
+                    "term": "Protein feature detection",
+                    "uri": "http://edamontology.org/operation_3092"
+                },
+                {
+                    "term": "Sequence motif recognition",
+                    "uri": "http://edamontology.org/operation_0239"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Jhabindra-bioinfo/CapsNh-Kcr",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-12T16:01:53.341195Z",
+    "license": "Not licensed",
+    "name": "CapsNh-Kcr",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.11.056",
+            "metadata": {
+                "abstract": "© 2022Lysine crotonylation (Kcr) is one of the most important post-translational modifications (PTMs) that is widely detected in both histone and non-histone proteins. In fact, Kcr is reported to be involved in various biological processes, such as metabolism and cell differentiation. However, the available experimental methods for Kcr site identification are laborious and costly. To effectively replace existing experimental approaches, some computational methods have been developed in the last few years. The available computational methods still lack some important aspects, as they can only identify Kcr sites on either histone-only or combined histone and nonhistone proteins. Although a tool was developed to identify Kcr sites on non-histone proteins only, its performance is inadequate and the exploration of hidden Kcr patterns (motifs) has been completely ignored, which might be significant for detailed Kcr studies. Therefore, algorithms that can more effectively predict Kcr sites on non-histone proteins with their biological meaning need to be designed. Accordingly, we developed a novel deep learning (capsule network)-based model, named CapsNh-Kcr, for Kcr site prediction, particularly focusing on non-histone proteins. Based on the independent results, the proposed model achieves an AUC of 0.9120, which is approximately 6% higher than that of previous nhKcr model in the prediction of Kcr sites on non-histone proteins. Further, we revealed, for the first time, that the proposed model can represent obvious motif distribution across Kcr sites in non-histone proteins. The source code (in Python) is publicly available at https://github.com/Jhabindra-bioinfo/CapsNh-Kcr.",
+                "authors": [
+                    {
+                        "name": "Chong K.T."
+                    },
+                    {
+                        "name": "Kandel J."
+                    },
+                    {
+                        "name": "Khanal J."
+                    },
+                    {
+                        "name": "Tayara H."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "CapsNh-Kcr: Capsule network-based prediction of lysine crotonylation sites in human non-histone proteins"
+            },
+            "pmcid": "PMC9735261",
+            "pmid": "36544479"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Protein modifications",
+            "uri": "http://edamontology.org/topic_0601"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/cararime/cararime.biotools.json b/data/cararime/cararime.biotools.json
new file mode 100644
index 0000000000000..0c1eef861b376
--- /dev/null
+++ b/data/cararime/cararime.biotools.json
@@ -0,0 +1,144 @@
+{
+    "additionDate": "2023-02-13T18:54:36.781958Z",
+    "biotoolsCURIE": "biotools:cararime",
+    "biotoolsID": "cararime",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "liuyg168@smu.edu.cn",
+            "name": "Yongguang Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhaoming02@hotmail.com",
+            "name": "Ming Zhao",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Interactive web server for comprehensive analysis of renal allograft rejection in immune microenvironment.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.cararime.com",
+    "lastUpdate": "2023-02-13T18:54:36.784661Z",
+    "license": "Other",
+    "name": "CARARIME",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FIMMU.2022.1026280",
+            "metadata": {
+                "abstract": "Copyright © 2022 Liu, Liu, Zhou, Jiang, Zhang, Hu, Liao, Liao, Guo, Li, Yang, Li, Chen, Guo, Li, Fan, Li, Zhao and Liu.Background: Renal transplantation is a very effective treatment for renal failure patients following kidney transplant. However, the clinical benefit is restricted by the high incidence of organ rejection. Therefore, there exists a wealth of literature regarding the mechanism of renal transplant rejection, including a large library of expression data. In recent years, research has shown the immune microenvironment to play an important role in renal transplant rejection. Nephrology web analysis tools currently exist to address chronic nephropathy, renal tumors and children’s kidneys, but no such tool exists that analyses the impact of immune microenvironment in renal transplantation rejection. Methods: To fill this gap, we have developed a web page analysis tool called Comprehensive Analysis of Renal Allograft Rerejction in Immune Microenvironment (CARARIME). Results: CARARIME analyzes the gene expression and immune microenvironment of published renal transplant rejection cohorts, including differential analysis (gene expression and immune cells), prognosis analysis (logistics regression, Univariable Cox Regression and Kaplan Meier), correlation analysis, enrichment analysis (GSEA and ssGSEA), and ROC analysis. Conclusions: Using this tool, researchers can easily analyze the immune microenvironment in the context of renal transplant rejection by clicking on the available options, helping to further the development of approaches to renal transplant rejection in the immune microenvironment field. CARARIME can be found in http://www.cararime.com.",
+                "authors": [
+                    {
+                        "name": "Chen H."
+                    },
+                    {
+                        "name": "Fan L."
+                    },
+                    {
+                        "name": "Guo Y."
+                    },
+                    {
+                        "name": "Guo Z."
+                    },
+                    {
+                        "name": "Hu J."
+                    },
+                    {
+                        "name": "Jiang W."
+                    },
+                    {
+                        "name": "Li L."
+                    },
+                    {
+                        "name": "Li M."
+                    },
+                    {
+                        "name": "Li S."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Liao G."
+                    },
+                    {
+                        "name": "Liao J."
+                    },
+                    {
+                        "name": "Liu D."
+                    },
+                    {
+                        "name": "Liu X."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Yang S."
+                    },
+                    {
+                        "name": "Zhang J."
+                    },
+                    {
+                        "name": "Zhao M."
+                    },
+                    {
+                        "name": "Zhou S."
+                    }
+                ],
+                "date": "2022-11-17T00:00:00Z",
+                "journal": "Frontiers in Immunology",
+                "title": "CARARIME: Interactive web server for comprehensive analysis of renal allograft rejection in immune microenvironment"
+            },
+            "pmcid": "PMC9714541",
+            "pmid": "36466852"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Surgery",
+            "uri": "http://edamontology.org/topic_3421"
+        },
+        {
+            "term": "Urology and nephrology",
+            "uri": "http://edamontology.org/topic_3422"
+        }
+    ]
+}
diff --git a/data/catnet/catnet.biotools.json b/data/catnet/catnet.biotools.json
new file mode 100644
index 0000000000000..81b16cd602b8f
--- /dev/null
+++ b/data/catnet/catnet.biotools.json
@@ -0,0 +1,83 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T19:02:43.349948Z",
+    "biotoolsCURIE": "biotools:catnet",
+    "biotoolsID": "catnet",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Sicen Liu"
+        }
+    ],
+    "description": "Cross-event attention-based time-aware network for medical event prediction",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/sherry6247/CATNet.git",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T19:02:43.352609Z",
+    "license": "Not licensed",
+    "name": "CATNet",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.ARTMED.2022.102440",
+            "metadata": {
+                "abstract": "© 2022 Elsevier B.V.Medical event prediction (MEP) is a fundamental task in the healthcare domain, which needs to predict medical events, including medications, diagnosis codes, laboratory tests, procedures, outcomes, and so on, according to historical medical records of patients. Many researchers have tried to build MEP models to overcome the challenges caused by the heterogeneous and irregular temporal characteristics of EHR data. However, most of them consider the heterogenous and temporal medical events separately and ignore the correlations among different types of medical events, especially relations between heterogeneous historical medical events and target medical events. In this paper, we propose a novel neural network based on attention mechanism called Cross-event Attention-based Time-aware Network (CATNet) for MEP. It is a time-aware, event-aware and task-adaptive method with the following advantages: 1) modeling heterogeneous information and temporal information in a unified way and considering irregular temporal characteristics locally and globally respectively, 2) taking full advantage of correlations among different types of events via cross-event attention. Experiments on two public datasets (MIMIC-III and eICU) show CATNet outperforms other state-of-the-art methods on various MEP tasks. The source code of CATNet is released at https://github.com/sherry6247/CATNet.git.",
+                "authors": [
+                    {
+                        "name": "Liu S."
+                    },
+                    {
+                        "name": "Tang B."
+                    },
+                    {
+                        "name": "Wang H."
+                    },
+                    {
+                        "name": "Wang X."
+                    },
+                    {
+                        "name": "Xiang Y."
+                    },
+                    {
+                        "name": "Xu H."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Artificial Intelligence in Medicine",
+                "title": "CATNet: Cross-event attention-based time-aware network for medical event prediction"
+            },
+            "pmid": "36462902"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Human biology",
+            "uri": "http://edamontology.org/topic_2815"
+        },
+        {
+            "term": "Laboratory techniques",
+            "uri": "http://edamontology.org/topic_3361"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/catsnap/catsnap.biotools.json b/data/catsnap/catsnap.biotools.json
new file mode 100644
index 0000000000000..7ee6159c3f0f7
--- /dev/null
+++ b/data/catsnap/catsnap.biotools.json
@@ -0,0 +1,88 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-09T13:58:25.475801Z",
+    "biotoolsCURIE": "biotools:catsnap",
+    "biotoolsID": "catsnap",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "kamil.ruzicka@ueb.cas.cz",
+            "name": "Kamil Růžička"
+        },
+        {
+            "name": "Ksenia Timofeyenko"
+        }
+    ],
+    "description": "A user-friendly algorithm for determining the conservation of protein variants reveals extensive parallelisms in the evolution of alternative splicing.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Alternative splicing prediction",
+                    "uri": "http://edamontology.org/operation_0264"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://catsnap.cesnet.cz/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-09T13:58:25.480401Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/kdcd/catsnap"
+        }
+    ],
+    "name": "Catsnap",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1111/NPH.18799",
+            "pmid": "36751910"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular evolution",
+            "uri": "http://edamontology.org/topic_3945"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/cauchygm/cauchygm.biotools.json b/data/cauchygm/cauchygm.biotools.json
new file mode 100644
index 0000000000000..df1b0bd6b6d78
--- /dev/null
+++ b/data/cauchygm/cauchygm.biotools.json
@@ -0,0 +1,101 @@
+{
+    "additionDate": "2023-01-26T15:45:24.374588Z",
+    "biotoolsCURIE": "biotools:cauchygm",
+    "biotoolsID": "cauchygm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "feizou@email.unc.edu",
+            "name": "Fei Zou",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "judong.shen@merck.com",
+            "name": "Judong Shen",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Robust genetic model-based SNP-set association test using CauchyGM.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Collapsing methods",
+                    "uri": "http://edamontology.org/operation_3791"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ykim03517/CauchyGM",
+    "language": [
+        "C++",
+        "R"
+    ],
+    "lastUpdate": "2023-01-26T15:45:24.377042Z",
+    "license": "Not licensed",
+    "name": "CauchyGM",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC728",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Association testing on genome-wide association studies (GWAS) data is commonly performed under a single (mostly additive) genetic model framework. However, the underlying true genetic mechanisms are often unknown in practice for most complex traits. When the employed inheritance model deviates from the underlying model, statistical power may be reduced. To overcome this challenge, an integrative association test that directly infers the underlying genetic model from GWAS data has previously been proposed for single-SNP analysis. RESULTS: In this article, we propose a Cauchy combination Genetic Model-based association test (CauchyGM) under a generalized linear model framework for SNP-set level analysis. CauchyGM does not require prior knowledge on the underlying inheritance pattern of each SNP. It performs a score test that first estimates an individual P-value of each SNP in an SNP-set with both minor allele frequency (MAF) > 1% and three genotypes and further aggregates the rest SNPs using SKAT. CauchyGM then combines the correlated P-values across multiple SNPs and different genetic models within the set using Cauchy Combination Test. To further accommodate both sparse and dense signal patterns, we also propose an omnibus association test (CauchyGM-O) by combining CauchyGM with SKAT and the burden test. Our extensive simulations show that both CauchyGM and CauchyGM-O maintain the type I error well at the genome-wide significance level and provide substantial power improvement compared to existing methods. We apply our methods to a pharmacogenomic GWAS data from a large cardiovascular randomized clinical trial. Both CauchyGM and CauchyGM-O identify several novel genome-wide significant genes. AVAILABILITY AND IMPLEMENTATION: The R package CauchyGM is publicly available on github: https://github.com/ykim03517/CauchyGM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Chi Y.-Y."
+                    },
+                    {
+                        "name": "Kim Y."
+                    },
+                    {
+                        "name": "Shen J."
+                    },
+                    {
+                        "name": "Zou F."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Robust genetic model-based SNP-set association test using CauchyGM"
+            },
+            "pmid": "36383169"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Pharmacogenomics",
+            "uri": "http://edamontology.org/topic_0208"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        }
+    ]
+}
diff --git a/data/caulifinder/caulifinder.biotools.json b/data/caulifinder/caulifinder.biotools.json
new file mode 100644
index 0000000000000..80fb128364846
--- /dev/null
+++ b/data/caulifinder/caulifinder.biotools.json
@@ -0,0 +1,144 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T19:08:00.793563Z",
+    "biotoolsCURIE": "biotools:caulifinder",
+    "biotoolsID": "caulifinder",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "florian.maumus@inrae.fr",
+            "name": "Florian Maumus",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A pipeline for the automated detection and annotation of caulimovirid endogenous viral elements in plant genomes.",
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://forgemia.inra.fr/urgi-anagen/caulifinder_docker"
+        },
+        {
+            "type": "Container file",
+            "url": "https://hub.docker.com/r/urgi/docker_caulifinder"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Ancestral reconstruction",
+                    "uri": "http://edamontology.org/operation_3745"
+                },
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Phylogenetic reconstruction",
+                    "uri": "http://edamontology.org/operation_3478"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://forgemia.inra.fr/urgi-anagen/event_caulifinder",
+    "language": [
+        "Bash",
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-13T19:08:00.796014Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://figshare.com/projects/Caulifinder/143532"
+        }
+    ],
+    "name": "CAULIFINDER",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S13100-022-00288-W",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Plant, animal and protist genomes often contain endogenous viral elements (EVEs), which correspond to partial and sometimes entire viral genomes that have been captured in the genome of their host organism through a variety of integration mechanisms. While the number of sequenced eukaryotic genomes is rapidly increasing, the annotation and characterization of EVEs remains largely overlooked. EVEs that derive from members of the family Caulimoviridae are widespread across tracheophyte plants, and sometimes they occur in very high copy numbers. However, existing programs for annotating repetitive DNA elements in plant genomes are poor at identifying and then classifying these EVEs. Other than accurately annotating plant genomes, there is intrinsic value in a tool that could identify caulimovirid EVEs as they testify to recent or ancient host-virus interactions and provide valuable insights into virus evolution. In response to this research need, we have developed CAULIFINDER, an automated and sensitive annotation software package. CAULIFINDER consists of two complementary workflows, one to reconstruct, annotate and group caulimovirid EVEs in a given plant genome and the second to classify these genetic elements into officially recognized or tentative genera in the Caulimoviridae. We have benchmarked the CAULIFINDER package using the Vitis vinifera reference genome, which contains a rich assortment of caulimovirid EVEs that have previously been characterized using manual methods. The CAULIFINDER package is distributed in the form of a Docker image.",
+                "authors": [
+                    {
+                        "name": "Choisne N."
+                    },
+                    {
+                        "name": "Geering A.D.W."
+                    },
+                    {
+                        "name": "Giraud D."
+                    },
+                    {
+                        "name": "Haddad S."
+                    },
+                    {
+                        "name": "Jamilloux V."
+                    },
+                    {
+                        "name": "Maumus F."
+                    },
+                    {
+                        "name": "Serfraz S."
+                    },
+                    {
+                        "name": "Sharma V."
+                    },
+                    {
+                        "name": "Teycheney P.-Y."
+                    },
+                    {
+                        "name": "Vassilieff H."
+                    },
+                    {
+                        "name": "Wan M."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Mobile DNA",
+                "title": "CAULIFINDER: a pipeline for the automated detection and annotation of caulimovirid endogenous viral elements in plant genomes"
+            },
+            "pmcid": "PMC9719215",
+            "pmid": "36463202"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/causnet/causnet.biotools.json b/data/causnet/causnet.biotools.json
new file mode 100644
index 0000000000000..2ca42ce7400c5
--- /dev/null
+++ b/data/causnet/causnet.biotools.json
@@ -0,0 +1,81 @@
+{
+    "additionDate": "2023-03-09T14:03:38.047223Z",
+    "biotoolsCURIE": "biotools:causnet",
+    "biotoolsID": "causnet",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "nandsh11@gmail.com",
+            "name": "Nand Sharma",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Tool for generational orderings based search for optimal Bayesian networks via dynamic programming with parent set constraints.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/nand1155/CausNet",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-09T14:03:38.051205Z",
+    "license": "Not licensed",
+    "name": "CausNet",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-023-05159-6",
+            "metadata": {
+                "abstract": "Background: Finding a globally optimal Bayesian Network using exhaustive search is a problem with super-exponential complexity, which severely restricts the number of variables that can feasibly be included. We implement a dynamic programming based algorithm with built-in dimensionality reduction and parent set identification. This reduces the search space substantially and can be applied to large-dimensional data. We use what we call ‘generational orderings’ based search for optimal networks, which is a novel way to efficiently search the space of possible networks given the possible parent sets. The algorithm supports both continuous and categorical data, as well as continuous, binary and survival outcomes. Results: We demonstrate the efficacy of our algorithm on both synthetic and real data. In simulations, our algorithm performs better than three state-of-art algorithms that are currently used extensively. We then apply it to an Ovarian Cancer gene expression dataset with 513 genes and a survival outcome. Our algorithm is able to find an optimal network describing the disease pathway consisting of 6 genes leading to the outcome node in just 3.4 min on a personal computer with a 2.3 GHz Intel Core i9 processor with 16 GB RAM. Conclusions: Our generational orderings based search for optimal networks is both an efficient and highly scalable approach for finding optimal Bayesian Networks and can be applied to 1000 s of variables. Using specifiable parameters—correlation, FDR cutoffs, and in-degree—one can increase or decrease the number of nodes and density of the networks. Availability of two scoring option—BIC and Bge—and implementation for survival outcomes and mixed data types makes our algorithm very suitable for many types of high dimensional data in a variety of fields.",
+                "authors": [
+                    {
+                        "name": "Millstein J."
+                    },
+                    {
+                        "name": "Sharma N."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "CausNet: generational orderings based search for optimal Bayesian networks via dynamic programming with parent set constraints"
+            },
+            "pmcid": "PMC9926787",
+            "pmid": "36788490"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/cdd/cdd.biotools.json b/data/cdd/cdd.biotools.json
index e9451ae47b82d..0a17985d384ba 100644
--- a/data/cdd/cdd.biotools.json
+++ b/data/cdd/cdd.biotools.json
@@ -324,7 +324,7 @@
         }
     ],
     "homepage": "http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml",
-    "lastUpdate": "2022-07-05T16:26:04.849088Z",
+    "lastUpdate": "2023-02-13T19:46:42.072824Z",
     "name": "Conserved domain database CDD",
     "operatingSystem": [
         "Linux",
@@ -333,6 +333,64 @@
     ],
     "owner": "DRCAT",
     "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1096",
+            "metadata": {
+                "abstract": "Published by Oxford University Press on behalf of Nucleic Acids Research 2022.NLM's conserved domain database (CDD) is a collection of protein domain and protein family models constructed as multiple sequence alignments. Its main purpose is to provide annotation for protein and translated nucleotide sequences with the location of domain footprints and associated functional sites, and to define protein domain architecture as a basis for assigning gene product names and putative/predicted function. CDD has been available publicly for over 20 years and has grown substantially during that time. Maintaining an archive of pre-computed annotation continues to be a challenge and has slowed down the cadence of CDD releases. CDD curation staff builds hierarchical classifications of large protein domain families, adds models for novel domain families via surveillance of the protein 'dark matter' that currently lacks annotation, and now spends considerable effort on providing names and attribution for conserved domain architectures. CDD can be accessed at https://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml.",
+                "authors": [
+                    {
+                        "name": "Chitsaz F."
+                    },
+                    {
+                        "name": "Derbyshire M.K."
+                    },
+                    {
+                        "name": "Gonzales N.R."
+                    },
+                    {
+                        "name": "Gwadz M."
+                    },
+                    {
+                        "name": "Lanczycki C.J."
+                    },
+                    {
+                        "name": "Lu S."
+                    },
+                    {
+                        "name": "Marchler G.H."
+                    },
+                    {
+                        "name": "Marchler-Bauer A."
+                    },
+                    {
+                        "name": "Song J.S."
+                    },
+                    {
+                        "name": "Thanki N."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Yamashita R.A."
+                    },
+                    {
+                        "name": "Yang M."
+                    },
+                    {
+                        "name": "Zhang D."
+                    },
+                    {
+                        "name": "Zheng C."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "The conserved domain database in 2023"
+            },
+            "pmcid": "PMC9825596",
+            "pmid": "36477806"
+        },
         {
             "doi": "10.1093/nar/gkz991",
             "metadata": {
@@ -390,7 +448,7 @@
                         "name": "Zheng C."
                     }
                 ],
-                "citationCount": 630,
+                "citationCount": 981,
                 "date": "2020-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "CDD/SPARCLE: The conserved domain database in 2020"
@@ -485,7 +543,7 @@
                         "name": "Zheng C."
                     }
                 ],
-                "citationCount": 2258,
+                "citationCount": 2346,
                 "date": "2011-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "CDD: A Conserved Domain Database for the functional annotation of proteins"
@@ -520,13 +578,98 @@
                         "name": "Thiessen P.A."
                     }
                 ],
-                "citationCount": 521,
+                "citationCount": 529,
                 "date": "2002-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "CDD: A database of conserved domain alignments with links to domain three-dimensional structure"
             },
             "pmid": "11752315"
         },
+        {
+            "doi": "10.1093/nar/gki069",
+            "metadata": {
+                "abstract": "The Conserved Domain Database (CDD) is the protein classification component of NCBI's Entrez query and retrieval system. CDD is linked to other Entrez databases such as Proteins, Taxonomy and PubMed®, and can be accessed at http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=cdd. CD-Search, which is available at http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi, is a fast, interactive tool to identify conserved domains in new protein sequences. CD-Search results for protein sequences in Entrez are pre-computed to provide links between proteins and domain models, and computational annotation visible upon request. Protein-protein queries submitted to NCBI's BLAST search service at http://www.ncbi.nlm.nih.gov/BLAST are scanned for the presence of conserved domains by default. While CDD started out as essentially a mirror of publicly available domain alignment collections, such as SMART, Pfam and COG, we have continued an effort to update, and in some cases replace these models with domain hierarchies curated at the NCBI. Here, we report on the progress of the curation effort and associated improvements in the functionality of the CDD information retrieval system. © Oxford University Press 2005; all rights reserved.",
+                "authors": [
+                    {
+                        "name": "Anderson J.B."
+                    },
+                    {
+                        "name": "Bryant S.H."
+                    },
+                    {
+                        "name": "Cherukuri P.F."
+                    },
+                    {
+                        "name": "DeWeese-Scott C."
+                    },
+                    {
+                        "name": "Geer L.Y."
+                    },
+                    {
+                        "name": "Gwadz M."
+                    },
+                    {
+                        "name": "He S."
+                    },
+                    {
+                        "name": "Hurwitz D.I."
+                    },
+                    {
+                        "name": "Jackson J.D."
+                    },
+                    {
+                        "name": "Ke Z."
+                    },
+                    {
+                        "name": "Lanczycki C.J."
+                    },
+                    {
+                        "name": "Liebert C.A."
+                    },
+                    {
+                        "name": "Liu C."
+                    },
+                    {
+                        "name": "Lu F."
+                    },
+                    {
+                        "name": "Marchler G.H."
+                    },
+                    {
+                        "name": "Marchler-Bauer A."
+                    },
+                    {
+                        "name": "Mullokandov M."
+                    },
+                    {
+                        "name": "Shoemaker B.A."
+                    },
+                    {
+                        "name": "Simonyan V."
+                    },
+                    {
+                        "name": "Song J.S."
+                    },
+                    {
+                        "name": "Thiessen P.A."
+                    },
+                    {
+                        "name": "Yamashita R.A."
+                    },
+                    {
+                        "name": "Yin J.J."
+                    },
+                    {
+                        "name": "Zhang D."
+                    }
+                ],
+                "citationCount": 964,
+                "date": "2005-01-01T00:00:00Z",
+                "journal": "Nucleic Acids Research",
+                "title": "CDD: A Conserved Domain Database for protein classification"
+            },
+            "pmid": "15608175"
+        },
         {
             "doi": "10.1093/nar/gkl951",
             "metadata": {
@@ -608,7 +751,7 @@
                         "name": "Zhang D."
                     }
                 ],
-                "citationCount": 686,
+                "citationCount": 695,
                 "date": "2007-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "CDD: A conserved domain database for interactive domain family analysis"
@@ -705,7 +848,7 @@
                         "name": "Zhang N."
                     }
                 ],
-                "citationCount": 908,
+                "citationCount": 916,
                 "date": "2009-01-09T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "CDD: Specific functional annotation with the Conserved Domain Database"
@@ -772,17 +915,13 @@
                         "name": "Zheng C."
                     }
                 ],
-                "citationCount": 653,
+                "citationCount": 676,
                 "date": "2013-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "CDD: Conserved domains and protein three-dimensional structure"
             },
             "pmid": "23197659"
         },
-        {
-            "doi": "10.1093/nar/gki069",
-            "pmid": "15608175"
-        },
         {
             "metadata": {
                 "abstract": "NCBI's Conserved Domain Database (CDD) is a resource for the annotation of protein sequences with the location of conserved domain footprints, and functional sites inferred from these footprints. CDD includes manually curated domain models that make use of protein 3D structure to refine domain models and provide insights into sequence/ structure/function relationships. Manually curated models are organized hierarchically if they describe domain families that are clearly related by common descent. As CDD also imports domain family models from a variety of external sources, it is a partially redundant collection. To simplify protein annotation, redundant models and models describing homologous families are clustered into superfamilies. By default, domain footprints are annotated with the corresponding superfamily designation, on top of which specific annotation may indicate high-confidence assignment of family membership. Pre-computed domain annotation is available for proteins in the Entrez/Protein dataset, and a novel interface, Batch CD-Search, allows the computation and download of annotation for large sets of protein queries. CDD can be accessed via http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml. © The Author(s) 2010.",
@@ -869,7 +1008,7 @@
                         "name": "Zheng C."
                     }
                 ],
-                "citationCount": 2258,
+                "citationCount": 2346,
                 "date": "2011-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "CDD: A Conserved Domain Database for the functional annotation of proteins"
diff --git a/data/cdhgnn/cdhgnn.biotools.json b/data/cdhgnn/cdhgnn.biotools.json
new file mode 100644
index 0000000000000..60ece50d61836
--- /dev/null
+++ b/data/cdhgnn/cdhgnn.biotools.json
@@ -0,0 +1,103 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T19:12:51.898871Z",
+    "biotoolsCURIE": "biotools:cdhgnn",
+    "biotoolsID": "cdhgnn",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "duangh@csu.edu.cn",
+            "name": "Guihua Duan",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "miRNA target prediction",
+                    "uri": "http://edamontology.org/operation_0463"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/BioinformaticsCSU/CDHGNN",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T19:12:51.901633Z",
+    "license": "MIT",
+    "name": "CDHGNN",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC549",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Emerging evidence has proved that circular RNAs (circRNAs) are implicated in pathogenic processes. They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA-disease associations at a lower cost. Aggregating multi-source pathogenesis data helps to alleviate data sparsity and infer potential associations at the system level. The majority of computational approaches construct a homologous network using multi-source data, but they lose the heterogeneity of the data. Effective methods that use the features of multi-source data are considered as a matter of urgency. In this paper, we propose a model (CDHGNN) based on edge-weighted graph attention and heterogeneous graph neural networks for potential circRNA-disease association prediction. The circRNA network, micro RNA network, disease network and heterogeneous network are constructed based on multi-source data. To reflect association probabilities between nodes, an edge-weighted graph attention network model is designed for node features. To assign attention weights to different types of edges and learn contextual meta-path, CDHGNN infers potential circRNA-disease association based on heterogeneous neural networks. CDHGNN outperforms state-of-the-art algorithms in terms of accuracy. Edge-weighted graph attention networks and heterogeneous graph networks have both improved performance significantly. Furthermore, case studies suggest that CDHGNN is capable of identifying specific molecular associations and investigating biomolecular regulatory relationships in pathogenesis. The code of CDHGNN is freely available at https://github.com/BioinformaticsCSU/CDHGNN.",
+                "authors": [
+                    {
+                        "name": "Duan G."
+                    },
+                    {
+                        "name": "Lan W."
+                    },
+                    {
+                        "name": "Lu C."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Zeng M."
+                    },
+                    {
+                        "name": "Zhang L."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network"
+            },
+            "pmid": "36572658"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/cdpred/cdpred.biotools.json b/data/cdpred/cdpred.biotools.json
index 08de35f2c7bd7..720962ea853b0 100644
--- a/data/cdpred/cdpred.biotools.json
+++ b/data/cdpred/cdpred.biotools.json
@@ -1,22 +1,26 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2022-09-30T06:55:08.965544Z",
     "biotoolsCURIE": "biotools:cdpred",
     "biotoolsID": "cdpred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "credit": [
+        {
+            "name": "Anjali Dhall"
+        },
+        {
+            "name": "Ritu Tomer"
+        },
+        {
+            "name": "Sumeet Patiyal"
+        },
         {
             "name": "Gajendra P.S. Raghava",
-            "url": "https://webs.iiitd.edu.in/raghava/cdpred/index.php"
+            "orcidid": "http://orcid.org/0000-0002-8902-2876"
         }
     ],
     "description": "CDpred is a web based approach used to predict the celiac disease associated peptides and motifs.",
-    "documentation": [
-        {
-            "type": [
-                "General"
-            ],
-            "url": "https://webs.iiitd.edu.in/raghava/cdpred/index.php"
-        }
-    ],
     "editPermission": {
         "type": "private"
     },
@@ -24,15 +28,37 @@
         {
             "operation": [
                 {
-                    "term": "Analysis",
-                    "uri": "http://edamontology.org/operation_2945"
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                },
+                {
+                    "term": "Protein identification",
+                    "uri": "http://edamontology.org/operation_3767"
+                },
+                {
+                    "term": "Sequence motif recognition",
+                    "uri": "http://edamontology.org/operation_0239"
                 }
             ]
         }
     ],
     "homepage": "https://webs.iiitd.edu.in/raghava/cdpred/index.php",
-    "lastUpdate": "2022-09-30T06:55:14.170525Z",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-28T19:12:52.779622Z",
+    "license": "GPL-3.0",
     "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://gitlab.com/raghavalab/cdpred"
+        },
         {
             "type": [
                 "Software catalogue"
@@ -47,13 +73,57 @@
         "Windows"
     ],
     "owner": "raghavagps",
+    "publication": [
+        {
+            "doi": "10.3389/fimmu.2023.1056101",
+            "metadata": {
+                "abstract": "Copyright © 2023 Tomer, Patiyal, Dhall and Raghava.Introduction: Celiac disease (CD) is an autoimmune gastrointestinal disorder causes immune-mediated enteropathy against gluten. Gluten immunogenic peptides have the potential to trigger immune responses which leads to damage the small intestine. HLA-DQ2/DQ8 are major alleles that bind to epitope/antigenic region of gluten and induce celiac disease. There is a need to identify CD associated epitopes in protein-based foods and therapeutics. Methods: In this study, computational tools have been developed to predict CD associated epitopes and motifs. Dataset used for training, testing and evaluation contain experimentally validated CD associated and non-CD associate peptides. We perform positional analysis to identify the most significant position of an amino acid residue in the peptide and checked the frequency of HLA alleles. We also compute amino acid composition to develop machine learning based models. We also developed ensemble method that combines motif-based approach and machine learning based models. Results and Discussion: Our analysis support existing hypothesis that proline (P) and glutamine (Q) are highly abundant in CD associated peptides. A model based on density of P&Q in peptides has been developed for predicting CD associated peptides which achieve maximum AUROC 0.98 on independent data. We discovered motifs (e.g., QPF, QPQ, PYP) which occurs specifically in CD associated peptides. We also developed machine learning based models using peptide composition and achieved maximum AUROC 0.99. Finally, we developed ensemble method that combines motif-based approach and machine learning based models. The ensemble model-predict CD associated motifs with 100% accuracy on an independent dataset, not used for training. Finally, the best models and motifs has been integrated in a web server and standalone software package “CDpred”. We hope this server anticipate the scientific community for the prediction, designing and scanning of CD associated peptides as well as CD associated motifs in a protein/peptide sequence (https://webs.iiitd.edu.in/raghava/cdpred/).",
+                "authors": [
+                    {
+                        "name": "Dhall A."
+                    },
+                    {
+                        "name": "Patiyal S."
+                    },
+                    {
+                        "name": "Raghava G.P.S."
+                    },
+                    {
+                        "name": "Tomer R."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Frontiers in Immunology",
+                "title": "Prediction of celiac disease associated epitopes and motifs in a protein"
+            },
+            "pmcid": "PMC9893285",
+            "pmid": "36742312"
+        }
+    ],
     "toolType": [
+        "Command-line tool",
         "Web application"
     ],
     "topic": [
         {
-            "term": "Computational biology",
-            "uri": "http://edamontology.org/topic_3307"
+            "term": "Immunogenetics",
+            "uri": "http://edamontology.org/topic_3930"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
         }
     ]
 }
diff --git a/data/celldrift/celldrift.biotools.json b/data/celldrift/celldrift.biotools.json
new file mode 100644
index 0000000000000..8a92b7cdd3340
--- /dev/null
+++ b/data/celldrift/celldrift.biotools.json
@@ -0,0 +1,123 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-18T22:28:32.895559Z",
+    "biotoolsCURIE": "biotools:celldrift",
+    "biotoolsID": "celldrift",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "bruce.aronow@cchmc.org",
+            "name": "Bruce J. Aronow",
+            "orcidid": "http://orcid.org/0000-0001-9689-2469",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Daniel Schnell"
+        },
+        {
+            "name": "Guangyuan Li",
+            "orcidid": "http://orcid.org/0000-0002-0628-2454"
+        },
+        {
+            "name": "Kang Jin",
+            "orcidid": "http://orcid.org/0000-0001-5638-040X"
+        }
+    ],
+    "description": "Inferring Perturbation Responses in Temporally-Sampled Single Cell Data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Enrichment analysis",
+                    "uri": "http://edamontology.org/operation_3501"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/KANG-BIOINFO/CellDrift",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-18T22:28:32.898176Z",
+    "license": "MIT",
+    "name": "CellDrift",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bib/bbac324",
+            "metadata": {
+                "abstract": "© 2022 The Author(s).Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in the alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method that is capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories and outcomes.",
+                "authors": [
+                    {
+                        "name": "Aronow B.J."
+                    },
+                    {
+                        "name": "Jin K."
+                    },
+                    {
+                        "name": "Li G."
+                    },
+                    {
+                        "name": "Prasath V.B.S."
+                    },
+                    {
+                        "name": "Salomonis N."
+                    },
+                    {
+                        "name": "Schnell D."
+                    },
+                    {
+                        "name": "Szczesniak R."
+                    }
+                ],
+                "date": "2022-09-01T00:00:00Z",
+                "journal": "Briefings in Bioinformatics",
+                "title": "CellDrift: Inferring perturbation responses in temporally sampled single-cell data"
+            },
+            "pmcid": "PMC9487655",
+            "pmid": "35998893"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/cellpalmseq/cellpalmseq.biotools.json b/data/cellpalmseq/cellpalmseq.biotools.json
new file mode 100644
index 0000000000000..fe3a38de463ce
--- /dev/null
+++ b/data/cellpalmseq/cellpalmseq.biotools.json
@@ -0,0 +1,111 @@
+{
+    "additionDate": "2023-03-09T14:07:06.354313Z",
+    "biotoolsCURIE": "biotools:cellpalmseq",
+    "biotoolsID": "cellpalmseq",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "shernaz.bamji@ubc.ca",
+            "name": "Shernaz X. Bamji",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A curated RNAseq database of palmitoylating and de-palmitoylating enzyme expression in human cell types and laboratory cell lines.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cellpalmseq.med.ubc.ca",
+    "language": [
+        "JavaScript",
+        "Python"
+    ],
+    "lastUpdate": "2023-03-09T14:07:06.358994Z",
+    "name": "CellPalmSeq",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FPHYS.2023.1110550",
+            "metadata": {
+                "abstract": "The reversible lipid modification protein S-palmitoylation can dynamically modify the localization, diffusion, function, conformation and physical interactions of substrate proteins. Dysregulated S-palmitoylation is associated with a multitude of human diseases including brain and metabolic disorders, viral infection and cancer. However, the diverse expression patterns of the genes that regulate palmitoylation in the broad range of human cell types are currently unexplored, and their expression in commonly used cell lines that are the workhorse of basic and preclinical research are often overlooked when studying palmitoylation dependent processes. We therefore created CellPalmSeq (https://cellpalmseq.med.ubc.ca), a curated RNAseq database and interactive webtool for visualization of the expression patterns of the genes that regulate palmitoylation across human single cell types, bulk tissue, cancer cell lines and commonly used laboratory non-human cell lines. This resource will allow exploration of these expression patterns, revealing important insights into cellular physiology and disease, and will aid with cell line selection and the interpretation of results when studying important cellular processes that depend on protein S-palmitoylation.",
+                "authors": [
+                    {
+                        "name": "Bamji S.X."
+                    },
+                    {
+                        "name": "Flibotte S."
+                    },
+                    {
+                        "name": "Haas K."
+                    },
+                    {
+                        "name": "Hogg P.W."
+                    },
+                    {
+                        "name": "Hollman R.B."
+                    },
+                    {
+                        "name": "Kochhar S."
+                    },
+                    {
+                        "name": "Wild A.R."
+                    }
+                ],
+                "date": "2023-01-24T00:00:00Z",
+                "journal": "Frontiers in Physiology",
+                "title": "CellPalmSeq: A curated RNAseq database of palmitoylating and de-palmitoylating enzyme expression in human cell types and laboratory cell lines"
+            },
+            "pmcid": "PMC9904442",
+            "pmid": "36760531"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/cellsium/cellsium.biotools.json b/data/cellsium/cellsium.biotools.json
new file mode 100644
index 0000000000000..9488a023cad15
--- /dev/null
+++ b/data/cellsium/cellsium.biotools.json
@@ -0,0 +1,78 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T00:52:19.023875Z",
+    "biotoolsCURIE": "biotools:cellsium",
+    "biotoolsID": "cellsium",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Christian Carsten Sachs"
+        },
+        {
+            "name": "Karina Ruzaeva,"
+        },
+        {
+            "name": "Katharina Nöh",
+            "orcidid": "https://orcid.org/0000-0002-5407-2275"
+        }
+    ],
+    "description": "CellSium is a cell simulator developed for the primary application of generating realistically looking images of bacterial microcolonies, which may serve as ground truth for machine learning training processes.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://cellsium.readthedocs.io/en/latest/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/modsim/cellsium",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-17T00:52:19.026395Z",
+    "license": "BSD-2-Clause",
+    "name": "CellSium",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioadv/vbac053"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/cf-seq/cf-seq.biotools.json b/data/cf-seq/cf-seq.biotools.json
new file mode 100644
index 0000000000000..40b1edbd61977
--- /dev/null
+++ b/data/cf-seq/cf-seq.biotools.json
@@ -0,0 +1,148 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T01:07:55.838578Z",
+    "biotoolsCURIE": "biotools:cf-seq",
+    "biotoolsID": "cf-seq",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Bruce.A.Stanton@dartmouth.edu",
+            "name": "Bruce A. Stanton",
+            "orcidid": "http://orcid.org/0000-0002-1661-407X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Samuel L. Neff",
+            "orcidid": "http://orcid.org/0000-0002-5993-8445"
+        },
+        {
+            "name": "Thomas H. Hampton",
+            "orcidid": "http://orcid.org/0000-0003-0543-402X"
+        }
+    ],
+    "description": "Accessible Web Application for Rapid Re-Analysis of Cystic Fibrosis Pathogen RNA Sequencing Studies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Species name",
+                        "uri": "http://edamontology.org/data_1045"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Expression data visualisation",
+                    "uri": "http://edamontology.org/operation_0571"
+                },
+                {
+                    "term": "Filtering",
+                    "uri": "http://edamontology.org/operation_3695"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://scangeo.dartmouth.edu/CFSeq/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-17T01:07:55.840993Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/samlo777/cf-seq.git"
+        }
+    ],
+    "name": "CF-Seq",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/s41597-022-01431-1",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Researchers studying cystic fibrosis (CF) pathogens have produced numerous RNA-seq datasets which are available in the gene expression omnibus (GEO). Although these studies are publicly available, substantial computational expertise and manual effort are required to compare similar studies, visualize gene expression patterns within studies, and use published data to generate new experimental hypotheses. Furthermore, it is difficult to filter available studies by domain-relevant attributes such as strain, treatment, or media, or for a researcher to assess how a specific gene responds to various experimental conditions across studies. To reduce these barriers to data re-analysis, we have developed an R Shiny application called CF-Seq, which works with a compendium of 128 studies and 1,322 individual samples from 13 clinically relevant CF pathogens. The application allows users to filter studies by experimental factors and to view complex differential gene expression analyses at the click of a button. Here we present a series of use cases that demonstrate the application is a useful and efficient tool for new hypothesis generation. (CF-Seq: http://scangeo.dartmouth.edu/CFSeq/)",
+                "authors": [
+                    {
+                        "name": "Cengher L."
+                    },
+                    {
+                        "name": "Cheung A.L."
+                    },
+                    {
+                        "name": "Cramer R.A."
+                    },
+                    {
+                        "name": "Doing G."
+                    },
+                    {
+                        "name": "Hampton T.H."
+                    },
+                    {
+                        "name": "Hogan D.A."
+                    },
+                    {
+                        "name": "Koeppen K."
+                    },
+                    {
+                        "name": "Lee A.J."
+                    },
+                    {
+                        "name": "Neff S.L."
+                    },
+                    {
+                        "name": "Puerner C."
+                    },
+                    {
+                        "name": "Stanton B.A."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Data",
+                "title": "CF-Seq, an accessible web application for rapid re-analysis of cystic fibrosis pathogen RNA sequencing studies"
+            },
+            "pmcid": "PMC9203545",
+            "pmid": "35710652"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/cfa/cfa.biotools.json b/data/cfa/cfa.biotools.json
new file mode 100644
index 0000000000000..824ba79d18fa1
--- /dev/null
+++ b/data/cfa/cfa.biotools.json
@@ -0,0 +1,80 @@
+{
+    "additionDate": "2023-02-13T19:17:03.835834Z",
+    "biotoolsCURIE": "biotools:cfa",
+    "biotoolsID": "cfa",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Tzu-Hsien Yang"
+        }
+    ],
+    "description": "An explainable deep learning model for annotating the transcriptional roles of cis-regulatory modules based on epigenetic codes.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Promoter prediction",
+                    "uri": "http://edamontology.org/operation_0440"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/cobisLab/CFA/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T19:17:03.838419Z",
+    "license": "Apache-2.0",
+    "name": "CFA",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106375",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdMetazoa gene expression is controlled by modular DNA segments called cis-regulatory modules (CRMs). CRMs can convey promoter/enhancer/insulator roles, generating additional regulation layers in transcription. Experiments for understanding CRM roles are low-throughput and costly. Large-scale CRM function investigation still depends on computational methods. However, existing in silico tools only recognize enhancers or promoters exclusively, thus accumulating errors when considering CRM promoter/enhancer/insulator roles altogether. Currently, no algorithm can concurrently consider these CRM roles. In this research, we developed the CRM Function Annotator (CFA) model. CFA provides complete CRM transcriptional role labeling based on epigenetic profiling interpretation. We demonstrated that CFA achieves high performance (test macro auROC/auPRC = 94.1%/90.3%) and outperforms existing tools in promoter/enhancer/insulator identification. CFA is also inspected to recognize explainable epigenetic codes consistent with previous findings when labeling CRM roles. By considering the higher-order combinations of the epigenetic codes, CFA significantly reduces false-positive rates in CRM transcriptional role annotation. CFA is available at https://github.com/cobisLab/CFA/.",
+                "authors": [
+                    {
+                        "name": "Wu S.-H."
+                    },
+                    {
+                        "name": "Yang T.-H."
+                    },
+                    {
+                        "name": "Yu Y.-H."
+                    },
+                    {
+                        "name": "Zhang F.-Y."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "CFA: An explainable deep learning model for annotating the transcriptional roles of cis-regulatory modules based on epigenetic codes"
+            },
+            "pmid": "36502693"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/cg-diva/cg-diva.biotools.json b/data/cg-diva/cg-diva.biotools.json
new file mode 100644
index 0000000000000..50036a794ccce
--- /dev/null
+++ b/data/cg-diva/cg-diva.biotools.json
@@ -0,0 +1,96 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T14:08:34.564803Z",
+    "biotoolsCURIE": "biotools:cg-diva",
+    "biotoolsID": "cg-diva",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Manuel Eichenlaub"
+        }
+    ],
+    "description": "A collection of software packages for the statistical performance assessment of CGM systems.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/IfDTUlm/CGM_Performance_Assessment",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-01-27T14:08:34.567291Z",
+    "license": "MIT",
+    "name": "CG-DIVA",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1177/19322968221134639",
+            "metadata": {
+                "abstract": "© 2022 Diabetes Technology Society.Background: The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for “integrated” CGM systems. Methods: The statistical concept of tolerance intervals and data from two approved CGM systems was used to illustrate the CG-DIVA. Results: The CG-DIVA characterizes the expected range of deviations of the CGM system from a comparison method in different glucose concentration ranges and the variability of accuracy within and between sensors. The results of the CG-DIVA are visualized in an intuitive and straightforward graphical presentation. Compared with conventional accuracy characterizations, the CG-DIVA infers the expected accuracy of a CGM system and highlights important differences between CGM systems. Furthermore, it provides information on the incidence of large errors which are of particular clinical relevance. A software implementation of the CG-DIVA is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment). Conclusions: We argue that the CG-DIVA can simplify the discussion and comparison of CGM accuracy and could replace the high number of conventional approaches. Future adaptations of the approach could thus become a putative standard for the accuracy characterization of CGM systems and serve as the basis for the definition of future CGM performance requirements.",
+                "authors": [
+                    {
+                        "name": "Diem P."
+                    },
+                    {
+                        "name": "Eichenlaub M."
+                    },
+                    {
+                        "name": "Freckmann G."
+                    },
+                    {
+                        "name": "Haug C."
+                    },
+                    {
+                        "name": "Hinzmann R."
+                    },
+                    {
+                        "name": "Jendle J."
+                    },
+                    {
+                        "name": "Pleus S."
+                    },
+                    {
+                        "name": "Rothenbuhler M."
+                    },
+                    {
+                        "name": "Stephan P."
+                    },
+                    {
+                        "name": "Thomas A."
+                    },
+                    {
+                        "name": "Waldenmaier D."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Journal of Diabetes Science and Technology",
+                "title": "Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems"
+            },
+            "pmid": "36329636"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        }
+    ]
+}
diff --git a/data/cgar/cgar.biotools.json b/data/cgar/cgar.biotools.json
index 8ec7b2e58d408..7914803768a4e 100644
--- a/data/cgar/cgar.biotools.json
+++ b/data/cgar/cgar.biotools.json
@@ -3,7 +3,64 @@
     "biotoolsCURIE": "biotools:CGAR",
     "biotoolsID": "CGAR",
     "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "carles.hernandez-ferrer@childrens.harvard.edu",
+            "name": "Carles Hernandez-Ferrer",
+            "orcidid": "https://orcid.org/0000-0002-8029-7160",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ],
+            "url": "http://www.carleshf.com"
+        },
+        {
+            "email": "SekWon.Kong@childrens.harvard.edu",
+            "name": "Sek-Won Kong",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ]
+        },
+        {
+            "email": "Kenneth.Mandl@childrens.harvard.edu",
+            "name": "Kenneth D. Mandl",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ],
+            "url": "https://scholar.harvard.edu/mandl/home"
+        },
+        {
+            "name": "In-Hee Lee",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Developer"
+            ]
+        },
+        {
+            "name": "Jose A. Negron",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Developer"
+            ]
+        }
+    ],
     "description": "An interactive web application for prioritizing clinically implicated variants from genome sequencing data with ancestry composition.\n\nClinical Genome & Ancestry Report (CGAR) enables users to identify and prioritize phenotype-associated variants from genome sequencing with a user-friendly and interactive online platform.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://cgar-doc.readthedocs.io/en/latest/"
+        }
+    ],
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://bitbucket.org/gnome_pipeline/cgar_pub"
+        }
+    ],
     "editPermission": {
         "type": "public"
     },
@@ -30,13 +87,19 @@
         }
     ],
     "homepage": "https://tom.tch.harvard.edu/apps/cgar/",
-    "lastUpdate": "2020-12-10T16:04:07Z",
+    "lastUpdate": "2023-02-06T10:57:50.456702Z",
     "link": [
         {
             "type": [
                 "Repository"
             ],
             "url": "https://bitbucket.org/gnome_pipeline/cgar_pub"
+        },
+        {
+            "type": [
+                "Service"
+            ],
+            "url": "https://tom.tch.harvard.edu/apps/cgar/"
         }
     ],
     "name": "Clinical Genome and Ancestry Report (CGAR)",
diff --git a/data/cheap/cheap.biotools.json b/data/cheap/cheap.biotools.json
new file mode 100644
index 0000000000000..a6f3bc70add74
--- /dev/null
+++ b/data/cheap/cheap.biotools.json
@@ -0,0 +1,110 @@
+{
+    "additionDate": "2023-02-13T19:21:10.027996Z",
+    "biotoolsCURIE": "biotools:cheap",
+    "biotoolsID": "cheap",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dkim@unist.ac.kr",
+            "name": "Donghyuk Kim",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "ChIP-exo Analysis Pipeline (ChEAP) that executes the one-step process, starting from trimming and aligning raw sequencing reads to visualization of ChIP-exo results.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Peak calling",
+                    "uri": "http://edamontology.org/operation_3222"
+                },
+                {
+                    "term": "Protein-nucleic acid interaction analysis",
+                    "uri": "http://edamontology.org/operation_0389"
+                },
+                {
+                    "term": "Sequence motif discovery",
+                    "uri": "http://edamontology.org/operation_0238"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/SBML-Kimlab/ChEAP",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T19:21:10.030873Z",
+    "license": "Not licensed",
+    "name": "ChEAP",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.11.053",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsGenome-scale studies of the bacterial regulatory network have been leveraged by declining sequencing cost and advances in ChIP (chromatin immunoprecipitation) methods. Of which, ChIP-exo has proven competent with its near-single base-pair resolution. While several algorithms and programs have been developed for different analytical steps in ChIP-exo data processing, there is a lack of effort in incorporating them into a convenient bioinformatics pipeline that is intuitive and publicly available. In this paper, we developed ChIP-exo Analysis Pipeline (ChEAP) that executes the one-step process, starting from trimming and aligning raw sequencing reads to visualization of ChIP-exo results. The pipeline was implemented on the interactive web-based Python development environment – Jupyter Notebook, which is compatible with the Google Colab cloud platform to facilitate the sharing of codes and collaboration among researchers. Additionally, users could exploit the free GPU and CPU resources allocated by Colab to carry out computing tasks regardless of the performance of their local machines. The utility of ChEAP was demonstrated with the ChIP-exo datasets of RpoN sigma factor in E. coli K-12 MG1655. To analyze two raw data files, ChEAP runtime was 2 min and 25 s. Subsequent analyses identified 113 RpoN binding sites showing a conserved RpoN binding pattern in the motif search. ChEAP application in ChIP-exo data analysis is extensive and flexible for the parallel processing of data from various organisms.",
+                "authors": [
+                    {
+                        "name": "Bang I."
+                    },
+                    {
+                        "name": "Khanh Nong L."
+                    },
+                    {
+                        "name": "Kim D."
+                    },
+                    {
+                        "name": "Mok Lee S."
+                    },
+                    {
+                        "name": "Thi Le H."
+                    },
+                    {
+                        "name": "Young Park J."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "ChEAP: ChIP-exo analysis pipeline and the investigation of Escherichia coli RpoN protein-DNA interactions"
+            },
+            "pmcid": "PMC9735260",
+            "pmid": "36544470"
+        }
+    ],
+    "toolType": [
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-on-chip",
+            "uri": "http://edamontology.org/topic_3179"
+        },
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/chemical-sa-bilstm/chemical-sa-bilstm.biotools.json b/data/chemical-sa-bilstm/chemical-sa-bilstm.biotools.json
new file mode 100644
index 0000000000000..cdcdf6b24c240
--- /dev/null
+++ b/data/chemical-sa-bilstm/chemical-sa-bilstm.biotools.json
@@ -0,0 +1,93 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T19:26:08.687518Z",
+    "biotoolsCURIE": "biotools:chemical-sa-bilstm",
+    "biotoolsID": "chemical-sa-bilstm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "gnxo@163.com",
+            "name": "Xiao Guan",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Grain protein function prediction based on self-attention mechanism and bidirectional LSTM.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein function prediction",
+                    "uri": "http://edamontology.org/operation_1777"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                },
+                {
+                    "term": "Protein structure prediction",
+                    "uri": "http://edamontology.org/operation_0474"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/HwaTong/Chemical-SA-BiLSTM",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T19:26:08.690535Z",
+    "license": "Not licensed",
+    "name": "Chemical-SA-BiLSTM",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC493",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.With the development of genome sequencing technology, using computing technology to predict grain protein function has become one of the important tasks of bioinformatics. The protein data of four grains, soybean, maize, indica and japonica are selected in this experimental dataset. In this paper, a novel neural network algorithm Chemical-SA-BiLSTM is proposed for grain protein function prediction. The Chemical-SA-BiLSTM algorithm fuses the chemical properties of proteins on the basis of amino acid sequences, and combines the self-attention mechanism with the bidirectional Long Short-Term Memory network. The experimental results show that the Chemical-SA-BiLSTM algorithm is superior to other classical neural network algorithms, and can more accurately predict the protein function, which proves the effectiveness of the Chemical-SA-BiLSTM algorithm in the prediction of grain protein function. The source code of our method is available at https://github.com/HwaTong/Chemical-SA-BiLSTM.",
+                "authors": [
+                    {
+                        "name": "Guan X."
+                    },
+                    {
+                        "name": "Liu J."
+                    },
+                    {
+                        "name": "Tang X."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "Grain protein function prediction based on self-attention mechanism and bidirectional LSTM"
+            },
+            "pmid": "36567619"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Function analysis",
+            "uri": "http://edamontology.org/topic_1775"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/chemistry42/chemistry42.biotools.json b/data/chemistry42/chemistry42.biotools.json
new file mode 100644
index 0000000000000..c81c5eaf2c4d2
--- /dev/null
+++ b/data/chemistry42/chemistry42.biotools.json
@@ -0,0 +1,126 @@
+{
+    "additionDate": "2023-03-09T14:14:09.371669Z",
+    "biotoolsCURIE": "biotools:chemistry42",
+    "biotoolsID": "chemistry42",
+    "confidence_flag": "tool",
+    "cost": "Free of charge (with restrictions)",
+    "credit": [
+        {
+            "email": "alex@insilico.com",
+            "name": "Alex Zhavoronkov",
+            "orcidid": "https://orcid.org/0000-0001-7067-8966",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Chemistry42 is a software platform for de novo small molecule design and optimization that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methodologies. Chemistry42 efficiently generates novel molecular structures with optimized properties validated in both in vitro and in vivo studies and is available through licensing or collaboration.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Backbone modelling",
+                    "uri": "http://edamontology.org/operation_0479"
+                },
+                {
+                    "term": "Molecular docking",
+                    "uri": "http://edamontology.org/operation_0478"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://chemistry42.com",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-09T14:14:09.375834Z",
+    "license": "Other",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/insilicomedicine/GENTRL"
+        }
+    ],
+    "name": "Chemistry42",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCIM.2C01191",
+            "metadata": {
+                "abstract": "Chemistry42 is a software platform for de novo small molecule design and optimization that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methodologies. Chemistry42 efficiently generates novel molecular structures with optimized properties validated in both in vitro and in vivo studies and is available through licensing or collaboration. Chemistry42 is the core component of Insilico Medicine’s Pharma.ai drug discovery suite. Pharma.ai also includes PandaOmics for target discovery and multiomics data analysis, and inClinico─a data-driven multimodal forecast of a clinical trial’s probability of success (PoS). In this paper, we demonstrate how the platform can be used to efficiently find novel molecular structures against DDR1 and CDK20.",
+                "authors": [
+                    {
+                        "name": "Aladinskiy V."
+                    },
+                    {
+                        "name": "Aliper A."
+                    },
+                    {
+                        "name": "Bezrukov D."
+                    },
+                    {
+                        "name": "Ivanenkov Y.A."
+                    },
+                    {
+                        "name": "Kamya P."
+                    },
+                    {
+                        "name": "Polykovskiy D."
+                    },
+                    {
+                        "name": "Ren F."
+                    },
+                    {
+                        "name": "Zagribelnyy B."
+                    },
+                    {
+                        "name": "Zhavoronkov A."
+                    }
+                ],
+                "date": "2023-02-13T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "Chemistry42: An AI-Driven Platform for Molecular Design and Optimization"
+            },
+            "pmcid": "PMC9930109",
+            "pmid": "36728505"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/chempert/chempert.biotools.json b/data/chempert/chempert.biotools.json
new file mode 100644
index 0000000000000..d64870cfb813d
--- /dev/null
+++ b/data/chempert/chempert.biotools.json
@@ -0,0 +1,128 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-11T13:17:44.504617Z",
+    "biotoolsCURIE": "biotools:chempert",
+    "biotoolsID": "chempert",
+    "collectionID": [
+        "LCSB-CBG"
+    ],
+    "credit": [
+        {
+            "email": "antonio.delsol@uni.lu",
+            "name": "Antonio del Sol",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ],
+            "url": "https://wwwen.uni.lu/lcsb/people/antonio_del_sol_mesa"
+        }
+    ],
+    "description": "Mapping between chemical perturbation and transcriptional response for non-cancer cells",
+    "documentation": [
+        {
+            "type": [
+                "Quick start guide"
+            ],
+            "url": "https://chempert.uni.lu/information"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://git-r3lab.uni.lu/CBG/chempert"
+        }
+    ],
+    "editPermission": {
+        "type": "group"
+    },
+    "elixirNode": [
+        "Luxembourg"
+    ],
+    "elixirPlatform": [
+        "Tools"
+    ],
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Compound name",
+                        "uri": "http://edamontology.org/data_0990"
+                    },
+                    "format": [
+                        {
+                            "term": "plain text format (unformatted)",
+                            "uri": "http://edamontology.org/format_1964"
+                        }
+                    ]
+                },
+                {
+                    "data": {
+                        "term": "Expression data",
+                        "uri": "http://edamontology.org/data_2603"
+                    },
+                    "format": [
+                        {
+                            "term": "TSV",
+                            "uri": "http://edamontology.org/format_3475"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Modelling and simulation",
+                    "uri": "http://edamontology.org/operation_2426"
+                },
+                {
+                    "term": "Prediction and recognition",
+                    "uri": "http://edamontology.org/operation_2423"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://chempert.uni.lu/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-11T13:17:44.507334Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://git-r3lab.uni.lu/CBG/chempert"
+        }
+    ],
+    "name": "ChemPert",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "KartikeyaS",
+    "publication": [
+        {
+            "doi": "10.1093/nar/gkac862",
+            "pmcid": "PMC9825489",
+            "pmid": "36200827",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Data identity and mapping",
+            "uri": "http://edamontology.org/topic_3345"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/chimerax/chimerax.biotools.json b/data/chimerax/chimerax.biotools.json
index a67d95e0d465a..15764aef3f8c7 100644
--- a/data/chimerax/chimerax.biotools.json
+++ b/data/chimerax/chimerax.biotools.json
@@ -55,7 +55,7 @@
         }
     ],
     "homepage": "https://www.rbvi.ucsf.edu/chimerax",
-    "lastUpdate": "2022-07-12T14:22:12.012492Z",
+    "lastUpdate": "2023-02-28T19:32:31.068061Z",
     "link": [
         {
             "type": [
@@ -97,7 +97,7 @@
                         "name": "Pettersen E.F."
                     }
                 ],
-                "citationCount": 637,
+                "citationCount": 1348,
                 "date": "2021-01-01T00:00:00Z",
                 "journal": "Protein Science",
                 "title": "UCSF ChimeraX: Structure visualization for researchers, educators, and developers"
@@ -132,7 +132,7 @@
                         "name": "Pettersen E.F."
                     }
                 ],
-                "citationCount": 1263,
+                "citationCount": 1606,
                 "date": "2018-01-01T00:00:00Z",
                 "journal": "Protein Science",
                 "title": "UCSF ChimeraX: Meeting modern challenges in visualization and analysis"
@@ -140,6 +140,12 @@
             "pmid": "28710774"
         }
     ],
+    "relation": [
+        {
+            "biotoolsID": "artiax",
+            "type": "includes"
+        }
+    ],
     "topic": [
         {
             "term": "Imaging",
diff --git a/data/chipbase/chipbase.biotools.json b/data/chipbase/chipbase.biotools.json
new file mode 100644
index 0000000000000..637e7b9ba0dd7
--- /dev/null
+++ b/data/chipbase/chipbase.biotools.json
@@ -0,0 +1,157 @@
+{
+    "additionDate": "2023-01-27T14:13:11.402325Z",
+    "biotoolsCURIE": "biotools:chipbase",
+    "biotoolsID": "chipbase",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "kzhou@coh.org",
+            "name": "Keren Zhou",
+            "orcidid": "https://orcid.org/0000-0001-7905-3399",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "yangjh7@mail.sysu.edu.cn",
+            "name": "Jianhua Yang",
+            "orcidid": "https://orcid.org/0000-0003-3863-2786",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "libin73@mail.sysu.edu.cn",
+            "name": "Bin Li",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "liushr27@mail.sysu.edu.cn",
+            "name": "Shurong Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Lianghu Qu",
+            "orcidid": "https://orcid.org/0000-0003-3657-2863",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "The encyclopedia of transcriptional regulations of non-coding RNAs and protein-coding genes.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Gene regulatory network analysis",
+                    "uri": "http://edamontology.org/operation_1781"
+                },
+                {
+                    "term": "Gene regulatory network prediction",
+                    "uri": "http://edamontology.org/operation_2437"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://rnasysu.com/chipbase3/",
+    "lastUpdate": "2023-01-27T14:13:11.404763Z",
+    "license": "GPL-1.0",
+    "name": "ChIPBase",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1067",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Non-coding RNAs (ncRNAs) are emerging as key regulators of various biological processes. Although thousands of ncRNAs have been discovered, the transcriptional mechanisms and networks of the majority of ncRNAs have not been fully investigated. In this study, we updated ChIPBase to version 3.0 (https://rnasysu.com/chipbase3/) to provide the most comprehensive transcriptional regulation atlas of ncRNAs and protein-coding genes (PCGs). ChIPBase has identified ∼151 187 000 regulatory relationships between ∼171 600 genes and ∼3000 regulators by analyzing ∼55 000 ChIP-seq datasets, which represent a 30-fold expansion. Moreover, we de novo identified ∼29 000 motif matrices of transcription factors. In addition, we constructed a novel 'Enhancer' module to predict ∼1 837 200 regulation regions functioning as poised, active or super enhancers under ∼1300 conditions. Importantly, we constructed exhaustive coexpression maps between regulators and their target genes by integrating expression profiles of ∼65 000 normal and ∼15 000 tumor samples. We built a 'Disease' module to obtain an atlas of the disease-associated variations in the regulation regions of genes. We also constructed an 'EpiInter' module to explore potential interactions between epitranscriptome and epigenome. Finally, we designed 'Network' module to provide extensive and gene-centred regulatory networks. ChIPBase will serve as a useful resource to facilitate integrative explorations and expand our understanding of transcriptional regulation.",
+                "authors": [
+                    {
+                        "name": "Chen Z."
+                    },
+                    {
+                        "name": "Huang J."
+                    },
+                    {
+                        "name": "Huang Q."
+                    },
+                    {
+                        "name": "Li B."
+                    },
+                    {
+                        "name": "Lin Q."
+                    },
+                    {
+                        "name": "Liu C."
+                    },
+                    {
+                        "name": "Liu S."
+                    },
+                    {
+                        "name": "Qu L."
+                    },
+                    {
+                        "name": "Wu D."
+                    },
+                    {
+                        "name": "Xuan J."
+                    },
+                    {
+                        "name": "Yang J."
+                    },
+                    {
+                        "name": "Zhang P."
+                    },
+                    {
+                        "name": "Zheng L."
+                    },
+                    {
+                        "name": "Zheng W."
+                    },
+                    {
+                        "name": "Zhou K."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "ChIPBase v3.0: the encyclopedia of transcriptional regulations of non-coding RNAs and protein-coding genes"
+            },
+            "pmcid": "PMC9825553",
+            "pmid": "36399495"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        }
+    ],
+    "version": [
+        "3.0"
+    ]
+}
diff --git a/data/chromdmm/chromdmm.biotools.json b/data/chromdmm/chromdmm.biotools.json
new file mode 100644
index 0000000000000..a304fe94498c0
--- /dev/null
+++ b/data/chromdmm/chromdmm.biotools.json
@@ -0,0 +1,96 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T01:20:28.986399Z",
+    "biotoolsCURIE": "biotools:chromdmm",
+    "biotoolsID": "chromdmm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Gökçen Eraslan"
+        },
+        {
+            "name": "Harri Lähdesmäki"
+        },
+        {
+            "name": "Maria Osmala",
+            "orcidid": "http://orcid.org/0000-0003-0128-4896"
+        }
+    ],
+    "description": "A Dirichlet-Multinomial Mixture Model For Clustering Heterogeneous Epigenetic Data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Transcriptional regulatory element prediction",
+                    "uri": "http://edamontology.org/operation_0438"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MariaOsmala/ChromDMM",
+    "language": [
+        "C++",
+        "R"
+    ],
+    "lastUpdate": "2023-01-17T01:20:28.989666Z",
+    "license": "LGPL-3.0",
+    "name": "ChromDMM",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac444",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press.Motivation: Research on epigenetic modifications and other chromatin features at genomic regulatory elements elucidates essential biological mechanisms including the regulation of gene expression. Despite the growing number of epigenetic datasets, new tools are still needed to discover novel distinctive patterns of heterogeneous epigenetic signals at regulatory elements. Results: We introduce ChromDMM, a product Dirichlet-multinomial mixture model for clustering genomic regions that are characterized by multiple chromatin features. ChromDMM extends the mixture model framework by profile shifting and flipping that can probabilistically account for inaccuracies in the position and strand-orientation of the genomic regions. Owing to hyper-parameter optimization, ChromDMM can also regularize the smoothness of the epigenetic profiles across the consecutive genomic regions. With simulated data, we demonstrate that ChromDMM clusters, shifts and strand-orients the profiles more accurately than previous methods. With ENCODE data, we show that the clustering of enhancer regions in the human genome reveals distinct patterns in several chromatin features. We further validate the enhancer clusters by their enrichment for transcriptional regulatory factor binding sites.",
+                "authors": [
+                    {
+                        "name": "Eraslan G."
+                    },
+                    {
+                        "name": "Lahdesmaki H."
+                    },
+                    {
+                        "name": "Osmala M."
+                    }
+                ],
+                "date": "2022-08-15T00:00:00Z",
+                "journal": "Bioinformatics",
+                "title": "ChromDMM: a Dirichlet-multinomial mixture model for clustering heterogeneous epigenetic data"
+            },
+            "pmcid": "PMC9364382",
+            "pmid": "35786716"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Gene regulation",
+            "uri": "http://edamontology.org/topic_0204"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/cirdataset/cirdataset.biotools.json b/data/cirdataset/cirdataset.biotools.json
new file mode 100644
index 0000000000000..deb422b4d3e94
--- /dev/null
+++ b/data/cirdataset/cirdataset.biotools.json
@@ -0,0 +1,71 @@
+{
+    "additionDate": "2023-01-08T15:00:03.610431Z",
+    "biotoolsCURIE": "biotools:cirdataset",
+    "biotoolsID": "cirdataset",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "nadeems@mskcc.org",
+            "name": "Saad Nadeem",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "wookjin.choi@jefferson.edu",
+            "name": "Wookjin Choi",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A large-scale Dataset for Clinically-Interpretable lung nodule Radiomics and malignancy prediction.",
+    "editPermission": {
+        "type": "public"
+    },
+    "homepage": "https://github.com/nadeemlab/CIR",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-08T15:00:03.613987Z",
+    "license": "Not licensed",
+    "name": "CIRDataset",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1007/978-3-031-16443-9_2",
+            "metadata": {
+                "abstract": "© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice assessment by radiologists, manual spiculation/lobulation annotation is a tedious task and thus no public datasets exist to date for probing the importance of these clinically-reported features in the SOTA malignancy prediction algorithms. As part of this paper, we release a large-scale Clinically-Interpretable Radiomics Dataset, CIRDataset, containing 956 radiologist QA/QC’ed spiculation/lobulation annotations on segmented lung nodules from two public datasets, LIDC-IDRI (N = 883) and LUNGx (N = 73). We also present an end-to-end deep learning model based on multi-class Voxel2Mesh extension to segment nodules (while preserving spikes), classify spikes (sharp/spiculation and curved/lobulation), and perform malignancy prediction. Previous methods have performed malignancy prediction for LIDC and LUNGx datasets but without robust attribution to any clinically reported/actionable features (due to known hyperparameter sensitivity issues with general attribution schemes). With the release of this comprehensively-annotated CIRDataset and end-to-end deep learning baseline, we hope that malignancy prediction methods can validate their explanations, benchmark against our baseline, and provide clinically-actionable insights. Dataset, code, pretrained models, and docker containers are available at https://github.com/nadeemlab/CIR.",
+                "authors": [
+                    {
+                        "name": "Choi W."
+                    },
+                    {
+                        "name": "Dahiya N."
+                    },
+                    {
+                        "name": "Nadeem S."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
+                "title": "CIRDataset: A Large-Scale Dataset for Clinically-Interpretable Lung Nodule Radiomics and Malignancy Prediction"
+            },
+            "pmcid": "PMC9527770",
+            "pmid": "36198166"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Probes and primers",
+            "uri": "http://edamontology.org/topic_0632"
+        }
+    ]
+}
diff --git a/data/citedb/citedb.biotools.json b/data/citedb/citedb.biotools.json
new file mode 100644
index 0000000000000..9530a383c9691
--- /dev/null
+++ b/data/citedb/citedb.biotools.json
@@ -0,0 +1,130 @@
+{
+    "additionDate": "2023-01-08T15:04:58.707694Z",
+    "biotoolsCURIE": "biotools:citedb",
+    "biotoolsID": "citedb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "houl@tsinghua.edu.cn",
+            "name": "Lin Hou",
+            "orcidid": "https://orcid.org/0000-0002-4283-8501",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A manually curated database of cell-cell interactions in human.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Text annotation",
+                    "uri": "http://edamontology.org/operation_3778"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://citedb.cn/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-08T15:04:58.710536Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/shanny01/benchmark"
+        }
+    ],
+    "name": "CITEdb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC654",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The interactions among various types of cells play critical roles in cell functions and the maintenance of the entire organism. While cell-cell interactions are traditionally revealed from experimental studies, recent developments in single-cell technologies combined with data mining methods have enabled computational prediction of cell-cell interactions, which have broadened our understanding of how cells work together, and have important implications in therapeutic interventions targeting cell-cell interactions for cancers and other diseases. Despite the importance, to our knowledge, there is no database for systematic documentation of high-quality cell-cell interactions at the cell type level, which hinders the development of computational approaches to identify cell-cell interactions. RESULTS: We develop a publicly accessible database, CITEdb (Cell-cell InTEraction database, https://citedb.cn/), which not only facilitates interactive exploration of cell-cell interactions in specific physiological contexts (e.g. a disease or an organ) but also provides a benchmark dataset to interpret and evaluate computationally derived cell-cell interactions from different tools. CITEdb contains 728 pairs of cell-cell interactions in human that are manually curated. Each interaction is equipped with structured annotations including the physiological context, the ligand-receptor pairs that mediate the interaction, etc. Our database provides a web interface to search, visualize and download cell-cell interactions. Users can search for cell-cell interactions by selecting the physiological context of interest or specific cell types involved. CITEdb is the first attempt to catalogue cell-cell interactions at the cell type level, which is beneficial to both experimental, computational and clinical studies of cell-cell interactions. AVAILABILITY AND IMPLEMENTATION: CITEdb is freely available at https://citedb.cn/ and the R package implementing benchmark is available at https://github.com/shanny01/benchmark. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Gao J."
+                    },
+                    {
+                        "name": "Guo H."
+                    },
+                    {
+                        "name": "Hou L."
+                    },
+                    {
+                        "name": "Jiang J."
+                    },
+                    {
+                        "name": "Li D."
+                    },
+                    {
+                        "name": "Lu Y."
+                    },
+                    {
+                        "name": "Ren Y."
+                    },
+                    {
+                        "name": "Shan N."
+                    },
+                    {
+                        "name": "Yan L."
+                    },
+                    {
+                        "name": "Zhao X."
+                    }
+                ],
+                "date": "2022-11-15T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "CITEdb: a manually curated database of cell-cell interactions in human"
+            },
+            "pmcid": "PMC9665858",
+            "pmid": "36179089"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Library",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Data mining",
+            "uri": "http://edamontology.org/topic_3473"
+        },
+        {
+            "term": "Data submission, annotation and curation",
+            "uri": "http://edamontology.org/topic_0219"
+        },
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        }
+    ]
+}
diff --git a/data/citrus/citrus.biotools.json b/data/citrus/citrus.biotools.json
new file mode 100644
index 0000000000000..1f062a91a8a31
--- /dev/null
+++ b/data/citrus/citrus.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-08T15:10:03.022697Z",
+    "biotoolsCURIE": "biotools:citrus",
+    "biotoolsID": "citrus",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "osmanbeyogluhu@pitt.edu",
+            "name": "Hatice Ulku Osmanbeyoglu",
+            "orcidid": "https://orcid.org/0000-0002-3175-1777",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/osmanbeyoglulab/CITRUS",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-08T15:10:03.027091Z",
+    "license": "MIT",
+    "name": "CITRUS",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC881",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Interpreting somatic alterations within context-specific transcriptional programs will facilitate personalized therapeutic decisions but is a monumental task. Toward this goal, we develop a partially interpretable neural network model called Chromatin-informed Inference of Transcriptional Regulators Using Self-attention mechanism (CITRUS). CITRUS models the impact of somatic alterations on transcription factors and downstream transcriptional programs. Our approach employs a self-attention mechanism to model the contextual impact of somatic alterations. Furthermore, CITRUS uses a layer of hidden nodes to explicitly represent the state of transcription factors (TFs) to learn the relationships between TFs and their target genes based on TF binding motifs in the open chromatin regions of tumor samples. We apply CITRUS to genomic, transcriptomic, and epigenomic data from 17 cancer types profiled by The Cancer Genome Atlas. CITRUS predicts patient-specific TF activities and reveals transcriptional program variations between and within tumor types. We show that CITRUS yields biological insights into delineating TFs associated with somatic alterations in individual tumors. Thus, CITRUS is a promising tool for precision oncology.",
+                "authors": [
+                    {
+                        "name": "Lu X."
+                    },
+                    {
+                        "name": "Ma X."
+                    },
+                    {
+                        "name": "Osmanbeyoglu H.U."
+                    },
+                    {
+                        "name": "Palmer D."
+                    },
+                    {
+                        "name": "Schwartz R."
+                    },
+                    {
+                        "name": "Tao Y."
+                    }
+                ],
+                "date": "2022-10-28T00:00:00Z",
+                "journal": "Nucleic Acids Research",
+                "title": "Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers"
+            },
+            "pmcid": "PMC9638905",
+            "pmid": "36243974"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/civic/civic.biotools.json b/data/civic/civic.biotools.json
new file mode 100644
index 0000000000000..8928b84c3745b
--- /dev/null
+++ b/data/civic/civic.biotools.json
@@ -0,0 +1,332 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T14:20:17.081666Z",
+    "biotoolsCURIE": "biotools:civic",
+    "biotoolsID": "civic",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "kkrysiak@wustl.edu",
+            "name": "Kilannin Krysiak",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "mgriffit@wustl.edu",
+            "name": "Malachi Griffith",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "obigriffith@wustl.edu",
+            "name": "Obi L Griffith",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Text mining",
+                    "uri": "http://edamontology.org/operation_0306"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "Variant filtering",
+                    "uri": "http://edamontology.org/operation_3675"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://civicdb.org",
+    "language": [
+        "Ruby"
+    ],
+    "lastUpdate": "2023-01-27T14:20:17.084081Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "http://bionlp.bcgsc.ca/civicmine/"
+        },
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://civicdb.org/releases"
+        },
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://civicdb.org/releases/main"
+        },
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://griffithlab.github.io/civic-v2/"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/griffithlab/civic-v2"
+        }
+    ],
+    "name": "CIViC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC979",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC's functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications.",
+                "authors": [
+                    {
+                        "name": "Ainscough B.J."
+                    },
+                    {
+                        "name": "Andric V."
+                    },
+                    {
+                        "name": "Barnell E.K."
+                    },
+                    {
+                        "name": "Campbell K.M."
+                    },
+                    {
+                        "name": "Chiorean A."
+                    },
+                    {
+                        "name": "Clark K.A."
+                    },
+                    {
+                        "name": "Coffman A.C."
+                    },
+                    {
+                        "name": "Corson L.B."
+                    },
+                    {
+                        "name": "Cotto K.C."
+                    },
+                    {
+                        "name": "Danos A.M."
+                    },
+                    {
+                        "name": "Delong S."
+                    },
+                    {
+                        "name": "Evans M."
+                    },
+                    {
+                        "name": "Farncombe K.M."
+                    },
+                    {
+                        "name": "Giles R.H."
+                    },
+                    {
+                        "name": "Griffith M."
+                    },
+                    {
+                        "name": "Griffith O.L."
+                    },
+                    {
+                        "name": "Grisdale C.J."
+                    },
+                    {
+                        "name": "Hoang M.H."
+                    },
+                    {
+                        "name": "Horak P."
+                    },
+                    {
+                        "name": "Jani P."
+                    },
+                    {
+                        "name": "Ji J."
+                    },
+                    {
+                        "name": "Jones S.J.M."
+                    },
+                    {
+                        "name": "Kanagal-Shamanna R."
+                    },
+                    {
+                        "name": "Kesserwan C."
+                    },
+                    {
+                        "name": "Khanfar M."
+                    },
+                    {
+                        "name": "Kim R.H."
+                    },
+                    {
+                        "name": "King I."
+                    },
+                    {
+                        "name": "Kiwala S."
+                    },
+                    {
+                        "name": "Krysiak K."
+                    },
+                    {
+                        "name": "Kujan L."
+                    },
+                    {
+                        "name": "Lamping M."
+                    },
+                    {
+                        "name": "Lever J."
+                    },
+                    {
+                        "name": "Li B.V."
+                    },
+                    {
+                        "name": "Lin W.-H."
+                    },
+                    {
+                        "name": "Madhavan S."
+                    },
+                    {
+                        "name": "Mardis E.R."
+                    },
+                    {
+                        "name": "Marr A.R."
+                    },
+                    {
+                        "name": "McMichael J.F."
+                    },
+                    {
+                        "name": "Milosavljevic A."
+                    },
+                    {
+                        "name": "Patel R.Y."
+                    },
+                    {
+                        "name": "Pema S."
+                    },
+                    {
+                        "name": "Raca G."
+                    },
+                    {
+                        "name": "Rao S."
+                    },
+                    {
+                        "name": "Reisle C."
+                    },
+                    {
+                        "name": "Ridd S."
+                    },
+                    {
+                        "name": "Rieke D.T."
+                    },
+                    {
+                        "name": "Ritter D.I."
+                    },
+                    {
+                        "name": "Salama Y."
+                    },
+                    {
+                        "name": "Saliba J."
+                    },
+                    {
+                        "name": "Schriml L.M."
+                    },
+                    {
+                        "name": "Shen H."
+                    },
+                    {
+                        "name": "Sheta L."
+                    },
+                    {
+                        "name": "Singhal K."
+                    },
+                    {
+                        "name": "Skidmore Z.L."
+                    },
+                    {
+                        "name": "Spies N.C."
+                    },
+                    {
+                        "name": "Suda A."
+                    },
+                    {
+                        "name": "Takahashi H."
+                    },
+                    {
+                        "name": "Terraf P."
+                    },
+                    {
+                        "name": "Venigalla A.C."
+                    },
+                    {
+                        "name": "Wagner A.H."
+                    },
+                    {
+                        "name": "Walker J.R."
+                    },
+                    {
+                        "name": "Xu X."
+                    },
+                    {
+                        "name": "Zhang J."
+                    },
+                    {
+                        "name": "Zhao X."
+                    },
+                    {
+                        "name": "Zhou X."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase"
+            },
+            "pmcid": "PMC9825608",
+            "pmid": "36373660"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        }
+    ]
+}
diff --git a/data/clair3-trio/clair3-trio.biotools.json b/data/clair3-trio/clair3-trio.biotools.json
new file mode 100644
index 0000000000000..6143cc8ed167d
--- /dev/null
+++ b/data/clair3-trio/clair3-trio.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-22T01:12:25.794991Z",
+    "biotoolsCURIE": "biotools:clair3-trio",
+    "biotoolsID": "clair3-trio",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Syed Shakeel Ahmed"
+        },
+        {
+            "name": "Junhao Su",
+            "orcidid": "http://orcid.org/0000-0002-8560-3999"
+        },
+        {
+            "name": "Ruibang Luo",
+            "orcidid": "http://orcid.org/0000-0001-9711-6533"
+        },
+        {
+            "name": "Tak-Wah Lam",
+            "orcidid": "http://orcid.org/0000-0003-4676-8587"
+        },
+        {
+            "name": "Zhenxian Zheng",
+            "orcidid": "http://orcid.org/0000-0002-6546-2324"
+        }
+    ],
+    "description": "High-performance Nanopore long-read variant calling in family trios with Trio-to-Trio deep neural networks.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/HKU-BAL/Clair3-Trio",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-22T01:12:25.799862Z",
+    "license": "BSD-3-Clause",
+    "name": "Clair3-Trio",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bib/bbac301",
+            "metadata": {
+                "abstract": "© 2022 The Author(s).Accurate identification of genetic variants from family child-mother-father trio sequencing data is important in genomics. However, state-of-the-art approaches treat variant calling from trios as three independent tasks, which limits their calling accuracy for Nanopore long-read sequencing data. For better trio variant calling, we introduce Clair3-Trio, the first variant caller tailored for family trio data from Nanopore long-reads. Clair3-Trio employs a Trio-to-Trio deep neural network model, which allows it to input the trio sequencing information and output all of the trio's predicted variants within a single model to improve variant calling. We also present MCVLoss, a novel loss function tailor-made for variant calling in trios, leveraging the explicit encoding of the Mendelian inheritance. Clair3-Trio showed comprehensive improvement in experiments. It predicted far fewer Mendelian inheritance violation variations than current state-of-the-art methods. We also demonstrated that our Trio-to-Trio model is more accurate than competing architectures. Clair3-Trio is accessible as a free, open-source project at https://github.com/HKU-BAL/Clair3-Trio.",
+                "authors": [
+                    {
+                        "name": "Ahmed S.S."
+                    },
+                    {
+                        "name": "Lam T.-W."
+                    },
+                    {
+                        "name": "Luo R."
+                    },
+                    {
+                        "name": "Su J."
+                    },
+                    {
+                        "name": "Zheng Z."
+                    }
+                ],
+                "date": "2022-09-01T00:00:00Z",
+                "journal": "Briefings in Bioinformatics",
+                "title": "Clair3-trio: High-performance Nanopore long-read variant calling in family trios with trio-to-trio deep neural networks"
+            },
+            "pmcid": "PMC9487642",
+            "pmid": "35849103"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genetics",
+            "uri": "http://edamontology.org/topic_3053"
+        },
+        {
+            "term": "Laboratory techniques",
+            "uri": "http://edamontology.org/topic_3361"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/clam/clam.biotools.json b/data/clam/clam.biotools.json
new file mode 100644
index 0000000000000..6e9fc181b8c39
--- /dev/null
+++ b/data/clam/clam.biotools.json
@@ -0,0 +1,108 @@
+{
+    "additionDate": "2023-03-09T14:19:17.072698Z",
+    "biotoolsCURIE": "biotools:clam",
+    "biotoolsID": "clam",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "zhuyunping@ncpsb.org.cn",
+            "name": "Yunping Zhu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "CLAM is an analytical framework for detecting co-regulated gene modules by integrating multi-omics data and known molecular interactions.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Weighted correlation network analysis",
+                    "uri": "http://edamontology.org/operation_3766"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/free1234hm/CLAM",
+    "language": [
+        "Java"
+    ],
+    "lastUpdate": "2023-03-09T14:19:17.079511Z",
+    "license": "GPL-3.0",
+    "name": "CLAM",
+    "operatingSystem": [
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FGENE.2023.1082032",
+            "metadata": {
+                "abstract": "Multi-omics data integration has emerged as a promising approach to identify patient subgroups. However, in terms of grouping genes (or gene products) into co-expression modules, data integration methods suffer from two main drawbacks. First, most existing methods only consider genes or samples measured in all different datasets. Second, known molecular interactions (e.g., transcriptional regulatory interactions, protein–protein interactions and biological pathways) cannot be utilized to assist in module detection. Herein, we present a novel data integration framework, Correlation-based Local Approximation of Membership (CLAM), which provides two methodological innovations to address these limitations: 1) constructing a trans-omics neighborhood matrix by integrating multi-omics datasets and known molecular interactions, and 2) using a local approximation procedure to define gene modules from the matrix. Applying Correlation-based Local Approximation of Membership to human colorectal cancer (CRC) and mouse B-cell differentiation multi-omics data obtained from The Cancer Genome Atlas (TCGA), Clinical Proteomics Tumor Analysis Consortium (CPTAC), Gene Expression Omnibus (GEO) and ProteomeXchange database, we demonstrated its superior ability to recover biologically relevant modules and gene ontology (GO) terms. Further investigation of the colorectal cancer modules revealed numerous transcription factors and KEGG pathways that played crucial roles in colorectal cancer progression. Module-based survival analysis constructed four survival-related networks in which pairwise gene correlations were significantly correlated with colorectal cancer patient survival. Overall, the series of evaluations demonstrated the great potential of Correlation-based Local Approximation of Membership for identifying modular biomarkers for complex diseases. We implemented Correlation-based Local Approximation of Membership as a user-friendly application available at https://github.com/free1234hm/CLAM.",
+                "authors": [
+                    {
+                        "name": "Chen X."
+                    },
+                    {
+                        "name": "Han M."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Zhang J."
+                    },
+                    {
+                        "name": "Zhu Y."
+                    }
+                ],
+                "date": "2023-01-24T00:00:00Z",
+                "journal": "Frontiers in Genetics",
+                "title": "Identification of functional gene modules by integrating multi-omics data and known molecular interactions"
+            },
+            "pmcid": "PMC9902936",
+            "pmid": "36760999"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/clampfish/clampfish.biotools.json b/data/clampfish/clampfish.biotools.json
new file mode 100644
index 0000000000000..eba9329b6442f
--- /dev/null
+++ b/data/clampfish/clampfish.biotools.json
@@ -0,0 +1,123 @@
+{
+    "additionDate": "2023-01-08T15:16:37.330819Z",
+    "biotoolsCURIE": "biotools:clampfish",
+    "biotoolsID": "clampfish",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Ian Dardani"
+        }
+    ],
+    "description": "clampFISH 2.0, a method that uses an inverted padlock design to efficiently detect many RNA species and exponentially amplify their signals at once, while also reducing the time and cost compared with the prior clampFISH method.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Amplification detection",
+                    "uri": "http://edamontology.org/operation_3965"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Primer and probe design",
+                    "uri": "http://edamontology.org/operation_2419"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/iandarr/clampFISH2allcode",
+    "language": [
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-01-08T15:16:37.333501Z",
+    "license": "Not licensed",
+    "name": "clampFISH",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41592-022-01653-6",
+            "metadata": {
+                "abstract": "© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.RNA labeling in situ has enormous potential to visualize transcripts and quantify their levels in single cells, but it remains challenging to produce high levels of signal while also enabling multiplexed detection of multiple RNA species simultaneously. Here, we describe clampFISH 2.0, a method that uses an inverted padlock design to efficiently detect many RNA species and exponentially amplify their signals at once, while also reducing the time and cost compared with the prior clampFISH method. We leverage the increased throughput afforded by multiplexed signal amplification and sequential detection to detect 10 different RNA species in more than 1 million cells. We also show that clampFISH 2.0 works in tissue sections. We expect that the advantages offered by clampFISH 2.0 will enable many applications in spatial transcriptomics.",
+                "authors": [
+                    {
+                        "name": "Alicea G.M."
+                    },
+                    {
+                        "name": "Dardani I."
+                    },
+                    {
+                        "name": "Emert B.L."
+                    },
+                    {
+                        "name": "Fane M.E."
+                    },
+                    {
+                        "name": "Goyal Y."
+                    },
+                    {
+                        "name": "Herlyn M."
+                    },
+                    {
+                        "name": "Jiang C.L."
+                    },
+                    {
+                        "name": "Kaur A."
+                    },
+                    {
+                        "name": "Lee J."
+                    },
+                    {
+                        "name": "Raj A."
+                    },
+                    {
+                        "name": "Rouhanifard S.H."
+                    },
+                    {
+                        "name": "Weeraratna A.T."
+                    },
+                    {
+                        "name": "Xiao M."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Nature Methods",
+                "title": "ClampFISH 2.0 enables rapid, scalable amplified RNA detection in situ"
+            },
+            "pmid": "36280724"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Probes and primers",
+            "uri": "http://edamontology.org/topic_0632"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ],
+    "version": [
+        "2.0"
+    ]
+}
diff --git a/data/clarion/clarion.biotools.json b/data/clarion/clarion.biotools.json
new file mode 100644
index 0000000000000..479826b75f3ff
--- /dev/null
+++ b/data/clarion/clarion.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T14:25:48.166787Z",
+    "biotoolsCURIE": "biotools:clarion",
+    "biotoolsID": "clarion",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Jiangning Song"
+        },
+        {
+            "name": "Yue Bi"
+        }
+    ],
+    "description": "Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://monash.bioweb.cloud.edu.au/Clarion/center.php?page=help"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "DNA transcription",
+                    "uri": "http://edamontology.org/operation_0372"
+                },
+                {
+                    "term": "Gene regulatory network prediction",
+                    "uri": "http://edamontology.org/operation_2437"
+                },
+                {
+                    "term": "Subcellular localisation prediction",
+                    "uri": "http://edamontology.org/operation_2489"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://monash.bioweb.cloud.edu.au/Clarion/",
+    "lastUpdate": "2023-01-27T14:25:48.169340Z",
+    "name": "Clarion",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC467",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Subcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.",
+                "authors": [
+                    {
+                        "name": "Bi Y."
+                    },
+                    {
+                        "name": "Guo X."
+                    },
+                    {
+                        "name": "Guo Y."
+                    },
+                    {
+                        "name": "Jia C."
+                    },
+                    {
+                        "name": "Li F."
+                    },
+                    {
+                        "name": "Pan T."
+                    },
+                    {
+                        "name": "Song J."
+                    },
+                    {
+                        "name": "Wang Z."
+                    },
+                    {
+                        "name": "Webb G.I."
+                    },
+                    {
+                        "name": "Yao J."
+                    }
+                ],
+                "date": "2022-11-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations"
+            },
+            "pmid": "36341591"
+        }
+    ],
+    "toolType": [
+        "Desktop application",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        }
+    ]
+}
diff --git a/data/clevrvis/clevrvis.biotools.json b/data/clevrvis/clevrvis.biotools.json
new file mode 100644
index 0000000000000..00e3267d6ce85
--- /dev/null
+++ b/data/clevrvis/clevrvis.biotools.json
@@ -0,0 +1,134 @@
+{
+    "additionDate": "2023-01-18T09:58:02.321637Z",
+    "biotoolsCURIE": "biotools:clevrvis",
+    "biotoolsID": "clevrvis",
+    "collectionID": [
+        "Bioconductor"
+    ],
+    "credit": [
+        {
+            "email": "sarah.sandmann@uni-muenster.de",
+            "name": "Sarah Sandmann",
+            "orcidid": "https://orcid.org/0000-0002-5011-0641"
+        }
+    ],
+    "description": "clevRvis provides an extensive set of visualization techniques for clonal evolution. Three types of plots are available: 1) shark plots (basic trees, showing the phylogeny and optionally the cancer cell fraction CCF); 2) dolphin plots (advanced visualization, showing the phylogeny and the development of CCFs over time); 3) plaice plots (novel visualization, showing the phylogeny, the development of CCFs and the development of remaining healthy alleles, influenced by bi-allelic events, over time). Moreover, the tool provides algorithms for fully automatic interpolation of time points and estimation of therapy effect to approximate a tumor's development in the presence of few measured time points, as well as exploring alternative trees.",
+    "documentation": [
+        {
+            "note": "Detailed documentation of the functions can be found in the manuals. A detailed walk-through is provided in the vignette.",
+            "type": [
+                "Quick start guide"
+            ],
+            "url": "https://github.com/sandmanns/clevRvis"
+        }
+    ],
+    "download": [
+        {
+            "note": "clevRvis is an R package. It can be easily downloaded by executing\n\nif (!requireNamespace(\"devtools\", quietly=TRUE))\n  install.packages(\"devtools\")\ndevtools::install_github(\"sandmanns/clevRvis\")\n\nin R.",
+            "type": "Source code",
+            "url": "https://github.com/sandmanns/clevRvis",
+            "version": "0.99.5"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Phylogenetic data",
+                        "uri": "http://edamontology.org/data_2523"
+                    },
+                    "format": [
+                        {
+                            "term": "CSV",
+                            "uri": "http://edamontology.org/format_3752"
+                        }
+                    ]
+                },
+                {
+                    "data": {
+                        "term": "Phylogenetic data",
+                        "uri": "http://edamontology.org/data_2523"
+                    },
+                    "format": [
+                        {
+                            "term": "xls",
+                            "uri": "http://edamontology.org/format_3468"
+                        }
+                    ]
+                },
+                {
+                    "data": {
+                        "term": "Phylogenetic data",
+                        "uri": "http://edamontology.org/data_2523"
+                    },
+                    "format": [
+                        {
+                            "term": "xlsx",
+                            "uri": "http://edamontology.org/format_3620"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Phylogenetic tree",
+                        "uri": "http://edamontology.org/data_0872"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Plot",
+                        "uri": "http://edamontology.org/data_2884"
+                    }
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/sandmanns/clevRvis",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-18T10:20:59.143492Z",
+    "license": "LGPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/sandmanns/clevRvis"
+        }
+    ],
+    "maturity": "Mature",
+    "name": "clevRvis",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "sandmanns",
+    "toolType": [
+        "Command-line tool",
+        "Library",
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        }
+    ],
+    "version": [
+        "0.99.5"
+    ]
+}
diff --git a/data/clin_skat/clin_skat.biotools.json b/data/clin_skat/clin_skat.biotools.json
new file mode 100644
index 0000000000000..416ebd9dedd31
--- /dev/null
+++ b/data/clin_skat/clin_skat.biotools.json
@@ -0,0 +1,112 @@
+{
+    "additionDate": "2023-01-08T15:20:47.053374Z",
+    "biotoolsCURIE": "biotools:clin_skat",
+    "biotoolsID": "clin_skat",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tplu@ntu.edu.tw",
+            "name": "Tzu-Pin Lu",
+            "orcidid": "https://orcid.org/0000-0003-3697-0386",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "CLIN_SKAT is a package within the R programming language to (i) first extract clinically relevant variants (rare and common), followed by (ii) gene-based association analysis by grouping the selected variants.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Collapsing methods",
+                    "uri": "http://edamontology.org/operation_3791"
+                },
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                },
+                {
+                    "term": "Haplotype mapping",
+                    "uri": "http://edamontology.org/operation_0487"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ShihChingYu/CLIN_SKAT",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-08T15:20:47.055837Z",
+    "license": "GPL-2.0",
+    "name": "CLIN_SKAT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-04987-2",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Availability of next generation sequencing data, allows low-frequency and rare variants to be studied through strategies other than the commonly used genome-wide association studies (GWAS). Rare variants are important keys towards explaining the heritability for complex diseases that remains to be explained by common variants due to their low effect sizes. However, analysis strategies struggle to keep up with the huge amount of data at disposal therefore creating a bottleneck. This study describes CLIN_SKAT, an R package, that provides users with an easily implemented analysis pipeline with the goal of (i) extracting clinically relevant variants (both rare and common), followed by (ii) gene-based association analysis by grouping the selected variants. Results: CLIN_SKAT offers four simple functions that can be used to obtain clinically relevant variants, map them to genes or gene sets, calculate weights from global healthy populations and conduct weighted case–control analysis. CLIN_SKAT introduces improvements by adding certain pre-analysis steps and customizable features to make the SKAT results clinically more meaningful. Moreover, it offers several plot functions that can be availed towards obtaining visualizations for interpretation of the analyses results. CLIN_SKAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It can be freely downloaded from https://github.com/ShihChingYu/CLIN_SKAT, installed through devtools::install_github(\"ShihChingYu/CLIN_SKAT\", force=T) and executed by loading the package into R using library(CLIN_SKAT). All outputs (tabular and graphical) can be downloaded in simple, publishable formats. Conclusions: Statistical association analysis is often underpowered due to low sample sizes and high numbers of variants to be tested, limiting detection of causal ones. Therefore, retaining a subset of variants that are biologically meaningful seems to be a more effective strategy for identifying explainable associations while reducing the degrees of freedom. CLIN_SKAT offers users a one-stop R package that identifies disease risk variants with improved power via a series of tailor-made procedures that allows dimension reduction, by retaining functionally relevant variants, and incorporating ethnicity based priors. Furthermore, it also eliminates the requirement for high computational resources and bioinformatics expertise.",
+                "authors": [
+                    {
+                        "name": "Chattopadhyay A."
+                    },
+                    {
+                        "name": "Chuang E.Y."
+                    },
+                    {
+                        "name": "Hsu Y.-C."
+                    },
+                    {
+                        "name": "Juang J.-M.J."
+                    },
+                    {
+                        "name": "Lu T.-P."
+                    },
+                    {
+                        "name": "Shih C.-Y."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "CLIN_SKAT: an R package to conduct association analysis using functionally relevant variants"
+            },
+            "pmcid": "PMC9590128",
+            "pmid": "36274122"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        }
+    ]
+}
diff --git a/data/clinical_longformer/clinical_longformer.biotools.json b/data/clinical_longformer/clinical_longformer.biotools.json
new file mode 100644
index 0000000000000..aeaa4758c4657
--- /dev/null
+++ b/data/clinical_longformer/clinical_longformer.biotools.json
@@ -0,0 +1,100 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T19:31:10.509233Z",
+    "biotoolsCURIE": "biotools:clinical_longformer",
+    "biotoolsID": "clinical_longformer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yuan.luo@northwestern.edu",
+            "name": "Yuan Luo",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Clinical-Longformer is a clinical knowledge enriched version of Longformer that was further pre-trained using MIMIC-III clinical notes.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Named-entity and concept recognition",
+                    "uri": "http://edamontology.org/operation_3280"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/luoyuanlab/Clinical-Longformer",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T19:31:10.511930Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://huggingface.co/yikuan8/Clinical-Longformer"
+        }
+    ],
+    "name": "Clinical-Longformer",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/JAMIA/OCAC225",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.OBJECTIVE: Clinical knowledge-enriched transformer models (eg, ClinicalBERT) have state-of-the-art results on clinical natural language processing (NLP) tasks. One of the core limitations of these transformer models is the substantial memory consumption due to their full self-attention mechanism, which leads to the performance degradation in long clinical texts. To overcome this, we propose to leverage long-sequence transformer models (eg, Longformer and BigBird), which extend the maximum input sequence length from 512 to 4096, to enhance the ability to model long-term dependencies in long clinical texts. MATERIALS AND METHODS: Inspired by the success of long-sequence transformer models and the fact that clinical notes are mostly long, we introduce 2 domain-enriched language models, Clinical-Longformer and Clinical-BigBird, which are pretrained on a large-scale clinical corpus. We evaluate both language models using 10 baseline tasks including named entity recognition, question answering, natural language inference, and document classification tasks. RESULTS: The results demonstrate that Clinical-Longformer and Clinical-BigBird consistently and significantly outperform ClinicalBERT and other short-sequence transformers in all 10 downstream tasks and achieve new state-of-the-art results. DISCUSSION: Our pretrained language models provide the bedrock for clinical NLP using long texts. We have made our source code available at https://github.com/luoyuanlab/Clinical-Longformer, and the pretrained models available for public download at: https://huggingface.co/yikuan8/Clinical-Longformer. CONCLUSION: This study demonstrates that clinical knowledge-enriched long-sequence transformers are able to learn long-term dependencies in long clinical text. Our methods can also inspire the development of other domain-enriched long-sequence transformers.",
+                "authors": [
+                    {
+                        "name": "Ahmad F.S."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Luo Y."
+                    },
+                    {
+                        "name": "Wang H."
+                    },
+                    {
+                        "name": "Wehbe R.M."
+                    }
+                ],
+                "date": "2023-01-18T00:00:00Z",
+                "journal": "Journal of the American Medical Informatics Association : JAMIA",
+                "title": "A comparative study of pretrained language models for long clinical text"
+            },
+            "pmcid": "PMC9846675",
+            "pmid": "36451266"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        }
+    ]
+}
diff --git a/data/clustercad/clustercad.biotools.json b/data/clustercad/clustercad.biotools.json
new file mode 100644
index 0000000000000..62e434f1a2b88
--- /dev/null
+++ b/data/clustercad/clustercad.biotools.json
@@ -0,0 +1,130 @@
+{
+    "additionDate": "2023-01-27T14:30:52.208554Z",
+    "biotoolsCURIE": "biotools:clustercad",
+    "biotoolsID": "clustercad",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tbackman@lbl.gov",
+            "name": "Tyler W H Backman",
+            "orcidid": "https://orcid.org/0000-0002-6056-353X",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "ClusterCAD provides a database and web-based toolkit designed to enable researchers to harness the potential of type I modular polyketide synthases and nonribosomal peptide synthetases for combinatorial biosynthesis.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://clustercad.jbei.org",
+    "language": [
+        "JavaScript",
+        "Python",
+        "Scheme"
+    ],
+    "lastUpdate": "2023-01-27T14:30:52.211341Z",
+    "license": "Other",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/JBEI/clusterCAD"
+        }
+    ],
+    "name": "ClusterCAD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1075",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Megasynthase enzymes such as type I modular polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs) play a central role in microbial chemical warfare because they can evolve rapidly by shuffling parts (catalytic domains) to produce novel chemicals. If we can understand the design rules to reshuffle these parts, PKSs and NRPSs will provide a systematic and modular way to synthesize millions of molecules including pharmaceuticals, biomaterials, and biofuels. However, PKS and NRPS engineering remains difficult due to a limited understanding of the determinants of PKS and NRPS fold and function. We developed ClusterCAD to streamline and simplify the process of designing and testing engineered PKS variants. Here, we present the highly improved ClusterCAD 2.0 release, available at https://clustercad.jbei.org. ClusterCAD 2.0 boasts support for PKS-NRPS hybrid and NRPS clusters in addition to PKS clusters; a vastly enlarged database of curated PKS, PKS-NRPS hybrid, and NRPS clusters; a diverse set of chemical 'starters' and loading modules; the new Domain Architecture Cluster Search Tool; and an offline Jupyter Notebook workspace, among other improvements. Together these features massively expand the chemical space that can be accessed by enzymes engineered with ClusterCAD.",
+                "authors": [
+                    {
+                        "name": "Backman T.W.H."
+                    },
+                    {
+                        "name": "Keasling J.D."
+                    },
+                    {
+                        "name": "LaFrance S."
+                    },
+                    {
+                        "name": "Martin H.G."
+                    },
+                    {
+                        "name": "Nava A.A."
+                    },
+                    {
+                        "name": "Tao X.B."
+                    },
+                    {
+                        "name": "Xing Y."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "ClusterCAD 2.0: an updated computational platform for chimeric type I polyketide synthase and nonribosomal peptide synthetase design"
+            },
+            "pmcid": "PMC9825560",
+            "pmid": "36416273"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Suite",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biomaterials",
+            "uri": "http://edamontology.org/topic_3368"
+        },
+        {
+            "term": "Enzymes",
+            "uri": "http://edamontology.org/topic_0821"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ],
+    "version": [
+        "2.0"
+    ]
+}
diff --git a/data/clusterseg/clusterseg.biotools.json b/data/clusterseg/clusterseg.biotools.json
new file mode 100644
index 0000000000000..3031bff86bb7e
--- /dev/null
+++ b/data/clusterseg/clusterseg.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-09T14:23:31.826793Z",
+    "biotoolsCURIE": "biotools:clusterseg",
+    "biotoolsID": "clusterseg",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Dinggang Shen"
+        }
+    ],
+    "description": "A nucleus segmentation framework, namely ClusterSeg, to tackle nuclei clusters, which consists of a convolutional-transformer hybrid encoder and a 2.5-path decoder for precise predictions of nuclei instance mask, contours, and clustered-edges.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Microscope image visualisation",
+                    "uri": "http://edamontology.org/operation_3552"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/lu-yizhou/ClusterSeg",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-09T14:23:31.832668Z",
+    "license": "Not licensed",
+    "name": "ClusterSeg",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.MEDIA.2023.102758",
+            "metadata": {
+                "abstract": "The detection and segmentation of individual cells or nuclei is often involved in image analysis across a variety of biology and biomedical applications as an indispensable prerequisite. However, the ubiquitous presence of crowd clusters with morphological variations often hinders successful instance segmentation. In this paper, nuclei cluster focused annotation strategies and frameworks are proposed to overcome this challenging practical problem. Specifically, we design a nucleus segmentation framework, namely ClusterSeg, to tackle nuclei clusters, which consists of a convolutional-transformer hybrid encoder and a 2.5-path decoder for precise predictions of nuclei instance mask, contours, and clustered-edges. Additionally, an annotation-efficient clustered-edge pointed strategy pinpoints the salient and error-prone boundaries, where a partially-supervised PS-ClusterSeg is presented using ClusterSeg as the segmentation backbone. The framework is evaluated with four privately curated image sets and two public sets with characteristic severely clustered nuclei across a variety range of image modalities, e.g., microscope, cytopathology, and histopathology images. The proposed ClusterSeg and PS-ClusterSeg are modality-independent and generalizable, and superior to current state-of-the-art approaches in multiple metrics empirically. Our collected data, the elaborate annotations to both public and private set, as well the source code, are released publicly at https://github.com/lu-yizhou/ClusterSeg.",
+                "authors": [
+                    {
+                        "name": "Guo Y."
+                    },
+                    {
+                        "name": "Huang J."
+                    },
+                    {
+                        "name": "Huang Q."
+                    },
+                    {
+                        "name": "Jiang F."
+                    },
+                    {
+                        "name": "Ke J."
+                    },
+                    {
+                        "name": "Liang X."
+                    },
+                    {
+                        "name": "Liu S."
+                    },
+                    {
+                        "name": "Lu Y."
+                    },
+                    {
+                        "name": "Shen D."
+                    },
+                    {
+                        "name": "Shen Y."
+                    },
+                    {
+                        "name": "Wei Z."
+                    },
+                    {
+                        "name": "Yao J."
+                    },
+                    {
+                        "name": "Zhou Y."
+                    },
+                    {
+                        "name": "Zhu J."
+                    }
+                ],
+                "date": "2023-04-01T00:00:00Z",
+                "journal": "Medical Image Analysis",
+                "title": "ClusterSeg: A crowd cluster pinpointed nucleus segmentation framework with cross-modality datasets"
+            },
+            "pmid": "36731275"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/clustmmra/clustmmra.biotools.json b/data/clustmmra/clustmmra.biotools.json
new file mode 100644
index 0000000000000..87c85cb225e17
--- /dev/null
+++ b/data/clustmmra/clustmmra.biotools.json
@@ -0,0 +1,94 @@
+{
+    "additionDate": "2023-01-27T14:34:54.539532Z",
+    "biotoolsCURIE": "biotools:clustmmra",
+    "biotoolsID": "clustmmra",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "Loredana.Martignetti@curie.fr",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A scalable version of the clustMMRA pipeline for the identification of genomically co-clustered microRNAs driving cancer subtypes.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "miRNA expression analysis",
+                    "uri": "http://edamontology.org/operation_3792"
+                },
+                {
+                    "term": "miRNA target prediction",
+                    "uri": "http://edamontology.org/operation_0463"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/sysbio-curie/clustMMRA_v2",
+    "language": [
+        "Perl",
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-27T14:34:54.542050Z",
+    "license": "Not licensed",
+    "name": "clustMMRA",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1007/978-3-031-08356-3_10",
+            "metadata": {
+                "abstract": "© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.In recent cancer genomics programs, large-scale profiling of microRNAs has been routinely used in order to better understand the role of microRNAs in gene regulation and disease. To support the analysis of such amount of data, scalability of bioinformatics pipelines is increasingly important to handle larger datasets. Here, we describe a scalable implementation of the clustered miRNA Master Regulator Analysis (clustMMRA) pipeline, developed to search for genomic clusters of microRNAs potentially driving cancer molecular subtyping. Genomically clustered microRNAs can be simultaneously expressed to work in a combined manner and jointly regulate cell phenotypes. However, the majority of computational approaches for the identification of microRNA master regulators are typically designed to detect the regulatory effect of a single microRNA. We have applied the clustMMRA pipeline to multiple pediatric tumor datasets, up to a hundred samples in size, demonstrating very satisfying performances of the software on large datasets. Results have highlighted genomic clusters of microRNAs potentially involved in several subgroups of the different pediatric cancers or specifically involved in the phenotype of a subgroup. In particular, we confirmed the cluster of microRNAs at the 14q32 locus to be involved in multiple pediatric cancers, showing its specific downregulation in tumor subgroups with aggressive phenotype.",
+                "authors": [
+                    {
+                        "name": "Ayrault O."
+                    },
+                    {
+                        "name": "Cancila G."
+                    },
+                    {
+                        "name": "Hernandez C."
+                    },
+                    {
+                        "name": "Martignetti L."
+                    },
+                    {
+                        "name": "Zinovyev A."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Advances in Experimental Medicine and Biology",
+                "title": "ClustMMRA v2: A Scalable Computational Pipeline for the Identification of MicroRNA Clusters Acting Cooperatively on Tumor Molecular Subgroups"
+            },
+            "pmid": "36352218"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene regulation",
+            "uri": "http://edamontology.org/topic_0204"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/cnn_pred/cnn_pred.biotools.json b/data/cnn_pred/cnn_pred.biotools.json
new file mode 100644
index 0000000000000..ea109f6331f79
--- /dev/null
+++ b/data/cnn_pred/cnn_pred.biotools.json
@@ -0,0 +1,92 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T19:40:49.105287Z",
+    "biotoolsCURIE": "biotools:cnn_pred",
+    "biotoolsID": "cnn_pred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Farnoush Manavi"
+        }
+    ],
+    "description": "CNN-Pred is a machine learning tool to accurately predict single-stranded DNA-binding proteins (SSBs) or double-stranded DNA-binding proteins (DSBs).",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "DNA binding site prediction",
+                    "uri": "http://edamontology.org/operation_3903"
+                },
+                {
+                    "term": "DNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3900"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MLBC-lab/CNN-Pred",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T19:40:49.107946Z",
+    "license": "Not licensed",
+    "name": "CNN-Pred",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.GENE.2022.147045",
+            "metadata": {
+                "abstract": "© 2022 Elsevier B.V.DNA-binding proteins play a vital role in biological activity including DNA replication, DNA packing, and DNA reparation. DNA-binding proteins can be classified into single-stranded DNA-binding proteins (SSBs) or double-stranded DNA-binding proteins (DSBs). Determining whether a protein is DSB or SSB helps determine the protein's function. Therefore, many studies have been conducted to accurately identify DSB and SSB in recent years. Despite all the efforts have been made so far, the DSB and SSB prediction performance remains limited. In this study, we propose a new method called CNN-Pred to accurately predict DSB and SSB. To build CNN-Pred, we first extract evolutionary-based features in the form of mono-gram and bi-gram profiles using position specific scoring matrix (PSSM). We then, use 1D-convolutional neural network (CNN) as the classifier to our extracted features. Our results demonstrate that CNN-Pred can enhance the DSB and SSB prediction accuracies by more than 4%, on the independent test compared to previous studies found in the literature. CNN-pred as a standalone tool and all its source codes are publicly available at: https://github.com/MLBC-lab/CNN-Pred.",
+                "authors": [
+                    {
+                        "name": "Dehzangi I."
+                    },
+                    {
+                        "name": "Manavi F."
+                    },
+                    {
+                        "name": "Sharma A."
+                    },
+                    {
+                        "name": "Sharma R."
+                    },
+                    {
+                        "name": "Shatabda S."
+                    },
+                    {
+                        "name": "Tsunoda T."
+                    }
+                ],
+                "date": "2023-02-15T00:00:00Z",
+                "journal": "Gene",
+                "title": "CNN-Pred: Prediction of single-stranded and double-stranded DNA-binding protein using convolutional neural networks"
+            },
+            "pmid": "36503892"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Proteins",
+            "uri": "http://edamontology.org/topic_0078"
+        }
+    ]
+}
diff --git a/data/cnnarginineme/cnnarginineme.biotools.json b/data/cnnarginineme/cnnarginineme.biotools.json
new file mode 100644
index 0000000000000..fdaaa602f8bba
--- /dev/null
+++ b/data/cnnarginineme/cnnarginineme.biotools.json
@@ -0,0 +1,106 @@
+{
+    "additionDate": "2023-01-27T14:37:59.015853Z",
+    "biotoolsCURIE": "biotools:cnnarginineme",
+    "biotoolsID": "cnnarginineme",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "leinama@gmail.com",
+            "name": "Leina Ma",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene methylation analysis",
+                    "uri": "http://edamontology.org/operation_3207"
+                },
+                {
+                    "term": "PCR primer design",
+                    "uri": "http://edamontology.org/operation_0308"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/guoyangzou/CNNArginineMe",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-27T14:37:59.018295Z",
+    "license": "Not licensed",
+    "name": "CNNArginineMe",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FGENE.2022.1036862",
+            "metadata": {
+                "abstract": "Copyright © 2022 Zhao, Jiang, Zou, Lin, Wang, Liu and Ma.Protein arginine methylation (PRme), as one post-translational modification, plays a critical role in numerous cellular processes and regulates critical cellular functions. Though several in silico models for predicting PRme sites have been reported, new models may be required to develop due to the significant increase of identified PRme sites. In this study, we constructed multiple machine-learning and deep-learning models. The deep-learning model CNN combined with the One-Hot coding showed the best performance, dubbed CNNArginineMe. CNNArginineMe performed best in AUC scoring metrics in comparisons with several reported predictors. Additionally, we employed CNNArginineMe to predict arginine methylation proteome and performed functional analysis. The arginine methylated proteome is significantly enriched in the amyotrophic lateral sclerosis (ALS) pathway. CNNArginineMe is freely available at https://github.com/guoyangzou/CNNArginineMe.",
+                "authors": [
+                    {
+                        "name": "Jiang H."
+                    },
+                    {
+                        "name": "Lin Q."
+                    },
+                    {
+                        "name": "Liu J."
+                    },
+                    {
+                        "name": "Ma L."
+                    },
+                    {
+                        "name": "Wang Q."
+                    },
+                    {
+                        "name": "Zhao J."
+                    },
+                    {
+                        "name": "Zou G."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-10-17T00:00:00Z",
+                "journal": "Frontiers in Genetics",
+                "title": "CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence"
+            },
+            "pmcid": "PMC9618650",
+            "pmid": "36324513"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Methylated DNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3674"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/cntseg/cntseg.biotools.json b/data/cntseg/cntseg.biotools.json
new file mode 100644
index 0000000000000..16814d94d0b0e
--- /dev/null
+++ b/data/cntseg/cntseg.biotools.json
@@ -0,0 +1,84 @@
+{
+    "additionDate": "2023-03-09T14:27:34.717775Z",
+    "biotoolsCURIE": "biotools:cntseg",
+    "biotoolsID": "cntseg",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Yuanjing Feng"
+        }
+    ],
+    "description": "A multimodal deep-learning-based network for cranial nerves tract segmentation.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/IPIS-XieLei/CNTSeg",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-09T14:27:34.721591Z",
+    "name": "CNTSeg",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.MEDIA.2023.102766",
+            "metadata": {
+                "abstract": "The segmentation of cranial nerves (CNs) tracts based on diffusion magnetic resonance imaging (dMRI) provides a valuable quantitative tool for the analysis of the morphology and course of individual CNs. Tractography-based approaches can describe and analyze the anatomical area of CNs by selecting the reference streamlines in combination with ROIs-based (regions-of-interests) or clustering-based. However, due to the slender structure of CNs and the complex anatomical environment, single-modality data based on dMRI cannot provide a complete and accurate description, resulting in low accuracy or even failure of current algorithms in performing individualized CNs segmentation. In this work, we propose a novel multimodal deep-learning-based multi-class network for automated cranial nerves tract segmentation without using tractography, ROI placement or clustering, called CNTSeg. Specifically, we introduced T1w images, fractional anisotropy (FA) images, and fiber orientation distribution function (fODF) peaks into the training data set, and design the back-end fusion module which uses the complementary information of the interphase feature fusion to improve the segmentation performance. CNTSeg has achieved the segmentation of 5 pairs of CNs (i.e. optic nerve CN II, oculomotor nerve CN III, trigeminal nerve CN V, and facial–vestibulocochlear nerve CN VII/VIII). Extensive comparisons and ablation experiments show promising results and are anatomically convincing even for difficult tracts. The code will be openly available at https://github.com/IPIS-XieLei/CNTSeg.",
+                "authors": [
+                    {
+                        "name": "Chen Z."
+                    },
+                    {
+                        "name": "Feng Y."
+                    },
+                    {
+                        "name": "Hu Q."
+                    },
+                    {
+                        "name": "Huang J."
+                    },
+                    {
+                        "name": "Xie G."
+                    },
+                    {
+                        "name": "Xie L."
+                    },
+                    {
+                        "name": "Yu J."
+                    },
+                    {
+                        "name": "Zeng Q."
+                    }
+                ],
+                "date": "2023-05-01T00:00:00Z",
+                "journal": "Medical Image Analysis",
+                "title": "CNTSeg: A multimodal deep-learning-based network for cranial nerves tract segmentation"
+            },
+            "pmid": "36812693"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        }
+    ]
+}
diff --git a/data/coadti/coadti.biotools.json b/data/coadti/coadti.biotools.json
new file mode 100644
index 0000000000000..559fec4ed3c77
--- /dev/null
+++ b/data/coadti/coadti.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-08T15:27:21.005340Z",
+    "biotoolsCURIE": "biotools:coadti",
+    "biotoolsID": "coadti",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "kc.w@cityu.edu.hk",
+            "name": "Xiangtao Li",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "lixt314@jlu.edu.cn",
+            "name": "Ka-Chun Wong",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A multi-modal co-attention based framework for drug-target interaction annotation.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Layne-Huang/CoaDTI",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-08T15:27:21.009117Z",
+    "license": "Apache-2.0",
+    "name": "CoaDTI",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC446",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.MOTIVATION: The identification of drug-target interactions (DTIs) plays a vital role for in silico drug discovery, in which the drug is the chemical molecule, and the target is the protein residues in the binding pocket. Manual DTI annotation approaches remain reliable; however, it is notoriously laborious and time-consuming to test each drug-target pair exhaustively. Recently, the rapid growth of labelled DTI data has catalysed interests in high-throughput DTI prediction. Unfortunately, those methods highly rely on the manual features denoted by human, leading to errors. RESULTS: Here, we developed an end-to-end deep learning framework called CoaDTI to significantly improve the efficiency and interpretability of drug target annotation. CoaDTI incorporates the Co-attention mechanism to model the interaction information from the drug modality and protein modality. In particular, CoaDTI incorporates transformer to learn the protein representations from raw amino acid sequences, and GraphSage to extract the molecule graph features from SMILES. Furthermore, we proposed to employ the transfer learning strategy to encode protein features by pre-trained transformer to address the issue of scarce labelled data. The experimental results demonstrate that CoaDTI achieves competitive performance on three public datasets compared with state-of-the-art models. In addition, the transfer learning strategy further boosts the performance to an unprecedented level. The extended study reveals that CoaDTI can identify novel DTIs such as reactions between candidate drugs and severe acute respiratory syndrome coronavirus 2-associated proteins. The visualization of co-attention scores can illustrate the interpretability of our model for mechanistic insights. AVAILABILITY: Source code are publicly available at https://github.com/Layne-Huang/CoaDTI.",
+                "authors": [
+                    {
+                        "name": "Chen X."
+                    },
+                    {
+                        "name": "Huang L."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Lin J."
+                    },
+                    {
+                        "name": "Liu R."
+                    },
+                    {
+                        "name": "Meng L."
+                    },
+                    {
+                        "name": "Wong K.-C."
+                    },
+                    {
+                        "name": "Zheng Z."
+                    }
+                ],
+                "date": "2022-11-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "CoaDTI: multi-modal co-attention based framework for drug-target interaction annotation"
+            },
+            "pmid": "36274236"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/cocktail/cocktail.biotools.json b/data/cocktail/cocktail.biotools.json
new file mode 100644
index 0000000000000..0031612bf223c
--- /dev/null
+++ b/data/cocktail/cocktail.biotools.json
@@ -0,0 +1,63 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T14:42:31.024157Z",
+    "biotoolsCURIE": "biotools:cocktail",
+    "biotoolsID": "cocktail",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Saija Johanna Kiljunen"
+        }
+    ],
+    "description": "Cocktail is a program for mathematical modelling of bacteriophage (phage) infection kinetics. Cocktail is a Windows 64-bit program and the source code can be developed in the directions that users see fit.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Modelling and simulation",
+                    "uri": "http://edamontology.org/operation_2426"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ASNilsson/Cocktail-phage-infection-kinetics",
+    "lastUpdate": "2023-01-27T14:42:31.027040Z",
+    "license": "CC-BY-NC-SA-4.0",
+    "name": "Cocktail",
+    "operatingSystem": [
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3390/V14112483",
+            "pmcid": "PMC9695944",
+            "pmid": "36366581"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "Mobile genetic elements",
+            "uri": "http://edamontology.org/topic_0798"
+        },
+        {
+            "term": "Software engineering",
+            "uri": "http://edamontology.org/topic_3372"
+        }
+    ]
+}
diff --git a/data/coda/coda.biotools.json b/data/coda/coda.biotools.json
new file mode 100644
index 0000000000000..b7476d0127816
--- /dev/null
+++ b/data/coda/coda.biotools.json
@@ -0,0 +1,138 @@
+{
+    "additionDate": "2023-01-08T15:31:23.037960Z",
+    "biotoolsCURIE": "biotools:coda",
+    "biotoolsID": "coda",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Ashley L. Kiemen"
+        }
+    ],
+    "description": "A tool for quantitative 3D reconstruction of large tissues at cellular resolution.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Image annotation",
+                    "uri": "http://edamontology.org/operation_3553"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ashleylk/CODA",
+    "language": [
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-01-08T15:31:23.040823Z",
+    "license": "Not licensed",
+    "name": "CODA",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41592-022-01650-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.A central challenge in biology is obtaining high-content, high-resolution information while analyzing tissue samples at volumes relevant to disease progression. We address this here with CODA, a method to reconstruct exceptionally large (up to multicentimeter cubed) tissues at subcellular resolution using serially sectioned hematoxylin and eosin-stained tissue sections. Here we demonstrate CODA’s ability to reconstruct three-dimensional (3D) distinct microanatomical structures in pancreas, skin, lung and liver tissues. CODA allows creation of readily quantifiable tissue volumes amenable to biological research. As a testbed, we assess the microanatomy of the human pancreas during tumorigenesis within the branching pancreatic ductal system, labeling ten distinct structures to examine heterogeneity and structural transformation during neoplastic progression. We show that pancreatic precancerous lesions develop into distinct 3D morphological phenotypes and that pancreatic cancer tends to spread far from the bulk tumor along collagen fibers that are highly aligned to the 3D curves of ductal, lobular, vascular and neural structures. Thus, CODA establishes a means to transform broadly the structural study of human diseases through exploration of exhaustively labeled 3D microarchitecture.",
+                "authors": [
+                    {
+                        "name": "Amoa F."
+                    },
+                    {
+                        "name": "Babu J.M."
+                    },
+                    {
+                        "name": "Braxton A.M."
+                    },
+                    {
+                        "name": "Cornish T.C."
+                    },
+                    {
+                        "name": "Grahn M.P."
+                    },
+                    {
+                        "name": "Han K.S."
+                    },
+                    {
+                        "name": "Hong S.-M."
+                    },
+                    {
+                        "name": "Hruban R.H."
+                    },
+                    {
+                        "name": "Hsu J."
+                    },
+                    {
+                        "name": "Huang P."
+                    },
+                    {
+                        "name": "Jiang A.C."
+                    },
+                    {
+                        "name": "Kiemen A.L."
+                    },
+                    {
+                        "name": "Kim B."
+                    },
+                    {
+                        "name": "Reddy S."
+                    },
+                    {
+                        "name": "Reichel R."
+                    },
+                    {
+                        "name": "Thompson E.D."
+                    },
+                    {
+                        "name": "Wirtz D."
+                    },
+                    {
+                        "name": "Wood L.D."
+                    },
+                    {
+                        "name": "Wu P.-H."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Nature Methods",
+                "title": "CODA: quantitative 3D reconstruction of large tissues at cellular resolution"
+            },
+            "pmid": "36280719"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        }
+    ]
+}
diff --git a/data/codetta/codetta.biotools.json b/data/codetta/codetta.biotools.json
new file mode 100644
index 0000000000000..e4c43df29a914
--- /dev/null
+++ b/data/codetta/codetta.biotools.json
@@ -0,0 +1,101 @@
+{
+    "additionDate": "2023-02-13T19:44:04.247573Z",
+    "biotoolsCURIE": "biotools:codetta",
+    "biotoolsID": "codetta",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "seaneddy@fas.harvard.edu",
+            "name": "Sean R Eddy",
+            "orcidid": "https://orcid.org/0000-0001-6676-4706",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Codetta is a Python program for predicting the genetic code table of an organism from nucleotide sequences. Codetta can analyze an arbitrary nucleotide sequence and needs no sequence annotation or taxonomic placement.",
+    "download": [
+        {
+            "type": "Software package",
+            "url": "http://eddylab.org/software/codetta/codetta2.tar.gz"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genetic code prediction",
+                    "uri": "http://edamontology.org/operation_0489"
+                },
+                {
+                    "term": "Sequence annotation",
+                    "uri": "http://edamontology.org/operation_0361"
+                },
+                {
+                    "term": "Sequence profile alignment",
+                    "uri": "http://edamontology.org/operation_0300"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/kshulgina/codetta",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T19:44:04.250295Z",
+    "license": "BSD-3-Clause",
+    "name": "Codetta",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC802",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: Codetta is a Python program for predicting the genetic code table of an organism from nucleotide sequences. Codetta can analyze an arbitrary nucleotide sequence and needs no sequence annotation or taxonomic placement. The most likely amino acid decoding for each of the 64 codons is inferred from alignments of profile hidden Markov models of conserved proteins to the input sequence. AVAILABILITY AND IMPLEMENTATION: Codetta 2.0 is implemented as a Python 3 program for MacOS and Linux and is available from http://eddylab.org/software/codetta/codetta2.tar.gz and at http://github.com/kshulgina/codetta. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Eddy S.R."
+                    },
+                    {
+                        "name": "Shulgina Y."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Codetta: predicting the genetic code from nucleotide sequence"
+            },
+            "pmcid": "PMC9825746",
+            "pmid": "36511586"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Genetics",
+            "uri": "http://edamontology.org/topic_3053"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/collapse/collapse.biotools.json b/data/collapse/collapse.biotools.json
new file mode 100644
index 0000000000000..47f5cb2c4d75f
--- /dev/null
+++ b/data/collapse/collapse.biotools.json
@@ -0,0 +1,114 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-28T17:30:49.328701Z",
+    "biotoolsCURIE": "biotools:collapse",
+    "biotoolsID": "collapse",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "aderry@stanford.edu",
+            "name": "Alexander Derry",
+            "orcidid": "http://orcid.org/0000-0003-2076-1184",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "russ.altman@stanford.edu",
+            "name": "Russ B. Altman",
+            "orcidid": "http://orcid.org/0000-0003-3859-2905",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A representation learning framework for identification and characterization of protein structural sites.",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://zenodo.org/record/6903423"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Collapsing methods",
+                    "uri": "http://edamontology.org/operation_3791"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Protein feature detection",
+                    "uri": "http://edamontology.org/operation_3092"
+                },
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/awfderry/COLLAPSE",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-28T17:30:49.331169Z",
+    "license": "MIT",
+    "name": "COLLAPSE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/pro.4541",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.The identification and characterization of the structural sites which contribute to protein function are crucial for understanding biological mechanisms, evaluating disease risk, and developing targeted therapies. However, the quantity of known protein structures is rapidly outpacing our ability to functionally annotate them. Existing methods for function prediction either do not operate on local sites, suffer from high false positive or false negative rates, or require large site-specific training datasets, necessitating the development of new computational methods for annotating functional sites at scale. We present COLLAPSE (Compressed Latents Learned from Aligned Protein Structural Environments), a framework for learning deep representations of protein sites. COLLAPSE operates directly on the 3D positions of atoms surrounding a site and uses evolutionary relationships between homologous proteins as a self-supervision signal, enabling learned embeddings to implicitly capture structure–function relationships within each site. Our representations generalize across disparate tasks in a transfer learning context, achieving state-of-the-art performance on standardized benchmarks (protein–protein interactions and mutation stability) and on the prediction of functional sites from the Prosite database. We use COLLAPSE to search for similar sites across large protein datasets and to annotate proteins based on a database of known functional sites. These methods demonstrate that COLLAPSE is computationally efficient, tunable, and interpretable, providing a general-purpose platform for computational protein analysis.",
+                "authors": [
+                    {
+                        "name": "Altman R.B."
+                    },
+                    {
+                        "name": "Derry A."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "Protein Science",
+                "title": "COLLAPSE: A representation learning framework for identification and characterization of protein structural sites"
+            },
+            "pmcid": "PMC9847082",
+            "pmid": "36519247"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein binding sites",
+            "uri": "http://edamontology.org/topic_3534"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein structure analysis",
+            "uri": "http://edamontology.org/topic_2814"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        }
+    ]
+}
diff --git a/data/combatdb/combatdb.biotools.json b/data/combatdb/combatdb.biotools.json
new file mode 100644
index 0000000000000..a22923ffb5b98
--- /dev/null
+++ b/data/combatdb/combatdb.biotools.json
@@ -0,0 +1,115 @@
+{
+    "additionDate": "2023-01-27T14:46:37.506706Z",
+    "biotoolsCURIE": "biotools:combatdb",
+    "biotoolsID": "combatdb",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "julian@well.ox.ac.uk",
+            "name": "Julian C Knight",
+            "orcidid": "https://orcid.org/0000-0002-0377-5536",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "brian.marsden@cmd.ox.ac.uk",
+            "name": "Brian D Marsden",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "COMBATdb is a multi-omics database for the human blood response in SARS-CoV-2 infection generated by the COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://db.combat.ox.ac.uk",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-27T14:46:37.509283Z",
+    "license": "Not licensed",
+    "name": "COMBATdb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1019",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Advances in our understanding of the nature of the immune response to SARS-CoV-2 infection, and how this varies within and between individuals, is important in efforts to develop targeted therapies and precision medicine approaches. Here we present a database for the COvid-19 Multi-omics Blood ATlas (COMBAT) project, COMBATdb (https://db.combat.ox.ac.uk). This enables exploration of multi-modal datasets arising from profiling of patients with different severities of illness admitted to hospital in the first phase of the pandemic in the UK prior to vaccination, compared with community cases, healthy controls, and patients with all-cause sepsis and influenza. These data include whole blood transcriptomics, plasma proteomics, epigenomics, single-cell multi-omics, immune repertoire sequencing, flow and mass cytometry, and cohort metadata. COMBATdb provides access to the processed data in a well-defined framework of samples, cell types and genes/proteins that allows exploration across the assayed modalities, with functionality including browse, search, download, calculation and visualisation via shiny apps. This advances the ability of users to leverage COMBAT datasets to understand the pathogenesis of COVID-19, and the nature of specific and shared features with other infectious diseases.",
+                "authors": [
+                    {
+                        "name": "Burnham K.L."
+                    },
+                    {
+                        "name": "Knight J.C."
+                    },
+                    {
+                        "name": "Kumar V."
+                    },
+                    {
+                        "name": "Marsden B.D."
+                    },
+                    {
+                        "name": "Mentzer A.J."
+                    },
+                    {
+                        "name": "Wang D."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "COMBATdb: a database for the COVID-19 Multi-Omics Blood ATlas"
+            },
+            "pmcid": "PMC9825482",
+            "pmid": "36353986"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Cytometry",
+            "uri": "http://edamontology.org/topic_3934"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/cometanalyser/cometanalyser.biotools.json b/data/cometanalyser/cometanalyser.biotools.json
new file mode 100644
index 0000000000000..e7de9c8bd7ee5
--- /dev/null
+++ b/data/cometanalyser/cometanalyser.biotools.json
@@ -0,0 +1,120 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-28T17:26:36.371665Z",
+    "biotoolsCURIE": "biotools:cometanalyser",
+    "biotoolsID": "cometanalyser",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Anna Tesei"
+        },
+        {
+            "name": "Attila Beleon"
+        },
+        {
+            "name": "Filippo Piccinini"
+        },
+        {
+            "name": "Sara Pignatta"
+        }
+    ],
+    "description": "CometAnalyser is an open-source deep-learning tool designed for the analysis of both fluorescent and silver-stained wide-field microscopy images. Once the comets are segmented and classified, several intensity/morphological features are automatically exported as a spreadsheet file.",
+    "documentation": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://www.youtube.com/watch?v=vh2VFnMw50A"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://sourceforge.net/p/cometanalyser",
+    "language": [
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-02-28T17:26:36.374294Z",
+    "license": "BSD-3-Clause",
+    "name": "CometAnalyser",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.csbj.2022.07.053",
+            "metadata": {
+                "abstract": "© 2022 The Author(s)Comet assay provides an easy solution to estimate DNA damage in single cells through microscopy assessment. It is widely used in the analysis of genotoxic damages induced by radiotherapy or chemotherapeutic agents. DNA damage is quantified at the single-cell level by computing the displacement between the genetic material within the nucleus, typically called “comet head”, and the genetic material in the surrounding part of the cell, considered as the “comet tail”. Today, the number of works based on Comet Assay analyses is really impressive. In this work, besides revising the solutions available to obtain reproducible and reliable quantitative data, we developed an easy-to-use tool named CometAnalyser. It is designed for the analysis of both fluorescent and silver-stained wide-field microscopy images and allows to automatically segment and classify the comets, besides extracting Tail Moment and several other intensity/morphological features for performing statistical analysis. CometAnalyser is an open-source deep-learning tool. It works with Windows, Macintosh, and UNIX-based systems. Source code, standalone versions, user manual, sample images, video tutorial and further documentation are freely available at: https://sourceforge.net/p/cometanalyser.",
+                "authors": [
+                    {
+                        "name": "Arienti C."
+                    },
+                    {
+                        "name": "Beleon A."
+                    },
+                    {
+                        "name": "Carbonaro A."
+                    },
+                    {
+                        "name": "Castellani G."
+                    },
+                    {
+                        "name": "Horvath P."
+                    },
+                    {
+                        "name": "Martinelli G."
+                    },
+                    {
+                        "name": "Piccinini F."
+                    },
+                    {
+                        "name": "Pignatta S."
+                    },
+                    {
+                        "name": "Tesei A."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "CometAnalyser: A user-friendly, open-source deep-learning microscopy tool for quantitative comet assay analysis"
+            },
+            "pmcid": "PMC9385450",
+            "pmid": "36016714"
+        }
+    ],
+    "toolType": [
+        "Plug-in"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/commap/commap.biotools.json b/data/commap/commap.biotools.json
new file mode 100644
index 0000000000000..77caf00dc5388
--- /dev/null
+++ b/data/commap/commap.biotools.json
@@ -0,0 +1,117 @@
+{
+    "additionDate": "2023-03-09T14:31:13.992583Z",
+    "biotoolsCURIE": "biotools:commap",
+    "biotoolsID": "commap",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "lihuazhang@dicp.ac.cn",
+            "name": "Lihua Zhang",
+            "orcidid": "https://orcid.org/0000-0003-2543-1547",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "liuchaobuaa@buaa.edu.cn",
+            "name": "Chao Liu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "ComMap is a software for protein complexes structural mapping for cross-linking mass spectrometry (CXMS).",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
+                },
+                {
+                    "term": "Molecular docking",
+                    "uri": "http://edamontology.org/operation_0478"
+                },
+                {
+                    "term": "Protein structure analysis",
+                    "uri": "http://edamontology.org/operation_2406"
+                },
+                {
+                    "term": "Simulation analysis",
+                    "uri": "http://edamontology.org/operation_0244"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/DICP1810/ComMap",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-09T14:31:13.996965Z",
+    "license": "Not licensed",
+    "name": "ComMap",
+    "operatingSystem": [
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD077",
+            "metadata": {
+                "abstract": "MOTIVATION: Chemical cross-linking combined with mass spectrometry (CXMS) is now a well-established method for profiling existing protein-protein interactions (PPIs) with partially known structures. It is expected to map the results of CXMS with existing structure databases to study the protein dynamic profile in the structure analysis. However, currently available structure-based analysis software suffers from the difficulty of achieving large-scale analysis. Besides, it is infeasible for structure analysis and data mining on a large scale, since of lacking global measurement of dynamic structure mapping results. RESULTS: ComMap (protein complex structure mapping) is a software designed to perform large-scale structure-based mapping by integrating CXMS data with existing structures. It allows complete the distance calculation of PPIs with existing structures in batch within minutes and provides scores for different PPI-structure pairs of testable hypothetical structural dynamism via a global view. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Liang Z."
+                    },
+                    {
+                        "name": "Liu C."
+                    },
+                    {
+                        "name": "Shan Y."
+                    },
+                    {
+                        "name": "Zhang L."
+                    },
+                    {
+                        "name": "Zhang W."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zhao L."
+                    }
+                ],
+                "date": "2023-02-03T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "ComMap: a software to perform large-scale structure-based mapping for cross-linking mass spectrometry"
+            },
+            "pmcid": "PMC9960907",
+            "pmid": "36804670"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein structure analysis",
+            "uri": "http://edamontology.org/topic_2814"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/commpath/commpath.biotools.json b/data/commpath/commpath.biotools.json
new file mode 100644
index 0000000000000..f7a87eb222949
--- /dev/null
+++ b/data/commpath/commpath.biotools.json
@@ -0,0 +1,127 @@
+{
+    "additionDate": "2023-01-27T14:51:08.556561Z",
+    "biotoolsCURIE": "biotools:commpath",
+    "biotoolsID": "commpath",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "quanc1989@163.com",
+            "name": "Cheng Quan",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "ylu.phd@gmail.com",
+            "name": "Yiming Lu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhougq114@126.com",
+            "name": "Gangqiao Zhou",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An webserver  and R package for inference and analysis of pathway-mediated cell-cell communication chain from single-cell transcriptomics.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network visualisation",
+                    "uri": "http://edamontology.org/operation_3925"
+                },
+                {
+                    "term": "Pathway visualisation",
+                    "uri": "http://edamontology.org/operation_3926"
+                },
+                {
+                    "term": "Scatter plot plotting",
+                    "uri": "http://edamontology.org/operation_2940"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://commpath.omic.tech/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-27T14:51:08.559036Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/yingyonghui/CommPath"
+        }
+    ],
+    "name": "CommPath",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.10.028",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsSingle-cell transcriptomics offers opportunities to investigate ligand-receptor (LR) interactions between heterogeneous cell populations within tissues. However, most existing tools for the inference of intercellular communication do not allow prioritization of functional LR associations that provoke certain biological responses in the receiver cells. In addition, current tools do not enable the identification of the impact on the downstream cell types of the receiver cells. We present CommPath, an open-source R package and webserver, to analyze and visualize the LR interactions and pathway-mediated intercellular communication chain with single-cell transcriptomic data. CommPath curates a comprehensive signaling pathway database to interpret the consequences of LR associations and therefore infers functional LR interactions. Furthermore, CommPath determines cell-cell communication chain by considering both the upstream and downstream cells of user-defined cell populations. Applying CommPath to human hepatocellular carcinoma dataset shows its ability to decipher complex LR interaction patterns and the associated intercellular communication chain, as well as their changes in disease versus homeostasis.",
+                "authors": [
+                    {
+                        "name": "Gao W."
+                    },
+                    {
+                        "name": "Jiang Y."
+                    },
+                    {
+                        "name": "Lu H."
+                    },
+                    {
+                        "name": "Lu Y."
+                    },
+                    {
+                        "name": "Ping J."
+                    },
+                    {
+                        "name": "Quan C."
+                    },
+                    {
+                        "name": "Zhao Z."
+                    },
+                    {
+                        "name": "Zhou G."
+                    },
+                    {
+                        "name": "Zhou G."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "CommPath: An R package for inference and analysis of pathway-mediated cell-cell communication chain from single-cell transcriptomics"
+            },
+            "pmcid": "PMC9647193",
+            "pmid": "36382188"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/complet_plus/complet_plus.biotools.json b/data/complet_plus/complet_plus.biotools.json
new file mode 100644
index 0000000000000..919215d8d43d4
--- /dev/null
+++ b/data/complet_plus/complet_plus.biotools.json
@@ -0,0 +1,72 @@
+{
+    "additionDate": "2023-03-09T14:34:19.665404Z",
+    "biotoolsCURIE": "biotools:complet_plus",
+    "biotoolsID": "complet_plus",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "empr3ss@gmail.com",
+            "name": "Gail L. Rosen",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A computationally scalable method to improve completeness of large-scale protein sequence clustering.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Sequence clustering",
+                    "uri": "http://edamontology.org/operation_0291"
+                },
+                {
+                    "term": "Structure clustering",
+                    "uri": "http://edamontology.org/operation_2844"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/EESI/Complet-Plus",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-03-09T14:34:19.669716Z",
+    "license": "Not licensed",
+    "name": "Complet_plus",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.7717/PEERJ.14779",
+            "pmcid": "PMC9921987",
+            "pmid": "36785708"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene and protein families",
+            "uri": "http://edamontology.org/topic_0623"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/comutdb/comutdb.biotools.json b/data/comutdb/comutdb.biotools.json
new file mode 100644
index 0000000000000..4a388424bf100
--- /dev/null
+++ b/data/comutdb/comutdb.biotools.json
@@ -0,0 +1,91 @@
+{
+    "additionDate": "2023-01-27T14:55:39.518372Z",
+    "biotoolsCURIE": "biotools:comutdb",
+    "biotoolsID": "comutdb",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "yanguo1978@gmail.com",
+            "name": "Yan Guo",
+            "orcidid": "https://orcid.org/0000-0001-5252-3960",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "CoMutDB: the landscape of somatic mutation co-occurrence in cancers",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.innovebioinfo.com/Database/CoMutDB/Home.php",
+    "language": [
+        "JavaScript",
+        "PHP"
+    ],
+    "lastUpdate": "2023-01-27T14:55:39.521036Z",
+    "license": "Not licensed",
+    "name": "CoMutDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC725",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Somatic mutation co-occurrence has been proven to have a profound effect on tumorigenesis. While some studies have been conducted on co-mutations, a centralized resource dedicated to co-mutations in cancer is still lacking. RESULTS: Using multi-omics data from over 30 000 subjects and 1747 cancer cell lines, we present the Cancer co-mutation database (CoMutDB), the most comprehensive resource devoted to describing cancer co-mutations and their characteristics. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in the online database CoMutDB: http://www.innovebioinfo.com/Database/CoMutDB/Home.php.",
+                "authors": [
+                    {
+                        "name": "Guo Y."
+                    },
+                    {
+                        "name": "Jiang L."
+                    },
+                    {
+                        "name": "Tang J."
+                    },
+                    {
+                        "name": "Yu H."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "CoMutDB: the landscape of somatic mutation co-occurrence in cancers"
+            },
+            "pmcid": "PMC9805589",
+            "pmid": "36355452"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/conanvarvar/conanvarvar.biotools.json b/data/conanvarvar/conanvarvar.biotools.json
new file mode 100644
index 0000000000000..44136c97d1202
--- /dev/null
+++ b/data/conanvarvar/conanvarvar.biotools.json
@@ -0,0 +1,114 @@
+{
+    "additionDate": "2023-03-15T15:59:15.218485Z",
+    "biotoolsCURIE": "biotools:conanvarvar",
+    "biotoolsID": "conanvarvar",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "E.Giannoulatou@victorchang.edu.au",
+            "name": "Eleni Giannoulatou",
+            "orcidid": "https://orcid.org/0000-0002-7084-6736",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A versatile tool for the detection of large syndromic copy number variation from whole genome sequencing data",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Copy number variation detection",
+                    "uri": "http://edamontology.org/operation_3961"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "Variant prioritisation",
+                    "uri": "http://edamontology.org/operation_3226"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/VCCRI/ConanVarvar",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-15T15:59:15.223223Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://hub.docker.com/r/mgud/conanvarvar"
+        }
+    ],
+    "name": "ConanVarvar",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-023-05154-X",
+            "metadata": {
+                "abstract": "Background: A wide range of tools are available for the detection of copy number variants (CNVs) from whole-genome sequencing (WGS) data. However, none of them focus on clinically-relevant CNVs, such as those that are associated with known genetic syndromes. Such variants are often large in size, typically 1–5 Mb, but currently available CNV callers have been developed and benchmarked for the discovery of smaller variants. Thus, the ability of these programs to detect tens of real syndromic CNVs remains largely unknown. Results: Here we present ConanVarvar, a tool which implements a complete workflow for the targeted analysis of large germline CNVs from WGS data. ConanVarvar comes with an intuitive R Shiny graphical user interface and annotates identified variants with information about 56 associated syndromic conditions. We benchmarked ConanVarvar and four other programs on a dataset containing real and simulated syndromic CNVs larger than 1 Mb. In comparison to other tools, ConanVarvar reports 10–30 times less false-positive variants without compromising sensitivity and is quicker to run, especially on large batches of samples. Conclusions: ConanVarvar is a useful instrument for primary analysis in disease sequencing studies, where large CNVs could be the cause of disease.",
+                "authors": [
+                    {
+                        "name": "Blue G.M."
+                    },
+                    {
+                        "name": "Dunwoodie S.L."
+                    },
+                    {
+                        "name": "Giannoulatou E."
+                    },
+                    {
+                        "name": "Gudkov M."
+                    },
+                    {
+                        "name": "Khushi M."
+                    },
+                    {
+                        "name": "Thibaut L."
+                    },
+                    {
+                        "name": "Winlaw D.S."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "ConanVarvar: a versatile tool for the detection of large syndromic copy number variation from whole-genome sequencing data"
+            },
+            "pmcid": "PMC9930243",
+            "pmid": "36792982"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Copy number variation",
+            "uri": "http://edamontology.org/topic_3958"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/continuousflex/continuousflex.biotools.json b/data/continuousflex/continuousflex.biotools.json
index bef7349ee3dd4..dcb5d10050948 100644
--- a/data/continuousflex/continuousflex.biotools.json
+++ b/data/continuousflex/continuousflex.biotools.json
@@ -9,6 +9,9 @@
             "name": "Slavica Jonić",
             "orcidid": "https://orcid.org/0000-0001-5112-2743",
             "typeEntity": "Person"
+        },
+        {
+            "name": "Mohamad Harastani"
         }
     ],
     "description": "Hybrid Electron Microscopy Normal Mode Analysis with Scipion.\nPlugin to use continuousflex protocols within the Scipion framework.\nThis plugin provides HEMNMA and StructMap protocols and is frequently updated.",
@@ -42,8 +45,16 @@
         "MATLAB",
         "Python"
     ],
-    "lastUpdate": "2020-12-16T17:49:50Z",
+    "lastUpdate": "2023-01-08T15:43:54.750894Z",
     "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/scipion-em/scipion-em-continuousflex"
+        }
+    ],
     "name": "ContinuousFlex",
     "owner": "Pub2Tools",
     "publication": [
@@ -62,13 +73,41 @@
                         "name": "Sorzano C.O.S."
                     }
                 ],
-                "citationCount": 4,
+                "citationCount": 15,
                 "date": "2020-01-01T00:00:00Z",
                 "journal": "Protein Science",
                 "title": "Hybrid Electron Microscopy Normal Mode Analysis with Scipion"
             },
             "pmcid": "PMC6933837",
             "pmid": "31693263"
+        },
+        {
+            "doi": "10.1016/J.JSB.2022.107906",
+            "metadata": {
+                "abstract": "© 2022 Elsevier Inc.ContinuousFlex is a user-friendly open-source software package for analyzing continuous conformational variability of macromolecules in cryo electron microscopy (cryo-EM) and cryo electron tomography (cryo-ET) data. In 2019, ContinuousFlex became available as a plugin for Scipion, an image processing software package extensively used in the cryo-EM field. Currently, ContinuousFlex contains software for running (1) recently published methods HEMNMA-3D, TomoFlow, and NMMD; (2) earlier published methods HEMNMA and StructMap; and (3) methods for simulating cryo-EM and cryo-ET data with conformational variability and methods for data preprocessing. It also includes external software for molecular dynamics simulation (GENESIS) and normal mode analysis (ElNemo), used in some of the mentioned methods. The HEMNMA software has been presented in the past, but not the software of other methods. Besides, ContinuousFlex currently also offers a deep learning extension of HEMNMA, named DeepHEMNMA. In this article, we review these methods in the context of the ContinuousFlex package, developed to facilitate their use by the community.",
+                "authors": [
+                    {
+                        "name": "Hamitouche I."
+                    },
+                    {
+                        "name": "Harastani M."
+                    },
+                    {
+                        "name": "Jonic S."
+                    },
+                    {
+                        "name": "Moghadam N.B."
+                    },
+                    {
+                        "name": "Vuillemot R."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Journal of Structural Biology",
+                "title": "ContinuousFlex: Software package for analyzing continuous conformational variability of macromolecules in cryo electron microscopy and tomography data"
+            },
+            "pmid": "36244611"
         }
     ],
     "toolType": [
diff --git a/data/copper/copper.biotools.json b/data/copper/copper.biotools.json
new file mode 100644
index 0000000000000..e9b05fbdea196
--- /dev/null
+++ b/data/copper/copper.biotools.json
@@ -0,0 +1,82 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T19:51:09.782695Z",
+    "biotoolsCURIE": "biotools:copper",
+    "biotoolsID": "copper",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Jiangning.Song@monash.edu",
+            "name": "Fuyi Li",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "cangzhijia@dlmu.edu.cn",
+            "name": "Jiangning Song",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "fuyi.li@unimelb.edu.au",
+            "name": "Cangzhi Jia",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An ensemble deep-learning approach for identifying exclusive virus-derived small interfering RNAs in plants.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Homology-based gene prediction",
+                    "uri": "http://edamontology.org/operation_3663"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Sequence feature detection",
+                    "uri": "http://edamontology.org/operation_0253"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/yuanyuanbu/COPPER",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T19:51:09.785411Z",
+    "license": "Not licensed",
+    "name": "COPPER",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BFGP/ELAC049",
+            "pmid": "36528813"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        }
+    ]
+}
diff --git a/data/cordial/cordial.biotools.json b/data/cordial/cordial.biotools.json
new file mode 100644
index 0000000000000..32abf9f4278dd
--- /dev/null
+++ b/data/cordial/cordial.biotools.json
@@ -0,0 +1,96 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T15:00:19.694843Z",
+    "biotoolsCURIE": "biotools:cordial",
+    "biotoolsID": "cordial",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "p.cutillas@qmul.ac.uk",
+            "name": "Pedro R Cutillas",
+            "orcidid": "https://orcid.org/0000-0002-3426-2274",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A R package for convenient correlation analysis",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Enrichment analysis",
+                    "uri": "http://edamontology.org/operation_3501"
+                },
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Pathway analysis",
+                    "uri": "http://edamontology.org/operation_3928"
+                },
+                {
+                    "term": "Weighted correlation network analysis",
+                    "uri": "http://edamontology.org/operation_3766"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/CutillasLab/cordial",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-27T15:00:19.697248Z",
+    "license": "MIT",
+    "name": "cordial",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC769",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Pathway inference methods are important for annotating the genome, for providing insights into the mechanisms of biochemical processes and allow the discovery of signalling members and potential new drug targets. Here, we tested the hypothesis that genes with similar impact on cell viability across multiple cell lines belong to a common pathway, thus providing a conceptual basis for a pathway inference method based on correlated anti-proliferative gene properties. METHODS: To test this concept, we used recently available large-scale RNAi screens to develop a method, termed functional pathway inference analysis (FPIA), to systemically identify correlated gene dependencies. RESULTS: To assess FPIA, we initially focused on PI3K/AKT/MTOR signalling, a prototypic oncogenic pathway for which we have a good sense of ground truth. Dependencies for AKT1, MTOR and PDPK1 were among the most correlated with those for PIK3CA (encoding PI3Kα), as returned by FPIA, whereas negative regulators of PI3K/AKT/MTOR signalling, such as PTEN were anti-correlated. Following FPIA, MTOR, PIK3CA and PIK3CB produced significantly greater correlations for genes in the PI3K-Akt pathway versus other pathways. Application of FPIA to two additional pathways (p53 and MAPK) returned expected associations (e.g. MDM2 and TP53BP1 for p53 and MAPK1 and BRAF for MEK1). Over-representation analysis of FPIA-returned genes enriched the respective pathway, and FPIA restricted to specific tumour lineages uncovered cell type-specific networks. Overall, our study demonstrates the ability of FPIA to identify members of pro-survival biochemical pathways in cancer cells. AVAILABILITY AND IMPLEMENTATION: FPIA is implemented in a new R package named 'cordial' freely available from https://github.com/CutillasLab/cordial. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Badshah I.I."
+                    },
+                    {
+                        "name": "Cutillas P.R."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Systematic identification of biochemical networks in cancer cells by functional pathway inference analysis"
+            },
+            "pmcid": "PMC9805595",
+            "pmid": "36448701"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/cormap/cormap.biotools.json b/data/cormap/cormap.biotools.json
new file mode 100644
index 0000000000000..f5d029b087a65
--- /dev/null
+++ b/data/cormap/cormap.biotools.json
@@ -0,0 +1,123 @@
+{
+    "accessibility": "Restricted access",
+    "additionDate": "2023-01-08T15:55:05.061869Z",
+    "biotoolsCURIE": "biotools:cormap",
+    "biotoolsID": "cormap",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "aheyland@uoguelph.ca",
+            "name": "Andreas Heyland",
+            "orcidid": "https://orcid.org/0000-0002-7592-4473",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Comparative Meta RNA-Seq Data Standardized Analysis Pipeline (CMRP) is a processing frame for the standardized analysis of Meta RNA-Seq raw data from wide-ranged species.",
+    "documentation": [
+        {
+            "type": [
+                "Installation instructions"
+            ],
+            "url": "https://github.com/rubysheng/CoRMAP/blob/mus_comparison/doc/Install.md"
+        },
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/rubysheng/CoRMAP/blob/mus_comparison/doc/Usage.md"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "De-novo assembly",
+                    "uri": "http://edamontology.org/operation_0524"
+                },
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "RNA-Seq analysis",
+                    "uri": "http://edamontology.org/operation_3680"
+                },
+                {
+                    "term": "RNA-Seq quantification",
+                    "uri": "http://edamontology.org/operation_3800"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/rubysheng/CoRMAP.git",
+    "language": [
+        "Bash",
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-08T15:55:05.064473Z",
+    "license": "GPL-3.0",
+    "name": "CoRMAP",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-04972-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Transcriptional regulation is a fundamental mechanism underlying biological functions. In recent years, a broad array of RNA-Seq tools have been used to measure transcription levels in biological experiments, in whole organisms, tissues, and at the single cell level. Collectively, this is a vast comparative dataset on transcriptional processes across organisms. Yet, due to technical differences between the studies (sequencing, experimental design, and analysis) extracting usable comparative information and conducting meta-analyses remains challenging. Results: We introduce Comparative RNA-Seq Metadata Analysis Pipeline (CoRMAP), a meta-analysis tool to retrieve comparative gene expression data from any RNA-Seq dataset using de novo assembly, standardized gene expression tools and the implementation of OrthoMCL, a gene orthology search algorithm. It employs the use of orthogroup assignments to ensure the accurate comparison of gene expression levels between experiments and species. Here we demonstrate the use of CoRMAP on two mouse brain transcriptomes with similar scope, that were collected several years from each other using different sequencing technologies and analysis methods. We also compare the performance of CoRMAP with a functional mapping tool, previously published. Conclusion: CoRMAP provides a framework for the meta-analysis of RNA-Seq data from divergent taxonomic groups. This method facilitates the retrieval and comparison of gene expression levels from published data sets using standardized assembly and analysis. CoRMAP does not rely on reference genomes and consequently facilitates direct comparison between diverse studies on a range of organisms.",
+                "authors": [
+                    {
+                        "name": "Ali R.A."
+                    },
+                    {
+                        "name": "Heyland A."
+                    },
+                    {
+                        "name": "Sheng Y."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "Comparative transcriptomics analysis pipeline for the meta-analysis of phylogenetically divergent datasets (CoRMAP)"
+            },
+            "pmcid": "PMC9547434",
+            "pmid": "36207678"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Gene regulation",
+            "uri": "http://edamontology.org/topic_0204"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/cosbin/cosbin.biotools.json b/data/cosbin/cosbin.biotools.json
new file mode 100644
index 0000000000000..eef8890646dda
--- /dev/null
+++ b/data/cosbin/cosbin.biotools.json
@@ -0,0 +1,78 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T15:03:02.294541Z",
+    "biotoolsCURIE": "biotools:cosbin",
+    "biotoolsID": "cosbin",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yuewang@vt.edu",
+            "name": "Yue Wang",
+            "orcidid": "https://orcid.org/0000-0002-5197-5874",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Cosine score-based iterative normalization of biologically diverse samples.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MinjieSh/Cosbin",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-27T15:03:02.297035Z",
+    "license": "MIT",
+    "name": "Cosbin",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOADV/VBAC076",
+            "pmcid": "PMC9614059",
+            "pmid": "36330358"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Sample collections",
+            "uri": "http://edamontology.org/topic_3277"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/cottonmd/cottonmd.biotools.json b/data/cottonmd/cottonmd.biotools.json
new file mode 100644
index 0000000000000..7694e14efd68d
--- /dev/null
+++ b/data/cottonmd/cottonmd.biotools.json
@@ -0,0 +1,88 @@
+{
+    "additionDate": "2023-01-09T08:01:40.610810Z",
+    "biotoolsCURIE": "biotools:cottonmd",
+    "biotoolsID": "cottonmd",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "yangzuoren@caas.cn",
+            "name": "Zuoren Yang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "yqy@mail.hzau.edu.cn",
+            "name": "Qing-Yong Yang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "CottonMD is a curated and integrated multi-omics resource for cotton. In this database, we integrated and analyzed datasets from genomics, epigenomics, transcriptomics, metabolomics and phenomics, and offer multiple tools for users to make it easy to utilize datasets.",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "http://yanglab.hzau.edu.cn/CottonMD/download"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Gene expression QTL analysis",
+                    "uri": "http://edamontology.org/operation_3232"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://yanglab.hzau.edu.cn/CottonMD/",
+    "lastUpdate": "2023-01-09T08:01:40.614016Z",
+    "license": "Other",
+    "name": "CottonMD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC863",
+            "pmid": "36215030"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biological databases",
+            "uri": "http://edamontology.org/topic_3071"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Phenomics",
+            "uri": "http://edamontology.org/topic_3298"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/cov2_tcr/cov2_tcr.biotools.json b/data/cov2_tcr/cov2_tcr.biotools.json
new file mode 100644
index 0000000000000..1dd699f16a0b4
--- /dev/null
+++ b/data/cov2_tcr/cov2_tcr.biotools.json
@@ -0,0 +1,115 @@
+{
+    "additionDate": "2023-03-15T16:04:25.847639Z",
+    "biotoolsCURIE": "biotools:cov2_tcr",
+    "biotoolsID": "cov2_tcr",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "jianxingxing@foxmail.com",
+            "name": "Xingxing Jian",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xielu@sibpt.com",
+            "name": "Lu Xie",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A web server for screening TCR CDR3 from TCR immune repertoire of COVID-19 patients and their recognized SARS-CoV-2 epitopes.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                },
+                {
+                    "term": "Side chain modelling",
+                    "uri": "http://edamontology.org/operation_0480"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.biostatistics.online/CoV2-TCR/#/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-15T16:04:49.323179Z",
+    "license": "Other",
+    "name": "CoV2-TCR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2023.01.038",
+            "metadata": {
+                "abstract": "Although multiple vaccines have been developed and widely administered, several severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have been reported to evade immune responses and spread diffusely. Here, 108 RNA-seq files from coronavirus disease 2019 (COVID-19) patients and healthy donors (HD) were downloaded to extract their TCR immune repertoire by MiXCR. Those extracted TCR repertoire were compared and it was found that disease progression was related negatively with diversity and positively with clonality. Specifically, greater proportions of high-abundance clonotypes were observed in active and severe COVID-19 samples, probably resulting from strong stimulation of SARS-CoV-2 epitopes and a continued immune response in host. To investigate the specific recognition between TCR CDR3 and SARS-CoV-2 epitopes, we constructed an accurate classifier CoV2-TCR with an AUC of 0.967 in an independent dataset, which outperformed several similar tools. Based on this model, we observed a huge range in the number of those TCR CDR3 recognizing those different peptides, including 28 MHC-I epitopes from SARS-CoV-2 and 22 immunogenic peptides from SARS-CoV-2 variants. Interestingly, their proportions of high-abundance, low-abundance and rare clonotypes were close for each peptide. To expand the potential application of this model, we established the webserver, CoV2-TCR, in which users can obtain those recognizing CDR3 sequences from the TCR repertoire of COVID-19 patients based on the 9-mer peptides containing mutation site(s) on the four main proteins of SARS-CoV-2 variants. Overall, this study provides preliminary screening for candidate antigen epitopes and the TCR CDR3 that recognizes them, and should be helpful for vaccine design on SARS-CoV-2 variants.",
+                "authors": [
+                    {
+                        "name": "Jian X."
+                    },
+                    {
+                        "name": "Lu M."
+                    },
+                    {
+                        "name": "Xie L."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zhao J."
+                    },
+                    {
+                        "name": "Zhao Z."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "CoV2-TCR: A web server for screening TCR CDR3 from TCR immune repertoire of COVID-19 patients and their recognized SARS-CoV-2 epitopes"
+            },
+            "pmcid": "PMC9882952",
+            "pmid": "36741787"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Immunogenetics",
+            "uri": "http://edamontology.org/topic_3930"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Vaccinology",
+            "uri": "http://edamontology.org/topic_3966"
+        }
+    ]
+}
diff --git a/data/cov2clusters/cov2clusters.biotools.json b/data/cov2clusters/cov2clusters.biotools.json
new file mode 100644
index 0000000000000..a6b87678fab62
--- /dev/null
+++ b/data/cov2clusters/cov2clusters.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T08:05:52.842609Z",
+    "biotoolsCURIE": "biotools:cov2clusters",
+    "biotoolsID": "cov2clusters",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "benjamin_sobkowiak@sfu.ca",
+            "name": "Benjamin Sobkowiak",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Stable clustering of SARS-CoV-2 sequences from phylogenetic trees.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Phylogenetic inference",
+                    "uri": "http://edamontology.org/operation_0323"
+                },
+                {
+                    "term": "Phylogenetic tree editing",
+                    "uri": "http://edamontology.org/operation_0326"
+                },
+                {
+                    "term": "Tree dating",
+                    "uri": "http://edamontology.org/operation_3942"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/bensobkowiak/cov2clusters",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-09T08:06:22.039471Z",
+    "license": "MIT",
+    "name": "cov2clusters",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12864-022-08936-4",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: The COVID-19 pandemic remains a global public health concern. Advances in sequencing technologies has allowed for high numbers of SARS-CoV-2 whole genome sequence (WGS) data and rapid sharing of sequences through global repositories to enable almost real-time genomic analysis of the pathogen. WGS data has been used previously to group genetically similar viral pathogens to reveal evidence of transmission, including methods that identify distinct clusters on a phylogenetic tree. Identifying clusters of linked cases can aid in the regional surveillance and management of the disease. In this study, we present a novel method for producing stable genomic clusters of SARS-CoV-2 cases, cov2clusters, and compare the accuracy and stability of our approach to previous methods used for phylogenetic clustering using real-world SARS-CoV-2 sequence data obtained from British Columbia, Canada. Results: We found that cov2clusters produced more stable clusters than previously used phylogenetic clustering methods when adding sequence data through time, mimicking an increase in sequence data through the pandemic. Our method also showed high accuracy when predicting epidemiologically informed clusters from sequence data. Conclusions: Our new approach allows for the identification of stable clusters of SARS-CoV-2 from WGS data. Producing high-resolution SARS-CoV-2 clusters from sequence data alone can a challenge and, where possible, both genomic and epidemiological data should be used in combination.",
+                "authors": [
+                    {
+                        "name": "Colijn C."
+                    },
+                    {
+                        "name": "Hoang L.M.N."
+                    },
+                    {
+                        "name": "Kamelian K."
+                    },
+                    {
+                        "name": "Prystajecky N."
+                    },
+                    {
+                        "name": "Silva A.G."
+                    },
+                    {
+                        "name": "Sobkowiak B."
+                    },
+                    {
+                        "name": "Tyson J."
+                    },
+                    {
+                        "name": "Zlosnik J.E.A."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Genomics",
+                "title": "Cov2clusters: genomic clustering of SARS-CoV-2 sequences"
+            },
+            "pmcid": "PMC9579665",
+            "pmid": "36258173"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        },
+        {
+            "term": "Virology",
+            "uri": "http://edamontology.org/topic_0781"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/covbinderinpdb/covbinderinpdb.biotools.json b/data/covbinderinpdb/covbinderinpdb.biotools.json
new file mode 100644
index 0000000000000..754ce3b8f3679
--- /dev/null
+++ b/data/covbinderinpdb/covbinderinpdb.biotools.json
@@ -0,0 +1,80 @@
+{
+    "additionDate": "2023-02-14T12:19:59.195740Z",
+    "biotoolsCURIE": "biotools:covbinderinpdb",
+    "biotoolsID": "covbinderinpdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yingkai.zhang@nyu.edu",
+            "name": "Yingkai Zhang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "curated CovBinderInPDB database contains 7375 covalent modifications in which 2189 unique covalent binders target nine types of amino acid residues (Cys, Lys, Ser, Asp, Glu, His, Met, Thr, and Tyr) from 3555 complex structures of 1170 unique protein chains.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein design",
+                    "uri": "http://edamontology.org/operation_4008"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://yzhang.hpc.nyu.edu/CovBinderInPDB",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-02-14T12:19:59.198156Z",
+    "license": "Not licensed",
+    "name": "CovBinderInPDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCIM.2C01216",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society. All rights reserved.Covalent inhibition has emerged as a promising orthogonal approach for drug discovery, despite the significant challenge in achieving target specificity. To facilitate the structure-based rational design of target-specific covalent modulators, we developed an integrated computational protocol to curate covalent binders from the RCSB Protein Data Bank (PDB). Starting from the macromolecular crystallographic information files (mmCIF) in the PDB archive, covalent bond records, which indicate the side chain modification of amino acid residue by a covalent binder, were collected and cleaned. Then, residue-binder adducts, which are products of chemical reactions between targeted residues and covalent binders, were recovered with the help of the Chemical Component Dictionary in PDB. Finally, several strategies were employed to curate the pre-reaction forms of covalent binders from the adducts. Our curated CovBinderInPDB database contains 7375 covalent modifications in which 2189 unique covalent binders target nine types of amino acid residues (Cys, Lys, Ser, Asp, Glu, His, Met, Thr, and Tyr) from 3555 complex structures of 1170 unique protein chains. This database would set a solid foundation for developing and benchmarking computational strategies for covalent modulator design and is freely accessible at https://yzhang.hpc.nyu.edu/CovBinderInPDB.",
+                "authors": [
+                    {
+                        "name": "Guo X.-K."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    }
+                ],
+                "date": "2022-12-12T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "CovBinderInPDB: A Structure-Based Covalent Binder Database"
+            },
+            "pmcid": "PMC9772242",
+            "pmid": "36453831"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Protein folding, stability and design",
+            "uri": "http://edamontology.org/topic_0130"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/covic_db/covic_db.biotools.json b/data/covic_db/covic_db.biotools.json
new file mode 100644
index 0000000000000..c66240a2ce4d1
--- /dev/null
+++ b/data/covic_db/covic_db.biotools.json
@@ -0,0 +1,186 @@
+{
+    "additionDate": "2023-03-15T16:09:19.715640Z",
+    "biotoolsCURIE": "biotools:covic_db",
+    "biotoolsID": "covic_db",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "bpeters@lji.org",
+            "name": "Bjoern Peters",
+            "orcidid": "https://orcid.org/0000-0002-8457-6693",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "erica@lji.org",
+            "name": "Erica Ollmann Saphire",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "This database enables systematic analysis and interpretation of this large-scale dataset by providing a comprehensive view of various features such as affinity, neutralization, in vivo protection and effector functions for each antibody.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "Side chain modelling",
+                    "uri": "http://edamontology.org/operation_0480"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://covicdb.lji.org/",
+    "lastUpdate": "2023-03-15T16:09:19.719627Z",
+    "license": "CC-BY-4.0",
+    "name": "COVIC-DB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/DATABASE/BAAC112",
+            "metadata": {
+                "abstract": "The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has seen multiple anti-SARS-CoV-2 antibodies being generated globally. It is difficult, however, to assemble a useful compendium of these biological properties if they are derived from experimental measurements performed at different sites under different experimental conditions. The Coronavirus Immunotherapeutic Consortium (COVIC) circumvents these issues by experimentally testing blinded antibodies side by side for several functional activities. To collect these data in a consistent fashion and make it publicly available, we established the COVIC database (COVIC-DB, https://covicdb.lji.org/). This database enables systematic analysis and interpretation of this large-scale dataset by providing a comprehensive view of various features such as affinity, neutralization, in vivo protection and effector functions for each antibody. Interactive graphs enable direct comparisons of antibodies based on select functional properties. We demonstrate how the COVIC-DB can be utilized to examine relationships among antibody features, thereby guiding the design of therapeutic antibody cocktails. Database URL https://covicdb.lji.org/.",
+                "authors": [
+                    {
+                        "name": "Alter G."
+                    },
+                    {
+                        "name": "Atyeo C."
+                    },
+                    {
+                        "name": "Baric R.S."
+                    },
+                    {
+                        "name": "Bedinger D."
+                    },
+                    {
+                        "name": "Bukreyev A."
+                    },
+                    {
+                        "name": "Dennison S.M."
+                    },
+                    {
+                        "name": "Gagnon L."
+                    },
+                    {
+                        "name": "Gambiez A."
+                    },
+                    {
+                        "name": "Germann T."
+                    },
+                    {
+                        "name": "Greenbaum J.A."
+                    },
+                    {
+                        "name": "Guzman-Orozco H."
+                    },
+                    {
+                        "name": "Ha B."
+                    },
+                    {
+                        "name": "Halfmann P.J."
+                    },
+                    {
+                        "name": "Hastie K.M."
+                    },
+                    {
+                        "name": "Kawaoka Y."
+                    },
+                    {
+                        "name": "Kojima M."
+                    },
+                    {
+                        "name": "Kuzmina N."
+                    },
+                    {
+                        "name": "Li H."
+                    },
+                    {
+                        "name": "Li K."
+                    },
+                    {
+                        "name": "Mahita J."
+                    },
+                    {
+                        "name": "Mendes M."
+                    },
+                    {
+                        "name": "Munt J.E."
+                    },
+                    {
+                        "name": "Osei-Twum M."
+                    },
+                    {
+                        "name": "Overton J.A."
+                    },
+                    {
+                        "name": "Periasamy S."
+                    },
+                    {
+                        "name": "Peters B."
+                    },
+                    {
+                        "name": "Saphire E.O."
+                    },
+                    {
+                        "name": "Schendel S.L."
+                    },
+                    {
+                        "name": "Tomaras G.D."
+                    },
+                    {
+                        "name": "Vita R."
+                    }
+                ],
+                "date": "2023-02-10T00:00:00Z",
+                "journal": "Database : the journal of biological databases and curation",
+                "title": "Coronavirus Immunotherapeutic Consortium Database"
+            },
+            "pmcid": "PMC9913043",
+            "pmid": "36763096"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Allergy, clinical immunology and immunotherapeutics",
+            "uri": "http://edamontology.org/topic_3400"
+        },
+        {
+            "term": "Biotechnology",
+            "uri": "http://edamontology.org/topic_3297"
+        },
+        {
+            "term": "Drug metabolism",
+            "uri": "http://edamontology.org/topic_3375"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        }
+    ]
+}
diff --git a/data/covid-19_serohub/covid-19_serohub.biotools.json b/data/covid-19_serohub/covid-19_serohub.biotools.json
new file mode 100644
index 0000000000000..eb47b29ccc32e
--- /dev/null
+++ b/data/covid-19_serohub/covid-19_serohub.biotools.json
@@ -0,0 +1,135 @@
+{
+    "additionDate": "2023-01-27T16:28:51.494147Z",
+    "biotoolsCURIE": "biotools:covid-19_serohub",
+    "biotoolsID": "covid-19_serohub",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "freedmanne@mail.nih.gov",
+            "name": "Neal D. Freedman",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "the COVID-19 Seroprevalence Studies Hub, known as COVID-19 SeroHub, is an online dashboard intended to help researchers and policymakers monitor studies of Severe Acute Respiratory",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://covid19serohub.nih.gov/",
+    "lastUpdate": "2023-01-27T16:30:01.297485Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://covid19serohub.nih.gov/public/COVID-19_SeroHub_Submission_Template.xlsx"
+        }
+    ],
+    "name": "COVID-19 SeroHub",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41597-022-01830-4",
+            "metadata": {
+                "abstract": "© 2022, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.Seroprevalence studies provide useful information about the proportion of the population either vaccinated against SARS-CoV-2, previously infected with the virus, or both. Numerous studies have been conducted in the United States, but differ substantially by dates of enrollment, target population, geographic location, age distribution, and assays used. This can make it challenging to identify and synthesize available seroprevalence data by geographic region or to compare infection-induced versus combined infection- and vaccination-induced seroprevalence. To facilitate public access and understanding, the National Institutes of Health and the Centers for Disease Control and Prevention developed the COVID-19 Seroprevalence Studies Hub (COVID-19 SeroHub, https://covid19serohub.nih.gov/), a data repository in which seroprevalence studies are systematically identified, extracted using a standard format, and summarized through an interactive interface. Within COVID-19 SeroHub, users can explore and download data from 178 studies as of September 1, 2022. Tools allow users to filter results and visualize trends over time, geography, population, age, and antigen target. Because COVID-19 remains an ongoing pandemic, we will continue to identify and include future studies.",
+                "authors": [
+                    {
+                        "name": "Averhoff F."
+                    },
+                    {
+                        "name": "Bayrak K."
+                    },
+                    {
+                        "name": "Benoit T.J."
+                    },
+                    {
+                        "name": "Brown L."
+                    },
+                    {
+                        "name": "Bu X."
+                    },
+                    {
+                        "name": "Chanock S.J."
+                    },
+                    {
+                        "name": "Coffey B."
+                    },
+                    {
+                        "name": "Freedman N.D."
+                    },
+                    {
+                        "name": "Jackson L."
+                    },
+                    {
+                        "name": "Jones J.M."
+                    },
+                    {
+                        "name": "Kerlavage A.R."
+                    },
+                    {
+                        "name": "Lu A."
+                    },
+                    {
+                        "name": "Newman L.M."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Data",
+                "title": "COVID-19 SeroHub, an online repository of SARS-CoV-2 seroprevalence studies in the United States"
+            },
+            "pmcid": "PMC9701211",
+            "pmid": "36435936"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Allergy, clinical immunology and immunotherapeutics",
+            "uri": "http://edamontology.org/topic_3400"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        }
+    ]
+}
diff --git a/data/covid-gwab/covid-gwab.biotools.json b/data/covid-gwab/covid-gwab.biotools.json
new file mode 100644
index 0000000000000..93d7893349404
--- /dev/null
+++ b/data/covid-gwab/covid-gwab.biotools.json
@@ -0,0 +1,98 @@
+{
+    "additionDate": "2023-01-09T08:11:13.800423Z",
+    "biotoolsCURIE": "biotools:covid-gwab",
+    "biotoolsID": "covid-gwab",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "insuklee@yonsei.ac.kr",
+            "name": "Insuk Lee",
+            "orcidid": "https://orcid.org/0000-0003-3146-6180",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene prediction",
+                    "uri": "http://edamontology.org/operation_2454"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Network visualisation",
+                    "uri": "http://edamontology.org/operation_3925"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://inetbio.org/covidgwab/",
+    "lastUpdate": "2023-01-09T08:11:13.803176Z",
+    "license": "Not licensed",
+    "name": "COVID-GWAB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3390/BIOM12101446",
+            "metadata": {
+                "abstract": "© 2022 by the authors.Host genetics affect both the susceptibility and response to viral infection. Searching for host genes that contribute to COVID-19, the Host Genetics Initiative (HGI) was formed to investigate the genetic factors involved in COVID-19 via genome-wide association studies (GWAS). The GWAS suffer from limited statistical power and in general, only a few genes can pass the conventional significance thresholds. This statistical limitation may be overcome by boosting weak association signals through integrating independent functional information such as molecular interactions. Additionally, the boosted results can be evaluated by various independent data for further connections to COVID-19. We present COVID-GWAB, a web-based tool to boost original GWAS signals from COVID-19 patients by taking the signals of the interactome neighbors. COVID-GWAB takes summary statistics from the COVID-19 HGI or user input data and reprioritizes candidate host genes for COVID-19 using HumanNet, a co-functional human gene network. The current version of COVID-GWAB provides the pre-processed data of releases 5, 6, and 7 of the HGI. Additionally, COVID-GWAB provides web interfaces for a summary of augmented GWAS signals, prediction evaluations by appearance frequency in COVID-19 literature, single-cell transcriptome data, and associated pathways. The web server also enables browsing the candidate gene networks.",
+                "authors": [
+                    {
+                        "name": "Baek S."
+                    },
+                    {
+                        "name": "Lee I."
+                    },
+                    {
+                        "name": "Yang S."
+                    }
+                ],
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "Biomolecules",
+                "title": "COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data"
+            },
+            "pmcid": "PMC9599684",
+            "pmid": "36291657"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genetics",
+            "uri": "http://edamontology.org/topic_3053"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/covidscholar/covidscholar.biotools.json b/data/covidscholar/covidscholar.biotools.json
new file mode 100644
index 0000000000000..33b74829551a9
--- /dev/null
+++ b/data/covidscholar/covidscholar.biotools.json
@@ -0,0 +1,120 @@
+{
+    "additionDate": "2023-03-15T16:13:16.559951Z",
+    "biotoolsCURIE": "biotools:covidscholar",
+    "biotoolsID": "covidscholar",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "jdagdelen@berkeley.edu",
+            "name": "John Dagdelen",
+            "orcidid": "https://orcid.org/0000-0003-2181-4815",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "kristinpersson@berkeley.edu",
+            "name": "Kristin A. Persson",
+            "orcidid": "https://orcid.org/0000-0003-2495-5509",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "This website uses natural language processing (NLP) to power search on a set of research papers related to COVID-19.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Literature search",
+                    "uri": "http://edamontology.org/operation_0305"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://covidscholar.org",
+    "lastUpdate": "2023-03-15T16:13:16.564118Z",
+    "license": "Apache-2.0",
+    "name": "COVIDScholar",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PONE.0281147",
+            "metadata": {
+                "abstract": "The ongoing COVID-19 pandemic produced far-reaching effects throughout society, and science is no exception. The scale, speed, and breadth of the scientific community’s COVID-19 response lead to the emergence of new research at the remarkable rate of more than 250 papers published per day. This posed a challenge for the scientific community as traditional methods of engagement with the literature were strained by the volume of new research being produced. Meanwhile, the urgency of response lead to an increasingly prominent role for preprint servers and a diffusion of relevant research through many channels simultaneously. These factors created a need for new tools to change the way scientific literature is organized and found by researchers. With this challenge in mind, we present an overview of COVIDScholar https://covidscholar.org, an automated knowledge portal which utilizes natural language processing (NLP) that was built to meet these urgent needs. The search interface for this corpus of more than 260,000 research articles, patents, and clinical trials served more than 33,000 users at an average of 2,000 monthly active users and a peak of more than 8,600 weekly active users in the summer of 2020. Additionally, we include an analysis of trends in COVID-19 research over the course of the pandemic with a particular focus on the first 10 months, which represents a unique period of rapid worldwide shift in scientific attention.",
+                "authors": [
+                    {
+                        "name": "Ceder G."
+                    },
+                    {
+                        "name": "Cruse K."
+                    },
+                    {
+                        "name": "Dagdelen J."
+                    },
+                    {
+                        "name": "Fei Y."
+                    },
+                    {
+                        "name": "He T."
+                    },
+                    {
+                        "name": "Huo H."
+                    },
+                    {
+                        "name": "Justus B."
+                    },
+                    {
+                        "name": "Persson K.A."
+                    },
+                    {
+                        "name": "Subramanian A."
+                    },
+                    {
+                        "name": "Trewartha A."
+                    },
+                    {
+                        "name": "Wang Z."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "COVIDScholar: An automated COVID-19 research aggregation and analysis platform"
+            },
+            "pmcid": "PMC9891495",
+            "pmid": "36724184"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        }
+    ]
+}
diff --git a/data/covinter/covinter.biotools.json b/data/covinter/covinter.biotools.json
new file mode 100644
index 0000000000000..64a488ca0e29c
--- /dev/null
+++ b/data/covinter/covinter.biotools.json
@@ -0,0 +1,94 @@
+{
+    "additionDate": "2023-01-09T08:16:47.748755Z",
+    "biotoolsCURIE": "biotools:covinter",
+    "biotoolsID": "covinter",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hanlianyi@ipm-gba.org.cn",
+            "name": "Lianyi Han",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "taolin@hznu.edu.cn",
+            "name": "Lin Tao",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhufeng@zju.edu.cn",
+            "name": "Feng Zhu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Database of SARS-COV-2, SARS-COV, MERS-CoV, HCoV-229E and HCoV-OC43, etc.7 human pathogenic coronaviruses RNAs and host proteins interactions, which are critical for viral infection.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Protein-protein interaction analysis",
+                    "uri": "http://edamontology.org/operation_2949"
+                },
+                {
+                    "term": "RNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3901"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://idrblab.org/covinter/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T08:16:47.751397Z",
+    "license": "Other",
+    "name": "CovInter",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC834",
+            "pmid": "36200814"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/covtriage/covtriage.biotools.json b/data/covtriage/covtriage.biotools.json
new file mode 100644
index 0000000000000..fc55af1551363
--- /dev/null
+++ b/data/covtriage/covtriage.biotools.json
@@ -0,0 +1,142 @@
+{
+    "additionDate": "2023-02-14T12:26:27.684514Z",
+    "biotoolsCURIE": "biotools:covtriage",
+    "biotoolsID": "covtriage",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "deborah.caucheteur@hesge.ch",
+            "name": "Déborah Caucheteur",
+            "orcidid": "https://orcid.org/0000-0003-0800-544X",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Data integration into SIB Literature services, an application ontology (COVoc) and a triage service named COVTriage",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Ontology visualisation",
+                    "uri": "http://edamontology.org/operation_3559"
+                },
+                {
+                    "term": "Text annotation",
+                    "uri": "http://edamontology.org/operation_3778"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://candy.hesge.ch/COVTriage",
+    "language": [
+        "Ruby",
+        "Scala",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-14T12:26:27.687009Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Issue tracker"
+            ],
+            "url": "https://github.com/EBISPOT/covoc/issues"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/EBISPOT/covoc"
+        }
+    ],
+    "name": "COVTriage",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC800",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Since early 2020, the coronavirus disease 2019 (COVID-19) pandemic has confronted the biomedical community with an unprecedented challenge. The rapid spread of COVID-19 and ease of transmission seen worldwide is due to increased population flow and international trade. Front-line medical care, treatment research and vaccine development also require rapid and informative interpretation of the literature and COVID-19 data produced around the world, with 177 500 papers published between January 2020 and November 2021, i.e. almost 8500 papers per month. To extract knowledge and enable interoperability across resources, we developed the COVID-19 Vocabulary (COVoc), an application ontology related to the research on this pandemic. The main objective of COVoc development was to enable seamless navigation from biomedical literature to core databases and tools of ELIXIR, a European-wide intergovernmental organization for life sciences. RESULTS: This collaborative work provided data integration into SIB Literature services, an application ontology (COVoc) and a triage service named COVTriage and based on annotation processing to search for COVID-related information across pre-defined aspects with daily updates. Thanks to its interoperability potential, COVoc lends itself to wider applications, hopefully through further connections with other novel COVID-19 ontologies as has been established with Coronavirus Infectious Disease Ontology. AVAILABILITY AND IMPLEMENTATION: The data at https://github.com/EBISPOT/covoc and the service at https://candy.hesge.ch/COVTriage.",
+                "authors": [
+                    {
+                        "name": "Agosti D."
+                    },
+                    {
+                        "name": "Caucheteur D."
+                    },
+                    {
+                        "name": "Gobeill J."
+                    },
+                    {
+                        "name": "Matentzoglu N."
+                    },
+                    {
+                        "name": "May Pendlington Z."
+                    },
+                    {
+                        "name": "Mottin L."
+                    },
+                    {
+                        "name": "Osumi-Sutherland D."
+                    },
+                    {
+                        "name": "Parkinson H."
+                    },
+                    {
+                        "name": "Roncaglia P."
+                    },
+                    {
+                        "name": "Ruch P."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "COVoc and COVTriage: novel resources to support literature triage"
+            },
+            "pmcid": "PMC9825781",
+            "pmid": "36511598"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        }
+    ]
+}
diff --git a/data/cox/cox.biotools.json b/data/cox/cox.biotools.json
new file mode 100644
index 0000000000000..dcf875ac688c9
--- /dev/null
+++ b/data/cox/cox.biotools.json
@@ -0,0 +1,122 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T10:54:21.203478Z",
+    "biotoolsCURIE": "biotools:cox",
+    "biotoolsID": "cox",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ruilinli@stanford.edu",
+            "name": "Ruilin Li",
+            "orcidid": "https://orcid.org/0000-0002-5152-7086",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Yosuke Tanigawa",
+            "orcidid": "https://orcid.org/0000-0001-9759-157X"
+        },
+        {
+            "name": "Manuel A Rivas",
+            "orcidid": "https://orcid.org/0000-0003-1457-9925",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Robert Tibshirani",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Multi-snpnet-Cox (mrcox) Efficient Group-Sparse Lasso solver for multi-response Cox model.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/rivas-lab/multisnpnet-Cox",
+    "language": [
+        "C++",
+        "R"
+    ],
+    "lastUpdate": "2023-01-26T10:54:21.206017Z",
+    "license": "MIT",
+    "name": "Cox",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAB095",
+            "metadata": {
+                "abstract": "© 2021 The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.Motivation: The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data. Results: We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. Availabilityandimplementation: https://github.com/rivas-lab/multisnpnet-Cox",
+                "authors": [
+                    {
+                        "name": "Hastie T."
+                    },
+                    {
+                        "name": "Justesen J.M."
+                    },
+                    {
+                        "name": "Li R."
+                    },
+                    {
+                        "name": "Rivas M.A."
+                    },
+                    {
+                        "name": "Tanigawa Y."
+                    },
+                    {
+                        "name": "Taylor J."
+                    },
+                    {
+                        "name": "Tibshirani R."
+                    }
+                ],
+                "citationCount": 3,
+                "date": "2021-12-01T00:00:00Z",
+                "journal": "Bioinformatics",
+                "title": "Survival analysis on rare events using group-regularized multi-response Cox regression"
+            },
+            "pmcid": "PMC8652035",
+            "pmid": "33560296"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "Rare diseases",
+            "uri": "http://edamontology.org/topic_3325"
+        }
+    ]
+}
diff --git a/data/coxmkf/coxmkf.biotools.json b/data/coxmkf/coxmkf.biotools.json
new file mode 100644
index 0000000000000..d4e2f59fa3f54
--- /dev/null
+++ b/data/coxmkf/coxmkf.biotools.json
@@ -0,0 +1,103 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-28T14:05:18.412675Z",
+    "biotoolsCURIE": "biotools:coxmkf",
+    "biotoolsID": "coxmkf",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhhliu@hku.hk",
+            "name": "Zhonghua Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Minhao Yao"
+        },
+        {
+            "name": "Peixin Tian"
+        },
+        {
+            "name": "Tao Huang"
+        }
+    ],
+    "description": "A Knockoff Filter for High-Dimensional Mediation Analysis with a Survival Outcome in Epigenetic Studies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Filtering",
+                    "uri": "http://edamontology.org/operation_3695"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MinhaoYaooo/CoxMKF",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-28T14:05:18.415043Z",
+    "license": "Not licensed",
+    "name": "CoxMKF",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac687",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.MOTIVATION: It is of scientific interest to identify DNA methylation CpG sites that might mediate the effect of an environmental exposure on a survival outcome in high-dimensional mediation analysis. However, there is a lack of powerful statistical methods that can provide a guarantee of false discovery rate (FDR) control in finite-sample settings. RESULTS: In this article, we propose a novel method called CoxMKF, which applies aggregation of multiple knockoffs to a Cox proportional hazards model for a survival outcome with high-dimensional mediators. The proposed CoxMKF can achieve FDR control even in finite-sample settings, which is particularly advantageous when the sample size is not large. Moreover, our proposed CoxMKF can overcome the randomness of the unstable model-X knockoffs. Our simulation results show that CoxMKF controls FDR well in finite samples. We further apply CoxMKF to a lung cancer dataset from The Cancer Genome Atlas (TCGA) project with 754 subjects and 365 306 DNA methylation CpG sites, and identify four DNA methylation CpG sites that might mediate the effect of smoking on the overall survival among lung cancer patients. AVAILABILITY AND IMPLEMENTATION: The R package CoxMKF is publicly available at https://github.com/MinhaoYaooo/CoxMKF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Huang T."
+                    },
+                    {
+                        "name": "Liu Z."
+                    },
+                    {
+                        "name": "Tian P."
+                    },
+                    {
+                        "name": "Yao M."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-11-30T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "CoxMKF: a knockoff filter for high-dimensional mediation analysis with a survival outcome in epigenetic studies"
+            },
+            "pmid": "36255264"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/cplot/cplot.biotools.json b/data/cplot/cplot.biotools.json
new file mode 100644
index 0000000000000..8a679848d1152
--- /dev/null
+++ b/data/cplot/cplot.biotools.json
@@ -0,0 +1,104 @@
+{
+    "additionDate": "2023-01-09T08:22:34.438026Z",
+    "biotoolsCURIE": "biotools:cplot",
+    "biotoolsID": "cplot",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "gangman@dongguk.edu",
+            "name": "Gangman Yi",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A visualized contig plotting application for analysis of short read alignment of nucleotide sequences",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Read mapping",
+                    "uri": "http://edamontology.org/operation_3198"
+                },
+                {
+                    "term": "Reverse complement",
+                    "uri": "http://edamontology.org/operation_0363"
+                },
+                {
+                    "term": "Sequence alignment",
+                    "uri": "http://edamontology.org/operation_0292"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "Sequence visualisation",
+                    "uri": "http://edamontology.org/operation_0564"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://datalab.dongguk.edu/cPlot",
+    "language": [
+        "JavaScript",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T08:22:34.440625Z",
+    "license": "Not licensed",
+    "name": "cPlot",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3390/IJMS231911484",
+            "metadata": {
+                "abstract": "© 2022 by the authors.Advances in the next-generation sequencing technology have led to a dramatic decrease in read-generation cost and an increase in read output. Reconstruction of short DNA sequence reads generated by next-generation sequencing requires a read alignment method that reconstructs a reference genome. In addition, it is essential to analyze the results of read alignments for a biologically meaningful inference. However, read alignment from vast amounts of genomic data from various organisms is challenging in that it involves repeated automatic and manual analysis steps. We, here, devised cPlot software for read alignment of nucleotide sequences, with automated read alignment and position analysis, which allows visual assessment of the analysis results by the user. cPlot compares sequence similarity of reads by performing multiple read alignments, with FASTA format files as the input. This application provides a web-based interface for the user for facile implementation, without the need for a dedicated computing environment. cPlot identifies the location and order of the sequencing reads by comparing the sequence to a genetically close reference sequence in a way that is effective for visualizing the assembly of short reads generated by NGS and rapid gene map construction.",
+                "authors": [
+                    {
+                        "name": "Ji M."
+                    },
+                    {
+                        "name": "Jung J."
+                    },
+                    {
+                        "name": "Kan Y."
+                    },
+                    {
+                        "name": "Kim D."
+                    },
+                    {
+                        "name": "Yi G."
+                    }
+                ],
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "International Journal of Molecular Sciences",
+                "title": "cPlot: Contig-Plotting Visualization for the Analysis of Short-Read Nucleotide Sequence Alignments"
+            },
+            "pmcid": "PMC9570162",
+            "pmid": "36232783"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/cppa/cppa.biotools.json b/data/cppa/cppa.biotools.json
new file mode 100644
index 0000000000000..c44356625c5d3
--- /dev/null
+++ b/data/cppa/cppa.biotools.json
@@ -0,0 +1,103 @@
+{
+    "additionDate": "2023-02-14T12:31:37.322910Z",
+    "biotoolsCURIE": "biotools:cppa",
+    "biotoolsID": "cppa",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Guo-sheng Hu"
+        }
+    ],
+    "description": "CPPA (Cancer Proteome and Phosphoproteome Atlas), a web tool to mine abnormalities of the proteome and phosphoproteome in cancer based on published data sets.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Differential protein expression profiling",
+                    "uri": "http://edamontology.org/operation_3741"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cppa.site/cppa",
+    "language": [
+        "JavaScript",
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-02-14T12:31:37.326218Z",
+    "license": "Not licensed",
+    "name": "CPPA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JPROTEOME.2C00512",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Published by American Chemical Society.A tremendous amount of proteomic and phosphoproteomic data has been produced over the years with the development of mass spectrometry techniques, providing us with new opportunities to explore and understand the proteome and phosphoproteome as well as the function of proteins and protein phosphorylation sites. However, a lack of powerful tools that we can utilize to explore these valuable data limits our understanding of the proteome and phosphoproteome, particularly in diseases such as cancer. To address these unmet needs, we established CPPA (Cancer Proteome and Phosphoproteome Atlas), a web tool to mine abnormalities of the proteome and phosphoproteome in cancer based on published data sets. All analysis results are presented in CPPA with a flexible web interface to provide key customization utilities, including general analysis, differential expression profiling, statistical analysis of protein phosphorylation sites, correlation analysis, similarity analysis, survival analysis, pathological stage analysis, etc. CPPA greatly facilitates the process of data mining and therapeutic target discovery by providing a comprehensive analysis of proteomic and phosphoproteomic data in normal and tumor tissues with a simple click, which helps to unlock the precious value of mass spectrometry data by bridging the gap between raw data and experimental biologists. CPPA is currently available at https://cppa.site/cppa.",
+                "authors": [
+                    {
+                        "name": "He Y.-H."
+                    },
+                    {
+                        "name": "Hu G.-S."
+                    },
+                    {
+                        "name": "Liu W."
+                    },
+                    {
+                        "name": "Wang D.-C."
+                    },
+                    {
+                        "name": "Zheng Z.-Z."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Journal of Proteome Research",
+                "title": "CPPA: A Web Tool for Exploring Proteomic and Phosphoproteomic Data in Cancer"
+            },
+            "pmid": "36507870"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
+}
diff --git a/data/cramdb/cramdb.biotools.json b/data/cramdb/cramdb.biotools.json
new file mode 100644
index 0000000000000..0c5253091e0a8
--- /dev/null
+++ b/data/cramdb/cramdb.biotools.json
@@ -0,0 +1,145 @@
+{
+    "additionDate": "2023-01-27T16:33:26.779150Z",
+    "biotoolsCURIE": "biotools:cramdb",
+    "biotoolsID": "cramdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chent@nrc.ac.cn",
+            "name": "Tong Chen",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "hushengwei@163.com",
+            "name": "Wei Ni",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Shengwei Hu",
+            "orcidid": "https://orcid.org/0000-0001-8849-265X",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "CRAMdb (a database for composition and roles of animal microbiome) is a comprehensive resource of curated and consistently annotated metagenomes for non-human animals",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.ehbio.com/CRAMdb/",
+    "language": [
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-27T16:33:26.781744Z",
+    "license": "Other",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/Tong-Chen/CRAMdb"
+        }
+    ],
+    "name": "CRAMdb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC973",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.CRAMdb (a database for composition and roles of animal microbiome) is a comprehensive resource of curated and consistently annotated metagenomes for non-human animals. It focuses on the composition and roles of the microbiome in various animal species. The main goal of the CRAMdb is to facilitate the reuse of animal metagenomic data, and enable cross-host and cross-phenotype comparisons. To this end, we consistently annotated microbiomes (including 16S, 18S, ITS and metagenomics sequencing data) of 516 animals from 475 projects spanning 43 phenotype pairs to construct the database that is equipped with 9430 bacteria, 278 archaea, 2216 fungi and 458 viruses. CRAMdb provides two main contents: microbiome composition data, illustrating the landscape of the microbiota (bacteria, archaea, fungi, and viruses) in various animal species, and microbiome association data, revealing the relationships between the microbiota and various phenotypes across different animal species. More importantly, users can quickly compare the composition of the microbiota of interest cross-host or body site and the associated taxa that differ between phenotype pairs cross-host or cross-phenotype. CRAMdb is freely available at (http://www.ehbio.com/CRAMdb).",
+                "authors": [
+                    {
+                        "name": "Chen T."
+                    },
+                    {
+                        "name": "Cui C."
+                    },
+                    {
+                        "name": "Hu S."
+                    },
+                    {
+                        "name": "Lei B."
+                    },
+                    {
+                        "name": "Lei Y."
+                    },
+                    {
+                        "name": "Li C."
+                    },
+                    {
+                        "name": "Li F."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Liu C."
+                    },
+                    {
+                        "name": "Ni W."
+                    },
+                    {
+                        "name": "Wang L."
+                    },
+                    {
+                        "name": "Xu Y."
+                    },
+                    {
+                        "name": "Yang Q."
+                    },
+                    {
+                        "name": "Zhou P."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "CRAMdb: a comprehensive database for composition and roles of microbiome in animals"
+            },
+            "pmcid": "PMC9825719",
+            "pmid": "36318246"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/creammist/creammist.biotools.json b/data/creammist/creammist.biotools.json
new file mode 100644
index 0000000000000..cc2117a07bf4b
--- /dev/null
+++ b/data/creammist/creammist.biotools.json
@@ -0,0 +1,90 @@
+{
+    "additionDate": "2023-01-09T08:27:59.495695Z",
+    "biotoolsCURIE": "biotools:creammist",
+    "biotoolsID": "creammist",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "hatairat.y@cmu.ac.th",
+            "name": "Hatairat Yingtaweesittikul",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "suphavilaic@gis.a-star.edu.sg",
+            "name": "Chayaporn Suphavilai",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "CREAMMIST is an integrated cancer drug sensitivity database for in vitro pharmacogenomics analysis, providing an integrative dose-response curve across five widely used cancer cell-line drug-response datasets (CCLE, GDSC1, GDSC2, CTRP1, CTRP2).",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://creammist.mtms.dev/doc/dose_response_curve/"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://creammist.mtms.dev",
+    "lastUpdate": "2023-01-09T08:27:59.498248Z",
+    "license": "Not licensed",
+    "name": "CREAMMIST",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC911",
+            "pmid": "36259664"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pharmacogenomics",
+            "uri": "http://edamontology.org/topic_0208"
+        }
+    ]
+}
diff --git a/data/cresil/cresil.biotools.json b/data/cresil/cresil.biotools.json
new file mode 100644
index 0000000000000..d93cce61ea069
--- /dev/null
+++ b/data/cresil/cresil.biotools.json
@@ -0,0 +1,81 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T08:34:18.011208Z",
+    "biotoolsCURIE": "biotools:cresil",
+    "biotoolsID": "cresil",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "INookaew@uams.edu",
+            "name": "Intawat Nookaew",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A tool for detecting eccDNA from Nanopore reads",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Read mapping",
+                    "uri": "http://edamontology.org/operation_3198"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/visanuwan/cresil",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T08:34:18.013910Z",
+    "license": "MIT",
+    "name": "CReSIL",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC422",
+            "pmid": "36198068"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/crisprbase/crisprbase.biotools.json b/data/crisprbase/crisprbase.biotools.json
new file mode 100644
index 0000000000000..44a8da72b986d
--- /dev/null
+++ b/data/crisprbase/crisprbase.biotools.json
@@ -0,0 +1,143 @@
+{
+    "additionDate": "2023-01-27T16:40:43.658251Z",
+    "biotoolsCURIE": "biotools:crisprbase",
+    "biotoolsID": "crisprbase",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "fengbiaomao@bjmu.edu.cn",
+            "name": "Fengbiao Mao",
+            "orcidid": "https://orcid.org/0000-0003-0852-4266",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "lixiangxue@hsc.pku.edu.cn",
+            "name": "Lixiang Xue",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "sunlichao@cicams.ac.cn",
+            "name": "Lichao Sun",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "CRISPRbase is a comprehensive database curating the outcome and evaluating off-target effects of base editors on various cell types and tissues in dozens of species",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "http://crisprbase.beyondthe.top:580/welcome/tutorial.html"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://crisprbase.maolab.org",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-01-27T16:40:43.660718Z",
+    "license": "Not licensed",
+    "name": "CRISPRbase",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC967",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.CRISPR-Cas base editing (BE) system is a powerful tool to expand the scope and efficiency of genome editing with single-nucleotide resolution. The editing efficiency, product purity, and off-target effect differ among various BE systems. Herein, we developed CRISPRbase (http://crisprbase.maolab.org), by integrating 1 252 935 records of base editing outcomes in more than 50 cell types from 17 species. CRISPRbase helps to evaluate the putative editing precision of different BE systems by integrating multiple annotations, functional predictions and a blasting system for single-guide RNA sequences. We systematically assessed the editing window, editing efficiency and product purity of various BE systems. Intensive efforts were focused on increasing the editing efficiency and product purity of base editors since the byproduct could be detrimental in certain applications. Remarkably, more than half of cancer-related off-target mutations were non-synonymous and extremely damaging to protein functions in most common tumor types. Luckily, most of these cancer-related mutations were passenger mutations (4840/5703, 84.87%) rather than cancer driver mutations (863/5703, 15.13%), indicating a weak effect of off-target mutations on carcinogenesis. In summary, CRISPRbase is a powerful and convenient tool to study the outcomes of different base editors and help researchers choose appropriate BE designs for functional studies.",
+                "authors": [
+                    {
+                        "name": "Chen X."
+                    },
+                    {
+                        "name": "Fan J."
+                    },
+                    {
+                        "name": "Li K."
+                    },
+                    {
+                        "name": "Liu Q."
+                    },
+                    {
+                        "name": "Mao F."
+                    },
+                    {
+                        "name": "Shi L."
+                    },
+                    {
+                        "name": "Song R."
+                    },
+                    {
+                        "name": "Su J."
+                    },
+                    {
+                        "name": "Sun L."
+                    },
+                    {
+                        "name": "Wang F."
+                    },
+                    {
+                        "name": "Xue L."
+                    },
+                    {
+                        "name": "Zhou D."
+                    },
+                    {
+                        "name": "Zhu Z."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "Annotation and evaluation of base editing outcomes in multiple cell types using CRISPRbase"
+            },
+            "pmcid": "PMC9825451",
+            "pmid": "36350608"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Genetic engineering",
+            "uri": "http://edamontology.org/topic_3912"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/crisprcleanr/crisprcleanr.biotools.json b/data/crisprcleanr/crisprcleanr.biotools.json
new file mode 100644
index 0000000000000..71f3db2b29cd8
--- /dev/null
+++ b/data/crisprcleanr/crisprcleanr.biotools.json
@@ -0,0 +1,118 @@
+{
+    "additionDate": "2023-03-15T16:23:16.604527Z",
+    "biotoolsCURIE": "biotools:crisprcleanr",
+    "biotoolsID": "crisprcleanr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge (with restrictions)",
+    "credit": [
+        {
+            "email": "francesco.iorio@fht.org",
+            "name": "Francesco Iorio",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A R/Python package and an interactive web application, for processing, correcting, and visualizing genome-wide pooled CRISPR-Cas9 screens.",
+    "download": [
+        {
+            "type": "Test data",
+            "url": "https://github.com/francescojm/CRISPRcleanR/tree/master/inst/extdata"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://crisprcleanr-webapp.fht.org/",
+    "language": [
+        "Python",
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2023-03-15T16:23:16.609226Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://dockstore.org/containers/quay.io/wtsicgp/dockstore-pycrisprcleanr"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/cancerit/pyCRISPRcleanR"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/francescojm/CRISPRcleanR"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://zenodo.org/record/7347915"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://zenodo.org/record/7347952"
+        }
+    ],
+    "name": "CRISPRcleanR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.CRMETH.2022.100373",
+            "pmcid": "PMC9939378",
+            "pmid": "36814834"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/crispron_off/crispron_off.biotools.json b/data/crispron_off/crispron_off.biotools.json
new file mode 100644
index 0000000000000..c3386feb72d90
--- /dev/null
+++ b/data/crispron_off/crispron_off.biotools.json
@@ -0,0 +1,89 @@
+{
+    "additionDate": "2023-01-09T08:43:09.554581Z",
+    "biotoolsCURIE": "biotools:crispron_off",
+    "biotoolsID": "crispron_off",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "gorodkin@rth.dk",
+            "name": "Jan Gorodkin",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Webservers for CRISPR Cas9 on- and off-target predictions.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Promoter prediction",
+                    "uri": "http://edamontology.org/operation_0440"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://rth.dk/resources/crispr/crispron/",
+    "language": [
+        "C",
+        "JavaScript",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T08:43:09.557404Z",
+    "license": "Other",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://rth.dk/resources/crispr/crisproff/"
+        },
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://rth.dk/resources/crispr/crispron/"
+        }
+    ],
+    "name": "CRISPRon_off",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC697",
+            "pmid": "36271848"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Genetic engineering",
+            "uri": "http://edamontology.org/topic_3912"
+        },
+        {
+            "term": "Probes and primers",
+            "uri": "http://edamontology.org/topic_0632"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/crisprverse/crisprverse.biotools.json b/data/crisprverse/crisprverse.biotools.json
new file mode 100644
index 0000000000000..38a7160d700a5
--- /dev/null
+++ b/data/crisprverse/crisprverse.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T16:49:56.869024Z",
+    "biotoolsCURIE": "biotools:crisprverse",
+    "biotoolsID": "crisprverse",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "fortin946@gmail.com",
+            "name": "Jean-Philippe Fortin",
+            "orcidid": "https://orcid.org/0000-0001-9328-3852",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies.",
+    "documentation": [
+        {
+            "type": [
+                "Training material"
+            ],
+            "url": "https://github.com/crisprVerse/Tutorials"
+        }
+    ],
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://github.com/crisprVerse/Docker"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Genome indexing",
+                    "uri": "http://edamontology.org/operation_3211"
+                },
+                {
+                    "term": "SNP annotation",
+                    "uri": "http://edamontology.org/operation_3661"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/crisprVerse",
+    "language": [
+        "Python",
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-27T16:49:56.871525Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/crisprVerse/crisprVersePaper"
+        }
+    ],
+    "name": "crisprVerse",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41467-022-34320-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).The success of CRISPR-mediated gene perturbation studies is highly dependent on the quality of gRNAs, and several tools have been developed to enable optimal gRNA design. However, these tools are not all adaptable to the latest CRISPR modalities or nucleases, nor do they offer comprehensive annotation methods for advanced CRISPR applications. Here, we present a new ecosystem of R packages, called crisprVerse, that enables efficient gRNA design and annotation for a multitude of CRISPR technologies. This includes CRISPR knockout (CRISPRko), CRISPR activation (CRISPRa), CRISPR interference (CRISPRi), CRISPR base editing (CRISPRbe) and CRISPR knockdown (CRISPRkd). The core package, crisprDesign, offers a user-friendly and unified interface to add off-target annotations, rich gene and SNP annotations, and on- and off-target activity scores. These functionalities are enabled for any RNA- or DNA-targeting nucleases, including Cas9, Cas12, and Cas13. The crisprVerse ecosystem is open-source and deployed through the Bioconductor project (https://github.com/crisprVerse).",
+                "authors": [
+                    {
+                        "name": "Fortin J.-P."
+                    },
+                    {
+                        "name": "Hoberecht L."
+                    },
+                    {
+                        "name": "Lun A."
+                    },
+                    {
+                        "name": "Perampalam P."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies"
+            },
+            "pmcid": "PMC9630310",
+            "pmid": "36323688"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/crit/crit.biotools.json b/data/crit/crit.biotools.json
new file mode 100644
index 0000000000000..b8a146cede7a5
--- /dev/null
+++ b/data/crit/crit.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T16:54:03.079843Z",
+    "biotoolsCURIE": "biotools:crit",
+    "biotoolsID": "crit",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "jzxu01@stu.edu.cn",
+            "name": "Jianzhen Xu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "CRIT (CircRNA Regulator Identification Tool) is a pipeline based on a non-negative matrix factorization method to integrate various omics information and to identify regulating RBPs.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "RNA binding site prediction",
+                    "uri": "http://edamontology.org/operation_3902"
+                },
+                {
+                    "term": "RNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3901"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/BioinformaticsSTU/CRIT",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-27T16:54:03.082258Z",
+    "license": "Not licensed",
+    "name": "CRIT",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.OMTN.2022.10.015",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsCircular RNAs (circRNAs) are endogenous non-coding RNAs that regulate gene expression and participate in carcinogenesis. However, the RNA-binding proteins (RBPs) involved in circRNAs biogenesis and modulation remain largely unclear. We developed the circRNA regulator identification tool (CRIT), a non-negative matrix-factorization-based pipeline to identify regulating RBPs in cancers. CRIT uncovered 73 novel regulators across thousands of samples by effectively leveraging genomics data and functional annotations. We demonstrated that known RBPs involved in circRNA control are significantly enriched in these predictions. Analysis of circRNA-RBP interactions using two large cross-linking immunoprecipitation (CLIP) databases, we validated the consistency between CRIT prediction and the CLIP experiments. Furthermore, newly discovered RBPs are functionally connected with authentic circRNA regulators by various biological associations, such as physical interaction, similar binding motifs, common transcription factor modulation, and co-expression. When analyzing RNA sequencing (RNA-seq) datasets after short hairpin RNA (shRNA)/small interfering RNA (siRNA) knockdown, we found several novel RBPs that can affect global circRNA expression, which strengthens their role in the circRNA life cycle. The above evidence provided independent confirmation that CRIT is a useful tool to capture RBPs in circRNA processing. Finally, we show that authentic regulators are more likely the core splicing proteins and peripheral factors and usually harbor more alterations in the vast majority of cancers.",
+                "authors": [
+                    {
+                        "name": "Cai Y."
+                    },
+                    {
+                        "name": "Chen Q."
+                    },
+                    {
+                        "name": "Gao X."
+                    },
+                    {
+                        "name": "Hao S."
+                    },
+                    {
+                        "name": "Jiang L."
+                    },
+                    {
+                        "name": "Shao M."
+                    },
+                    {
+                        "name": "Xu J."
+                    },
+                    {
+                        "name": "Zhao X."
+                    }
+                ],
+                "date": "2022-12-13T00:00:00Z",
+                "journal": "Molecular Therapy - Nucleic Acids",
+                "title": "CRIT: Identifying RNA-binding protein regulator in circRNA life cycle via non-negative matrix factorization"
+            },
+            "pmcid": "PMC9664520",
+            "pmid": "36420213"
+        }
+    ],
+    "toolType": [
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3794"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/crmss/crmss.biotools.json b/data/crmss/crmss.biotools.json
new file mode 100644
index 0000000000000..83977f1473bad
--- /dev/null
+++ b/data/crmss/crmss.biotools.json
@@ -0,0 +1,103 @@
+{
+    "additionDate": "2023-02-14T12:35:48.559128Z",
+    "biotoolsCURIE": "biotools:crmss",
+    "biotoolsID": "crmss",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jxwang@mail.csu.edu.cn",
+            "name": "Jianxin Wang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Scripts for predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Binding site prediction",
+                    "uri": "http://edamontology.org/operation_2575"
+                },
+                {
+                    "term": "RNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3901"
+                },
+                {
+                    "term": "Sequence feature detection",
+                    "uri": "http://edamontology.org/operation_0253"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/BioinformaticsCSU/CRMSS",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-14T12:35:48.561673Z",
+    "license": "GPL-3.0",
+    "name": "CRMSS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC530",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Circular RNAs (circRNAs) are reverse-spliced and covalently closed RNAs. Their interactions with RNA-binding proteins (RBPs) have multiple effects on the progress of many diseases. Some computational methods are proposed to identify RBP binding sites on circRNAs but suffer from insufficient accuracy, robustness and explanation. In this study, we first take the characteristics of both RNA and RBP into consideration. We propose a method for discriminating circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features, called CRMSS. For circRNAs, we use sequence ${k}\\hbox{-}{mer}$ embedding and the forming probabilities of local secondary structures as features. For RBPs, we combine sequence and structure frequencies of RNA-binding domain regions to generate features. We capture binding patterns with multi-scale residual blocks. With BiLSTM and attention mechanism, we obtain the contextual information of high-level representation for circRNA-RBP binding. To validate the effectiveness of CRMSS, we compare its predictive performance with other methods on 37 RBPs. Taking the properties of both circRNAs and RBPs into account, CRMSS achieves superior performance over state-of-the-art methods. In the case study, our model provides reliable predictions and correctly identifies experimentally verified circRNA-RBP pairs. The code of CRMSS is freely available at https://github.com/BioinformaticsCSU/CRMSS.",
+                "authors": [
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Lu C."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Zeng M."
+                    },
+                    {
+                        "name": "Zhang L."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "CRMSS: predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features"
+            },
+            "pmid": "36511222"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "RNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3794"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ]
+}
diff --git a/data/crnasp12/crnasp12.biotools.json b/data/crnasp12/crnasp12.biotools.json
new file mode 100644
index 0000000000000..e23ea8b7d8e2e
--- /dev/null
+++ b/data/crnasp12/crnasp12.biotools.json
@@ -0,0 +1,108 @@
+{
+    "additionDate": "2023-03-15T16:29:12.691729Z",
+    "biotoolsCURIE": "biotools:crnasp12",
+    "biotoolsID": "crnasp12",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xuxiaojun@jsut.edu.cn",
+            "name": "Xiaojun Xu",
+            "orcidid": "https://orcid.org/0000-0002-6380-698X",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "cRNAsp12 Web Server for the Prediction of Circular RNA Secondary Structures and Stabilities.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "RNA secondary structure alignment",
+                    "uri": "http://edamontology.org/operation_0502"
+                },
+                {
+                    "term": "RNA secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0278"
+                },
+                {
+                    "term": "RNA structure prediction",
+                    "uri": "http://edamontology.org/operation_2441"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://xxulab.org.cn/crnasp12",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-03-15T16:29:12.698065Z",
+    "license": "Other",
+    "name": "cRNAsp12",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3390/IJMS24043822",
+            "metadata": {
+                "abstract": "Circular RNAs (circRNAs) are a novel class of non-coding RNA that, unlike linear RNAs, form a covalently closed loop without the 5′ and 3′ ends. Growing evidence shows that circular RNAs play important roles in life processes and have great potential implications in clinical and research fields. The accurate modeling of circRNAs structure and stability has far-reaching impact on our understanding of their functions and our ability to develop RNA-based therapeutics. The cRNAsp12 server offers a user-friendly web interface to predict circular RNA secondary structures and folding stabilities from the sequence. Through the helix-based landscape partitioning strategy, the server generates distinct ensembles of structures and predicts the minimal free energy structures for each ensemble with the recursive partition function calculation and backtracking algorithms. For structure predictions in the limited structural ensemble, the server also provides users with the option to set the structural constraints of forcing the base pairs and/or forcing the unpaired bases, such that only structures that meet the criteria are enumerated recursively.",
+                "authors": [
+                    {
+                        "name": "Li B."
+                    },
+                    {
+                        "name": "Li W."
+                    },
+                    {
+                        "name": "Tong Y."
+                    },
+                    {
+                        "name": "Wang F."
+                    },
+                    {
+                        "name": "Xie L."
+                    },
+                    {
+                        "name": "Xu X."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "International Journal of Molecular Sciences",
+                "title": "cRNAsp12 Web Server for the Prediction of Circular RNA Secondary Structures and Stabilities"
+            },
+            "pmcid": "PMC9959564",
+            "pmid": "36835231"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Nucleic acid structure analysis",
+            "uri": "http://edamontology.org/topic_0097"
+        },
+        {
+            "term": "Protein secondary structure",
+            "uri": "http://edamontology.org/topic_3542"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        }
+    ]
+}
diff --git a/data/cronos/cronos.biotools.json b/data/cronos/cronos.biotools.json
new file mode 100644
index 0000000000000..3633b68796d55
--- /dev/null
+++ b/data/cronos/cronos.biotools.json
@@ -0,0 +1,74 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T08:47:52.132055Z",
+    "biotoolsCURIE": "biotools:cronos",
+    "biotoolsID": "cronos",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ilias.lagkouvardos@tum.de",
+            "name": "Ilias Lagkouvardos",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Cronos, an analytical pipeline written in R. Cronos' inputs are a microbial composition table (e.g., OTU table), their phylogenetic relations as a tree, and the associated metadata.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "RNA-seq time series data analysis",
+                    "uri": "http://edamontology.org/operation_3565"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Lagkouvardos/Cronos",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-09T08:47:52.134686Z",
+    "license": "MIT",
+    "name": "Cronos",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FBINF.2022.866902",
+            "pmcid": "PMC9580867",
+            "pmid": "36304308"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/cross-attention_phv/cross-attention_phv.biotools.json b/data/cross-attention_phv/cross-attention_phv.biotools.json
new file mode 100644
index 0000000000000..96fa230706e0e
--- /dev/null
+++ b/data/cross-attention_phv/cross-attention_phv.biotools.json
@@ -0,0 +1,94 @@
+{
+    "additionDate": "2023-01-09T08:53:20.585589Z",
+    "biotoolsCURIE": "biotools:cross-attention_phv",
+    "biotoolsID": "cross-attention_phv",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "kurata@bio.kyutech.ac.jp",
+            "name": "Hiroyuki Kurata",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Prediction of human and virus protein-protein interactions using cross-attention-based neural networks.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/kuratahiroyuki/Cross-Attention_PHV",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T08:53:20.588118Z",
+    "license": "Apache-2.0",
+    "name": "cross-attention PHV",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.10.012",
+            "metadata": {
+                "abstract": "© 2022 The Author(s)Viral infections represent a major health concern worldwide. The alarming rate at which SARS-CoV-2 spreads, for example, led to a worldwide pandemic. Viruses incorporate genetic material into the host genome to hijack host cell functions such as the cell cycle and apoptosis. In these viral processes, protein–protein interactions (PPIs) play critical roles. Therefore, the identification of PPIs between humans and viruses is crucial for understanding the infection mechanism and host immune responses to viral infections and for discovering effective drugs. Experimental methods including mass spectrometry-based proteomics and yeast two-hybrid assays are widely used to identify human-virus PPIs, but these experimental methods are time-consuming, expensive, and laborious. To overcome this problem, we developed a novel computational predictor, named cross-attention PHV, by implementing two key technologies of the cross-attention mechanism and a one-dimensional convolutional neural network (1D-CNN). The cross-attention mechanisms were very effective in enhancing prediction and generalization abilities. Application of 1D-CNN to the word2vec-generated feature matrices reduced computational costs, thus extending the allowable length of protein sequences to 9000 amino acid residues. Cross-attention PHV outperformed existing state-of-the-art models using a benchmark dataset and accurately predicted PPIs for unknown viruses. Cross-attention PHV also predicted human–SARS-CoV-2 PPIs with area under the curve values >0.95. The Cross-attention PHV web server and source codes are freely available at https://kurata35.bio.kyutech.ac.jp/Cross-attention_PHV/ and https://github.com/kuratahiroyuki/Cross-Attention_PHV, respectively.",
+                "authors": [
+                    {
+                        "name": "Kurata H."
+                    },
+                    {
+                        "name": "Tsukiyama S."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "Cross-attention PHV: Prediction of human and virus protein-protein interactions using cross-attention–based neural networks"
+            },
+            "pmcid": "PMC9546503",
+            "pmid": "36249566"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein interaction experiment",
+            "uri": "http://edamontology.org/topic_3957"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/cscs/cscs.biotools.json b/data/cscs/cscs.biotools.json
new file mode 100644
index 0000000000000..4c52363fe3a9d
--- /dev/null
+++ b/data/cscs/cscs.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-10T01:05:18.374509Z",
+    "biotoolsCURIE": "biotools:cscs",
+    "biotoolsID": "cscs",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhangyijing@cemps.ac.cn",
+            "name": "Yijing Zhang",
+            "orcidid": "https://orcid.org/0000-0001-9568-9389",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhaofei@cemps.ac.cn",
+            "name": "Fei Zhao",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Tengfei Tang"
+        },
+        {
+            "name": "Xiaojuan Ran"
+        }
+    ],
+    "description": "A chromatin state interface for Chinese Spring bread wheat.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome assembly",
+                    "uri": "http://edamontology.org/operation_0525"
+                },
+                {
+                    "term": "Map drawing",
+                    "uri": "http://edamontology.org/operation_0573"
+                },
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bioinfo.cemps.ac.cn/CSCS/",
+    "lastUpdate": "2023-01-10T01:05:18.377036Z",
+    "name": "CSCS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1007/S42994-021-00048-Z",
+            "metadata": {
+                "abstract": "© 2021, Agricultural Information Institute, Chinese Academy of Agricultural Sciences.A chromosome-level genome assembly of the bread wheat variety Chinese Spring (CS) has recently been published. Genome-wide identification of regulatory elements (REs) responsible for regulating gene activity is key to further mechanistic studies. Because epigenetic activity can reflect RE activity, defining chromatin states based on epigenomic features is an effective way to detect REs. Here, we present the web-based platform Chinese Spring chromatin state (CSCS), which provides CS chromatin signature information. CSCS includes 15 recently published epigenomic data sets including open chromatin and major chromatin marks, which are further partitioned into 15 distinct chromatin states. CSCS curates detailed information about these chromatin states, with trained self-organization mapping (SOM) for segments in all chromatin states and JBrowse visualization for genomic regions or genes. Motif analysis for genomic regions or genes, GO analysis for genes and SOM analysis for new epigenomic data sets are also integrated into CSCS. In summary, the CSCS database contains the combinatorial patterns of chromatin signatures in wheat and facilitates the detection of functional elements and further clarification of regulatory activities. We illustrate how CSCS enables biological insights using one example, demonstrating that CSCS is a highly useful resource for intensive data mining. CSCS is available at http://bioinfo.cemps.ac.cn/CSCS/.",
+                "authors": [
+                    {
+                        "name": "Ran X."
+                    },
+                    {
+                        "name": "Tang T."
+                    },
+                    {
+                        "name": "Wang M."
+                    },
+                    {
+                        "name": "Ye L."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zhao F."
+                    },
+                    {
+                        "name": "Zhuang Y."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2021-12-01T00:00:00Z",
+                "journal": "aBIOTECH",
+                "title": "CSCS: a chromatin state interface for Chinese Spring bread wheat"
+            },
+            "pmcid": "PMC9590471",
+            "pmid": "36311809"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ]
+}
diff --git a/data/csm-toxin/csm-toxin.biotools.json b/data/csm-toxin/csm-toxin.biotools.json
new file mode 100644
index 0000000000000..0bc35cd8d6777
--- /dev/null
+++ b/data/csm-toxin/csm-toxin.biotools.json
@@ -0,0 +1,90 @@
+{
+    "additionDate": "2023-03-15T16:34:56.863478Z",
+    "biotoolsCURIE": "biotools:csm-toxin",
+    "biotoolsID": "csm-toxin",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "d.ascher@uq.edu.au",
+            "name": "David B. Ascher",
+            "orcidid": "https://orcid.org/0000-0003-2948-2413",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A novel in-silico protein toxicity classifier, which relies solely on the protein primary sequence.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://biosig.lab.uq.edu.au/csm_toxin",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-15T16:34:56.867939Z",
+    "license": "Not licensed",
+    "name": "CSM-Toxin",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3390/PHARMACEUTICS15020431",
+            "metadata": {
+                "abstract": "Biologics are one of the most rapidly expanding classes of therapeutics, but can be associated with a range of toxic properties. In small-molecule drug development, early identification of potential toxicity led to a significant reduction in clinical trial failures, however we currently lack robust qualitative rules or predictive tools for peptide- and protein-based biologics. To address this, we have manually curated the largest set of high-quality experimental data on peptide and protein toxicities, and developed CSM-Toxin, a novel in-silico protein toxicity classifier, which relies solely on the protein primary sequence. Our approach encodes the protein sequence information using a deep learning natural languages model to understand “biological” language, where residues are treated as words and protein sequences as sentences. The CSM-Toxin was able to accurately identify peptides and proteins with potential toxicity, achieving an MCC of up to 0.66 across both cross-validation and multiple non-redundant blind tests, outperforming other methods and highlighting the robust and generalisable performance of our model. We strongly believe the CSM-Toxin will serve as a valuable platform to minimise potential toxicity in the biologic development pipeline. Our method is freely available as an easy-to-use webserver.",
+                "authors": [
+                    {
+                        "name": "Ascher D.B."
+                    },
+                    {
+                        "name": "Morozov V."
+                    },
+                    {
+                        "name": "Rodrigues C.H.M."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "Pharmaceutics",
+                "title": "CSM-Toxin: A Web-Server for Predicting Protein Toxicity"
+            },
+            "pmcid": "PMC9966851",
+            "pmid": "36839752"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/csrep/csrep.biotools.json b/data/csrep/csrep.biotools.json
new file mode 100644
index 0000000000000..c9cabb0bf43f1
--- /dev/null
+++ b/data/csrep/csrep.biotools.json
@@ -0,0 +1,113 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-22T01:22:02.894147Z",
+    "biotoolsCURIE": "biotools:csrep",
+    "biotoolsID": "csrep",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jason.ernst@ucla.edu",
+            "name": "Jason Ernst",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Petko Fiziev"
+        },
+        {
+            "name": "Zane Koch"
+        },
+        {
+            "name": "Ha Vu",
+            "orcidid": "http://orcid.org/0000-0002-1131-7375"
+        }
+    ],
+    "description": "A framework for summarizing chromatin state annotations within and identifying differential annotations across groups of samples.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/ernstlab/csrep/blob/master/tutorial.md"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/ernstlab/csrep",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-01-22T01:22:02.896591Z",
+    "license": "Not licensed",
+    "name": "CSREP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac722",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Genome-wide maps of epigenetic modifications are powerful resources for non-coding genome annotation. Maps of multiple epigenetics marks have been integrated into cell or tissue type-specific chromatin state annotations for many cell or tissue types. With the increasing availability of multiple chromatin state maps for biologically similar samples, there is a need for methods that can effectively summarize the information about chromatin state annotations within groups of samples and identify differences across groups of samples at a high resolution. RESULTS: We developed CSREP, which takes as input chromatin state annotations for a group of samples. CSREP then probabilistically estimates the state at each genomic position and derives a representative chromatin state map for the group. CSREP uses an ensemble of multi-class logistic regression classifiers that predict the chromatin state assignment of each sample given the state maps from all other samples. The difference in CSREP's probability assignments for the two groups can be used to identify genomic locations with differential chromatin state assignments. Using groups of chromatin state maps of a diverse set of cell and tissue types, we demonstrate the advantages of using CSREP to summarize chromatin state maps and identify biologically relevant differences between groups at a high resolution. AVAILABILITY AND IMPLEMENTATION: The CSREP source code and generated data are available at http://github.com/ernstlab/csrep. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Ernst J."
+                    },
+                    {
+                        "name": "Fiziev P."
+                    },
+                    {
+                        "name": "Koch Z."
+                    },
+                    {
+                        "name": "Vu H."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "A framework for group-wise summarization and comparison of chromatin state annotations"
+            },
+            "pmcid": "PMC9805555",
+            "pmid": "36342196"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        }
+    ]
+}
diff --git a/data/ctc_5/ctc_5.biotools.json b/data/ctc_5/ctc_5.biotools.json
new file mode 100644
index 0000000000000..858d0e8840908
--- /dev/null
+++ b/data/ctc_5/ctc_5.biotools.json
@@ -0,0 +1,162 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-15T16:42:23.287361Z",
+    "biotoolsCURIE": "biotools:ctc_5",
+    "biotoolsID": "ctc_5",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ffrenchg@tcd.ie",
+            "name": "B. Ffrench",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A novel digital pathology approach to characterise circulating tumour cell biodiversity.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/CTC5/",
+    "language": [
+        "Java"
+    ],
+    "lastUpdate": "2023-03-15T16:42:23.291487Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://www.tcd.ie/medicine/histopathology/research/ctc-5/index.php"
+        }
+    ],
+    "name": "CTC-5",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.HELIYON.2023.E13044",
+            "metadata": {
+                "abstract": "Metastatic progression and tumor evolution complicates the clinical management of cancer patients. Circulating tumor cell (CTC) characterization is a growing discipline that aims to elucidate tumor metastasis and evolution processes. CTCs offer the clinical potential to monitor cancer patients for therapy response, disease relapse, and screen ‘at risk' groups for the onset of malignancy. However, such clinical utility is currently limited to breast, prostate, and colorectal cancer patients. Further understanding of the basic CTC biology of other malignancies is required to progress them towards clinical utility. Unfortunately, such basic clinical research is often limited by restrictive characterization methods and high-cost barrier to entry for CTC isolation and imaging infrastructure. As experimental clinical results on applications of CTC are accumulating, it is becoming clear that a two-tier system of CTC isolation and characterization is required. The first tier is to facilitate basic research into CTC characterization. This basic research then informs a second tier specialised in clinical prognostic and diagnostic testing. This study presented in this manuscript describes the development and application of a low-cost, CTC isolation and characterization pipeline; CTC-5. This approach uses an established ‘isolation by size’ approach (ScreenCell Cyto) and combines histochemical morphology stains and multiparametric immunofluorescence on the same isolated CTCs. This enables capture and characterization of CTCs independent of biomarker-based pre-selection and accommodates both single CTCs and clusters of CTCs. Additionally, the developed open-source software is provided to facilitate the synchronization of microscopy data from multiple sources (https://github.com/CTC5/). This enables high parameter histochemical and immunofluorescent analysis of CTCs with existing microscopy infrastructure without investment in CTC specific imaging hardware. Our approach confirmed by the number of successful tests represents a potential major advance towards highly accessible low-cost technology aiming at the basic research tier of CTC isolation and characterization. The biomarker independent approach facilitates closing the gap between malignancies with poorly, and well-defined CTC phenotypes. As is currently the case for some of the most commonly occurring breast, prostate and colorectal cancers, such advances will ultimately benefit the patient, as early detection of relapse or onset of malignancy strongly correlates with their prognosis.",
+                "authors": [
+                    {
+                        "name": "AbuSaadeh F."
+                    },
+                    {
+                        "name": "Brooks D.A."
+                    },
+                    {
+                        "name": "Brooks R.D."
+                    },
+                    {
+                        "name": "Charmsaz S."
+                    },
+                    {
+                        "name": "Cocchiglia S."
+                    },
+                    {
+                        "name": "Ffrench B."
+                    },
+                    {
+                        "name": "Flavin R."
+                    },
+                    {
+                        "name": "Gallagher M."
+                    },
+                    {
+                        "name": "Gleeson N."
+                    },
+                    {
+                        "name": "Huang Y."
+                    },
+                    {
+                        "name": "Kamran W."
+                    },
+                    {
+                        "name": "Kashdan E."
+                    },
+                    {
+                        "name": "Martin C."
+                    },
+                    {
+                        "name": "O'Brien C."
+                    },
+                    {
+                        "name": "O'Leary J.J."
+                    },
+                    {
+                        "name": "O'Riain C."
+                    },
+                    {
+                        "name": "O'Toole S.A."
+                    },
+                    {
+                        "name": "Scholz D."
+                    },
+                    {
+                        "name": "Selemidis S."
+                    },
+                    {
+                        "name": "Spillane C.D."
+                    },
+                    {
+                        "name": "Vareslija D."
+                    },
+                    {
+                        "name": "Young L."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Heliyon",
+                "title": "CTC-5: A novel digital pathology approach to characterise circulating tumour cell biodiversity"
+            },
+            "pmcid": "PMC9898658",
+            "pmid": "36747925"
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "fiji",
+            "type": "uses"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Biodiversity",
+            "uri": "http://edamontology.org/topic_3050"
+        },
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/ctd_r/ctd_r.biotools.json b/data/ctd_r/ctd_r.biotools.json
new file mode 100644
index 0000000000000..1c7c1401f0c3c
--- /dev/null
+++ b/data/ctd_r/ctd_r.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T11:02:17.003087Z",
+    "biotoolsCURIE": "biotools:ctd_r",
+    "biotoolsID": "ctd_r",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "amilosav@bcm.edu",
+            "name": "Aleksandar Milosavljevic",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Lillian R Thistlethwaite"
+        },
+        {
+            "name": "Varduhi Petrosyan"
+        },
+        {
+            "name": "Xiqi Li"
+        }
+    ],
+    "description": "An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Metabolic network modelling",
+                    "uri": "http://edamontology.org/operation_3660"
+                },
+                {
+                    "term": "Network visualisation",
+                    "uri": "http://edamontology.org/operation_3925"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.rdocumentation.org/packages/CTD/versions/1.1.0",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-26T11:02:17.005652Z",
+    "license": "MIT",
+    "name": "CTD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PCBI.1008550",
+            "metadata": {
+                "abstract": "© 2021 Thistlethwaite et al.We consider the following general family of algorithmic problems that arises in transcriptomics, metabolomics and other fields: given a weighted graph G and a subset of its nodes S, find subsets of S that show significant connectedness within G. A specific solution to this problem may be defined by devising a scoring function, the Maximum Clique problem being a classic example, where S includes all nodes in G and where the score is defined by the size of the largest subset of S fully connected within G. Major practical obstacles for the plethora of algorithms addressing this type of problem include computational efficiency and, particularly for more complex scores which take edge weights into account, the computational cost of permutation testing, a statistical procedure required to obtain a bound on the p-value for a connectedness score. To address these problems, we developed CTD, \"Connect the Dots\", a fast algorithm based on data compression that detects highly connected subsets within S. CTD provides information-theoretic upper bounds on p-values when S contains a small fraction of nodes in G without requiring computationally costly permutation testing. We apply the CTD algorithm to interpret multi-metabolite perturbations due to inborn errors of metabolism and multi-transcript perturbations associated with breast cancer in the context of disease-specific Gaussian Markov Random Field networks learned directly from respective molecular profiling data.",
+                "authors": [
+                    {
+                        "name": "Elsea S.H."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Miller M.J."
+                    },
+                    {
+                        "name": "Milosavljevic A."
+                    },
+                    {
+                        "name": "Petrosyan V."
+                    },
+                    {
+                        "name": "Thistlethwaite L.R."
+                    }
+                ],
+                "citationCount": 6,
+                "date": "2021-01-29T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models"
+            },
+            "pmcid": "PMC7875364",
+            "pmid": "33513132"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/ctdquerier/ctdquerier.biotools.json b/data/ctdquerier/ctdquerier.biotools.json
index 4119573460e73..065faa1759d5f 100644
--- a/data/ctdquerier/ctdquerier.biotools.json
+++ b/data/ctdquerier/ctdquerier.biotools.json
@@ -3,6 +3,15 @@
     "biotoolsCURIE": "biotools:ctdquerier",
     "biotoolsID": "ctdquerier",
     "credit": [
+        {
+            "email": "carles.hernandez@isglobal.org",
+            "name": "Carles Hernandez-Ferrer",
+            "orcidid": "https://orcid.org/0000-0002-8029-7160",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Developer"
+            ]
+        },
         {
             "email": "juanr.gonzalez@isglobal.org",
             "name": "Juan R Gonzalez",
@@ -10,10 +19,24 @@
             "typeRole": [
                 "Primary contact"
             ]
+        },
+        {
+            "email": "xavier.escriba@isglobal.org",
+            "name": "Xavier Escribà Montagut",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Maintainer"
+            ]
         }
     ],
     "description": "Comparative Toxicogenomics Database data extraction, visualization and enrichment of environmental and toxicological studies.",
     "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://bioconductor.org/packages/release/bioc/html/CTDquerier.html"
+        },
         {
             "type": [
                 "General"
@@ -21,9 +44,17 @@
             "url": "https://github.com/isglobal-brge/CTDquerier"
         }
     ],
+    "download": [
+        {
+            "type": "Software package",
+            "url": "https://bioconductor.org/packages/release/bioc/src/contrib/CTDquerier_2.6.0.tar.gz",
+            "version": "2.6.0"
+        }
+    ],
     "editPermission": {
         "authors": [
-            "brgelab"
+            "brgelab",
+            "chernan3"
         ],
         "type": "group"
     },
@@ -41,7 +72,15 @@
     "language": [
         "R"
     ],
-    "lastUpdate": "2021-01-11T10:41:10Z",
+    "lastUpdate": "2023-02-07T13:01:26.320334Z",
+    "link": [
+        {
+            "type": [
+                "Mirror"
+            ],
+            "url": "https://bioconductor.org/packages/CTDquerier"
+        }
+    ],
     "name": "CTDquerier",
     "operatingSystem": [
         "Linux",
@@ -62,13 +101,19 @@
                         "name": "Hernandez-Ferrer C."
                     }
                 ],
-                "citationCount": 2,
+                "citationCount": 7,
                 "date": "2018-09-15T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "CTDquerier: A bioconductor R package for comparative toxicogenomics database™ data extraction, visualization and enrichment of environmental and toxicological studies"
             }
         }
     ],
+    "relation": [
+        {
+            "biotoolsID": "rexposome",
+            "type": "uses"
+        }
+    ],
     "toolType": [
         "Library"
     ],
diff --git a/data/ctpathway/ctpathway.biotools.json b/data/ctpathway/ctpathway.biotools.json
new file mode 100644
index 0000000000000..180d9f6ee423e
--- /dev/null
+++ b/data/ctpathway/ctpathway.biotools.json
@@ -0,0 +1,167 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-10T01:01:49.472079Z",
+    "biotoolsCURIE": "biotools:ctpathway",
+    "biotoolsID": "ctpathway",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "christine.eischen@jefferson.edu",
+            "name": "Christine M. Eischen",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "weijiang@nuaa.edu.cn",
+            "name": "Wei Jiang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Haizhou Liu"
+        },
+        {
+            "name": "Mengqin Yuan"
+        }
+    ],
+    "description": "A CrossTalk-based pathway enrichment analysis method for cancer research.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene ID",
+                        "uri": "http://edamontology.org/data_2295"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Gene symbol",
+                        "uri": "http://edamontology.org/data_1026"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Locus ID (EntrezGene)",
+                        "uri": "http://edamontology.org/data_1904"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Expression profile pathway mapping",
+                    "uri": "http://edamontology.org/operation_0533"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                },
+                {
+                    "term": "Pathway visualisation",
+                    "uri": "http://edamontology.org/operation_3926"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.jianglab.cn/CTpathway/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-10T01:01:49.474651Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/Bioccjw/CTpathway"
+        }
+    ],
+    "name": "CTpathway",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13073-022-01119-6",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated. Methods: To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes. Results: Analysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy. Conclusions: Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/. The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway.",
+                "authors": [
+                    {
+                        "name": "Eischen C.M."
+                    },
+                    {
+                        "name": "Hou F."
+                    },
+                    {
+                        "name": "Huang Y.-E."
+                    },
+                    {
+                        "name": "Jiang W."
+                    },
+                    {
+                        "name": "Lei W."
+                    },
+                    {
+                        "name": "Liu H."
+                    },
+                    {
+                        "name": "Long M."
+                    },
+                    {
+                        "name": "Mitra R."
+                    },
+                    {
+                        "name": "Yuan M."
+                    },
+                    {
+                        "name": "Zhou S."
+                    },
+                    {
+                        "name": "Zhou X."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Genome Medicine",
+                "title": "CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research"
+            },
+            "pmcid": "PMC9563764",
+            "pmid": "36229842"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/ctpc/ctpc.biotools.json b/data/ctpc/ctpc.biotools.json
new file mode 100644
index 0000000000000..7cc47815dd5d5
--- /dev/null
+++ b/data/ctpc/ctpc.biotools.json
@@ -0,0 +1,104 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-28T14:01:15.239633Z",
+    "biotoolsCURIE": "biotools:ctpc",
+    "biotoolsID": "ctpc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "siyuan.cheng@lsuhs.edu",
+            "name": "Siyuan Cheng",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xiuping.yu@lsuhs.edu",
+            "name": "Xiuping Yu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Combined transcriptome dataset of prostate cancer cell lines.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene symbol",
+                        "uri": "http://edamontology.org/data_1026"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pcatools.shinyapps.io/CTPC_V2/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-28T14:01:15.242328Z",
+    "name": "CTPC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/pros.24448",
+            "metadata": {
+                "abstract": "© 2022 Wiley Periodicals LLC.Background: Cell lines are the most used model system in cancer research. The transcriptomic data of established prostate cancer (PCa) cell lines help researchers explore differential gene expressions across the various PCa cell lines. Methods: Through large scale datamining, we established a curated Combined Transcriptome dataset of PCa Cell lines (CTPC) which contains the transcriptomic data of 1840 samples of 9 commonly used PCa cell lines including LNCaP, LNCaP-95, LNCaP-abl, C4-2, VCaP, 22Rv1, PC3, DU145, and NCI-H660. Results: The CTPC dataset provides an opportunity for researchers to not only compare gene expression across different PCa cell lines but also retrieve the experiment information and associate the differential gene expression data with meta data, such as gene manipulation and drug treatment information. Additionally, based on the CTPC dataset, we built a platform for users to visualize the data (https://pcatools.shinyapps.io/CTPC_V2/). Conclusions: It is our hope that the combined CTPC dataset and the user-friendly platform are of great service to the PCa research community.",
+                "authors": [
+                    {
+                        "name": "Cheng S."
+                    },
+                    {
+                        "name": "Yu X."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "Prostate",
+                "title": "CTPC, a combined transcriptome data set of human prostate cancer cell lines"
+            },
+            "pmcid": "PMC9771918",
+            "pmid": "36207780"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/ctrr-ncrna/ctrr-ncrna.biotools.json b/data/ctrr-ncrna/ctrr-ncrna.biotools.json
new file mode 100644
index 0000000000000..d24eeb149e95a
--- /dev/null
+++ b/data/ctrr-ncrna/ctrr-ncrna.biotools.json
@@ -0,0 +1,66 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-10T00:55:17.724836Z",
+    "biotoolsCURIE": "biotools:ctrr-ncrna",
+    "biotoolsID": "ctrr-ncrna",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Bairong Shen",
+            "orcidid": "https://orcid.org/0000-0003-2899-1531"
+        },
+        {
+            "name": "Shumin Ren",
+            "orcidid": "https://orcid.org/0000-0002-1376-1891"
+        },
+        {
+            "name": "Tong Tang",
+            "orcidid": "https://orcid.org/0000-0003-1657-612X"
+        },
+        {
+            "name": "Xingyun Liu",
+            "orcidid": "https://orcid.org/0000-0002-9295-2767"
+        }
+    ],
+    "description": "A Knowledgebase for Cancer Therapy Resistance and Recurrence Associated Non-coding RNAs.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "http://ctrr.bioinf.org.cn/",
+    "lastUpdate": "2023-01-10T00:55:17.727542Z",
+    "name": "CTRR-ncRNA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.GPB.2022.10.003",
+            "pmid": "36265769"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/cubids/cubids.biotools.json b/data/cubids/cubids.biotools.json
new file mode 100644
index 0000000000000..125608cf6299f
--- /dev/null
+++ b/data/cubids/cubids.biotools.json
@@ -0,0 +1,171 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-22T01:37:14.628233Z",
+    "biotoolsCURIE": "biotools:cubids",
+    "biotoolsID": "cubids",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Matthew Cieslak",
+            "orcidid": "http://orcid.org/0000-0002-1931-4734"
+        },
+        {
+            "name": "Sydney Covitz",
+            "orcidid": "http://orcid.org/0000-0002-7430-4125"
+        },
+        {
+            "name": "Theodore D. Satterthwaite",
+            "orcidid": "http://orcid.org/0000-0001-7072-9399"
+        },
+        {
+            "name": "Tinashe M. Tapera",
+            "orcidid": "http://orcid.org/0000-0001-9080-5010"
+        }
+    ],
+    "description": "A workflow and software package for streamlining reproducible curation of large BIDS datasets.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://cubids.readthedocs.io/en/latest/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/PennLINC/CuBIDS",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-22T01:37:14.630846Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://pypi.org/project/cubids/"
+        }
+    ],
+    "name": "CuBIDS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.neuroimage.2022.119609",
+            "metadata": {
+                "abstract": "© 2022The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled “Curation of BIDS” (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad––a version control software package for data––as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images’ metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.",
+                "authors": [
+                    {
+                        "name": "Adebimpe A."
+                    },
+                    {
+                        "name": "Alexander-Bloch A.F."
+                    },
+                    {
+                        "name": "Bertolero M.A."
+                    },
+                    {
+                        "name": "Cieslak M."
+                    },
+                    {
+                        "name": "Covitz S."
+                    },
+                    {
+                        "name": "Fair D.A."
+                    },
+                    {
+                        "name": "Feczko E."
+                    },
+                    {
+                        "name": "Franco A.R."
+                    },
+                    {
+                        "name": "Gur R.C."
+                    },
+                    {
+                        "name": "Gur R.E."
+                    },
+                    {
+                        "name": "Hendrickson T."
+                    },
+                    {
+                        "name": "Houghton A."
+                    },
+                    {
+                        "name": "Mehta K."
+                    },
+                    {
+                        "name": "Milham M.P."
+                    },
+                    {
+                        "name": "Murtha K."
+                    },
+                    {
+                        "name": "Perrone A.J."
+                    },
+                    {
+                        "name": "Robert-Fitzgerald T."
+                    },
+                    {
+                        "name": "Satterthwaite T.D."
+                    },
+                    {
+                        "name": "Schabdach J.M."
+                    },
+                    {
+                        "name": "Shinohara R.T."
+                    },
+                    {
+                        "name": "Tapera T.M."
+                    },
+                    {
+                        "name": "Vogel J.W."
+                    },
+                    {
+                        "name": "Zhao C."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "NeuroImage",
+                "title": "Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets"
+            },
+            "pmid": "36064140"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Data submission, annotation and curation",
+            "uri": "http://edamontology.org/topic_0219"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/cvlr/cvlr.biotools.json b/data/cvlr/cvlr.biotools.json
new file mode 100644
index 0000000000000..f30848bce7275
--- /dev/null
+++ b/data/cvlr/cvlr.biotools.json
@@ -0,0 +1,84 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-15T16:47:03.485139Z",
+    "biotoolsCURIE": "biotools:cvlr",
+    "biotoolsID": "cvlr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "simon.heath@gmail.com",
+            "name": "Emanuele Raineri",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A tool for finding heterogeneously methylated genomic regions using ONT reads.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Bisulfite mapping",
+                    "uri": "http://edamontology.org/operation_3186"
+                },
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Methylation calling",
+                    "uri": "http://edamontology.org/operation_3919"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/EmanueleRaineri/cvlr",
+    "language": [
+        "C",
+        "Python"
+    ],
+    "lastUpdate": "2023-03-15T16:47:03.489321Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/EmanueleRaineri/cvlr"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/EmanueleRaineri/cvlr.<h4>Contact</h4>simon.heath@cnag.crg.eu"
+        }
+    ],
+    "name": "cvlr",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOADV/VBAC101",
+            "pmcid": "PMC9887406",
+            "pmid": "36726731"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Methylated DNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3674"
+        }
+    ]
+}
diff --git a/data/cyanomapdb/cyanomapdb.biotools.json b/data/cyanomapdb/cyanomapdb.biotools.json
new file mode 100644
index 0000000000000..20a294578b5b9
--- /dev/null
+++ b/data/cyanomapdb/cyanomapdb.biotools.json
@@ -0,0 +1,84 @@
+{
+    "additionDate": "2023-02-19T10:42:19.212847Z",
+    "biotoolsCURIE": "biotools:cyanomapdb",
+    "biotoolsID": "cyanomapdb",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "ch_wan@ccnu.edu.cn",
+            "name": "Cuihong Wan",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xpjiang@ccnu.edu.cn",
+            "name": "Xingpeng Jiang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "CyanoMapDB is a database providing cyanobacterial protein-protein interactions (PPIs) with experimental evidence, consisting of 52,304 PPIs among 6,789 proteins from 23 cyanobacterial species.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network visualisation",
+                    "uri": "http://edamontology.org/operation_3925"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.cyanomapdb.msbio.pro/",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-02-19T10:42:19.215969Z",
+    "license": "Not licensed",
+    "name": "CyanoMapDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/PLPHYS/KIAC594",
+            "pmid": "36548962"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/cycler/cycler.biotools.json b/data/cycler/cycler.biotools.json
new file mode 100644
index 0000000000000..2fc6ce86ed099
--- /dev/null
+++ b/data/cycler/cycler.biotools.json
@@ -0,0 +1,107 @@
+{
+    "additionDate": "2023-02-19T10:47:58.519440Z",
+    "biotoolsCURIE": "biotools:cycler",
+    "biotoolsID": "cycler",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "irmtraud.meyer@cantab.net",
+            "name": "Irmtraud M Meyer",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "CYCLeR is a software package for assembly of circRNA transcripts from\nRNA-seq data. Takes a set of BSJ prediction files and RNA-seq BAM files as an input\nand outputs circRNA trascnripts as FASTA, GTF and flat annotation files. The tools\nalso outputs a padded FASTA to serve as an index for transcript EM abundance estimation.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://raw.githubusercontent.com/stiv1n/CYCLeR/main/CYCLeR.pdf"
+        },
+        {
+            "type": [
+                "Training material"
+            ],
+            "url": "https://raw.githubusercontent.com/stiv1n/CYCLeR/main/CYCLeR_workflow.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Alternative splicing prediction",
+                    "uri": "http://edamontology.org/operation_0264"
+                },
+                {
+                    "term": "RNA-Seq quantification",
+                    "uri": "http://edamontology.org/operation_3800"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/stiv1n/CYCLeR",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-19T10:47:58.522315Z",
+    "license": "GPL-3.0",
+    "name": "CYCLeR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1100",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Splicing is one key mechanism determining the state of any eukaryotic cell. Apart from linear splice variants, circular splice variants (circRNAs) can arise via non-canonical splicing involving a back-splice junction (BSJ). Most existing methods only identify circRNAs via the corresponding BSJ, but do not aim to estimate their full sequence identity or to identify different, alternatively spliced circular isoforms arising from the same BSJ. We here present CYCLeR, the first computational method for identifying the full sequence identity of new and alternatively spliced circRNAs and their abundances while simultaneously co-estimating the abundances of known linear splicing isoforms. We show that CYCLeR significantly outperforms existing methods in terms of F score and quantification of transcripts in simulated data. In a in a comparative study with long-read data, we also show the advantages of CYCLeR compared to existing methods. When analysing Drosophila melanogaster data, CYCLeR uncovers biological patterns of circRNA expression that other methods fail to observe.",
+                "authors": [
+                    {
+                        "name": "Meyer I.M."
+                    },
+                    {
+                        "name": "Stefanov S.R."
+                    }
+                ],
+                "date": "2023-01-25T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "CYCLeR-a novel tool for the full isoform assembly and quantification of circRNAs"
+            },
+            "pmid": "36478276"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/cysmoddb/cysmoddb.biotools.json b/data/cysmoddb/cysmoddb.biotools.json
new file mode 100644
index 0000000000000..d60ee41820047
--- /dev/null
+++ b/data/cysmoddb/cysmoddb.biotools.json
@@ -0,0 +1,147 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-10T00:42:23.608490Z",
+    "biotoolsCURIE": "biotools:cysmoddb",
+    "biotoolsID": "cysmoddb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "lileime@hotmail.com",
+            "name": "Lei Li",
+            "orcidid": "https://orcid.org/0000-0003-0266-8939",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "bio_shangsp@hotmail.com",
+            "name": "Shipeng Shang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Lin Zhang",
+            "orcidid": "https://orcid.org/0000-0003-3902-6083"
+        },
+        {
+            "name": "Yanzheng Meng",
+            "orcidid": "https://orcid.org/0000-0002-1357-9635"
+        }
+    ],
+    "description": "A comprehensive platform with the integration of manually curated resources and analysis tools for cysteine posttranslational modifications.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene name",
+                        "uri": "http://edamontology.org/data_2299"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Protein name",
+                        "uri": "http://edamontology.org/data_1009"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "UniProt ID",
+                        "uri": "http://edamontology.org/data_2291"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Free cysteine detection",
+                    "uri": "http://edamontology.org/operation_1830"
+                },
+                {
+                    "term": "PTM localisation",
+                    "uri": "http://edamontology.org/operation_3755"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cysmoddb.bioinfogo.org/",
+    "lastUpdate": "2023-01-10T00:42:23.610987Z",
+    "name": "CysModDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC460",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.The unique chemical reactivity of cysteine residues results in various posttranslational modifications (PTMs), which are implicated in regulating a range of fundamental biological processes. With the advent of chemical proteomics technology, thousands of cysteine PTM (CysPTM) sites have been identified from multiple species. A few CysPTM-based databases have been developed, but they mainly focus on data collection rather than various annotations and analytical integration. Here, we present a platform-dubbed CysModDB, integrated with the comprehensive CysPTM resources and analysis tools. CysModDB contains five parts: (1) 70 536 experimentally verified CysPTM sites with annotations of sample origin and enrichment techniques, (2) 21 654 modified proteins annotated with functional regions and structure information, (3) cross-references to external databases such as the protein-protein interactions database, (4) online computational tools for predicting CysPTM sites and (5) integrated analysis tools such as gene enrichment and investigation of sequence features. These parts are integrated using a customized graphic browser and a Basket. The browser uses graphs to represent the distribution of modified sites with different CysPTM types on protein sequences and mapping these sites to the protein structures and functional regions, which assists in exploring cross-talks between the modified sites and their potential effect on protein functions. The Basket connects proteins and CysPTM sites to the analysis tools. In summary, CysModDB is an integrated platform to facilitate the CysPTM research, freely accessible via https://cysmoddb.bioinfogo.org/.",
+                "authors": [
+                    {
+                        "name": "Chen Y."
+                    },
+                    {
+                        "name": "Li C."
+                    },
+                    {
+                        "name": "Li L."
+                    },
+                    {
+                        "name": "Meng Y."
+                    },
+                    {
+                        "name": "Shang S."
+                    },
+                    {
+                        "name": "Wang X."
+                    },
+                    {
+                        "name": "Wang Z."
+                    },
+                    {
+                        "name": "Zhang L."
+                    },
+                    {
+                        "name": "Zhang L."
+                    }
+                ],
+                "date": "2022-11-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "CysModDB: a comprehensive platform with the integration of manually curated resources and analysis tools for cysteine posttranslational modifications"
+            },
+            "pmcid": "PMC9677505",
+            "pmid": "36305460"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Protein binding sites",
+            "uri": "http://edamontology.org/topic_3534"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein modifications",
+            "uri": "http://edamontology.org/topic_0601"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
+}
diff --git a/data/d3ai-spike/d3ai-spike.biotools.json b/data/d3ai-spike/d3ai-spike.biotools.json
new file mode 100644
index 0000000000000..938edcd570d41
--- /dev/null
+++ b/data/d3ai-spike/d3ai-spike.biotools.json
@@ -0,0 +1,129 @@
+{
+    "additionDate": "2023-02-11T07:27:51.412209Z",
+    "biotoolsCURIE": "biotools:d3ai-spike",
+    "biotoolsID": "d3ai-spike",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Likun Gong",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Weiliang Zhu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Zhijian Xu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A deep learning platform for predicting binding affinity between SARS-CoV-2 spike receptor binding domain with multiple amino acid mutations and human angiotensin-converting enzyme 2.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                },
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-Spike/index.php",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-11T07:27:51.414801Z",
+    "license": "Other",
+    "name": "D3AI-Spike",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106212",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdThe number of SARS-CoV-2 spike Receptor Binding Domain (RBD) with multiple amino acid mutations is huge due to random mutations and combinatorial explosions, making it almost impossible to experimentally determine their binding affinities to human angiotensin-converting enzyme 2 (hACE2). Although computational prediction is an alternative way, there is still no online platform to predict the mutation effect of RBD on the hACE2 binding affinity until now. In this study, we developed a free online platform based on deep learning models, namely D3AI-Spike, for quickly predicting binding affinity between spike RBD mutants and hACE2. The models based on CNN and CNN-RNN methods have the concordance index of around 0.8. Overall, the test results of the models are in agreement with the experimental data. To further evaluate the prediction power of D3AI-Spike, we predicted and experimentally determined the binding affinity of a VUM (variants under monitoring) variant IHU (B.1.640.2), which has fourteen amino acid substitutions, including N501Y and E484K, and 9 deletions located in the spike protein. The predicted average affinity score for wild-type RBD and IHU to hACE2 are 0.483 and 0.438, while the determined Kaff values are 5.39 ± 0.38 × 107 L/mol and 1.02 ± 0.47 × 107 L/mol, respectively, demonstrating the strong predictive power of D3AI-Spike. We think D3AI-Spike will be helpful to the viral transmission prediction for the new emerging SARS-CoV-2 variants. D3AI-Spike is now available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-Spike/index.php.",
+                "authors": [
+                    {
+                        "name": "Gong L."
+                    },
+                    {
+                        "name": "Han J."
+                    },
+                    {
+                        "name": "Li J."
+                    },
+                    {
+                        "name": "Liu T."
+                    },
+                    {
+                        "name": "Ma M."
+                    },
+                    {
+                        "name": "Shi Y."
+                    },
+                    {
+                        "name": "Xu Z."
+                    },
+                    {
+                        "name": "Yang Y."
+                    },
+                    {
+                        "name": "Zhang X."
+                    },
+                    {
+                        "name": "Zhu W."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "D3AI-Spike: A deep learning platform for predicting binding affinity between SARS-CoV-2 spike receptor binding domain with multiple amino acid mutations and human angiotensin-converting enzyme 2"
+            },
+            "pmcid": "PMC9597563",
+            "pmid": "36327885"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Enzymes",
+            "uri": "http://edamontology.org/topic_0821"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/dadapy/dadapy.biotools.json b/data/dadapy/dadapy.biotools.json
new file mode 100644
index 0000000000000..d049dfa20bcb0
--- /dev/null
+++ b/data/dadapy/dadapy.biotools.json
@@ -0,0 +1,138 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-10T00:35:58.369280Z",
+    "biotoolsCURIE": "biotools:dadapy",
+    "biotoolsID": "dadapy",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "aldo.glielmo@bancaditalia.it",
+            "name": "Aldo Glielmo",
+            "orcidid": "https://orcid.org/0000-0002-4737-2878",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "laio@sissa.it",
+            "name": "Alessandro Laio",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alex Rodriguez"
+        },
+        {
+            "name": "Iuri Macocco"
+        }
+    ],
+    "description": "Distance-based analysis of data-manifolds in Python.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://dadapy.readthedocs.io/en/latest/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Dendrogram visualisation",
+                    "uri": "http://edamontology.org/operation_2938"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Feature selection",
+                    "uri": "http://edamontology.org/operation_3936"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/sissa-data-science/DADApy",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-10T00:36:52.838158Z",
+    "license": "Apache-2.0",
+    "name": "DADApy",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.PATTER.2022.100589",
+            "metadata": {
+                "abstract": "© 2022 The Author(s)DADApy is a Python software package for analyzing and characterizing high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering, and for comparing different distance metrics. We review the main functionalities of the package and exemplify its usage in a synthetic dataset and in a real-world application. DADApy is freely available under the open-source Apache 2.0 license.",
+                "authors": [
+                    {
+                        "name": "Carli M."
+                    },
+                    {
+                        "name": "Doimo D."
+                    },
+                    {
+                        "name": "Glielmo A."
+                    },
+                    {
+                        "name": "Laio A."
+                    },
+                    {
+                        "name": "Macocco I."
+                    },
+                    {
+                        "name": "Rodriguez A."
+                    },
+                    {
+                        "name": "Wild R."
+                    },
+                    {
+                        "name": "Zeni C."
+                    },
+                    {
+                        "name": "d'Errico M."
+                    }
+                ],
+                "date": "2022-10-14T00:00:00Z",
+                "journal": "Patterns",
+                "title": "DADApy: Distance-based analysis of data-manifolds in Python"
+            },
+            "pmcid": "PMC9583186",
+            "pmid": "36277821"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Physics",
+            "uri": "http://edamontology.org/topic_3318"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/dapnet_hla/dapnet_hla.biotools.json b/data/dapnet_hla/dapnet_hla.biotools.json
new file mode 100644
index 0000000000000..8ab1060224e59
--- /dev/null
+++ b/data/dapnet_hla/dapnet_hla.biotools.json
@@ -0,0 +1,77 @@
+{
+    "additionDate": "2023-03-15T16:52:42.508299Z",
+    "biotoolsCURIE": "biotools:dapnet_hla",
+    "biotoolsID": "dapnet_hla",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Houqiang Wang"
+        }
+    ],
+    "description": "Adaptive dual-attention mechanism network based on deep learning to predict non-classical HLA binding sites.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Binding site prediction",
+                    "uri": "http://edamontology.org/operation_2575"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/JYY625/DapNet-HLA",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-15T16:52:42.512477Z",
+    "license": "Not licensed",
+    "name": "DapNet-HLA",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.AB.2023.115075",
+            "metadata": {
+                "abstract": "Human leukocyte antigen (HLA) plays a vital role in immunomodulatory function. Studies have shown that immunotherapy based on non-classical HLA has essential applications in cancer, COVID-19, and allergic diseases. However, there are few deep learning methods to predict non-classical HLA alleles. In this work, an adaptive dual-attention network named DapNet-HLA is established based on existing datasets. Firstly, amino acid sequences are transformed into digital vectors by looking up the table. To overcome the feature sparsity problem caused by unique one-hot encoding, the fused word embedding method is used to map each amino acid to a low-dimensional word vector optimized with the training of the classifier. Then, we use the GCB (group convolution block), SENet attention (squeeze-and-excitation networks), BiLSTM (bidirectional long short-term memory network), and Bahdanau attention mechanism to construct the classifier. The use of SENet can make the weight of the effective feature map high, so that the model can be trained to achieve better results. Attention mechanism is an Encoder-Decoder model used to improve the effectiveness of RNN, LSTM or GRU (gated recurrent neural network). The ablation experiment shows that DapNet-HLA has the best adaptability for five datasets. On the five test datasets, the ACC index and MCC index of DapNet-HLA are 4.89% and 0.0933 higher than the comparison method, respectively. According to the ROC curve and PR curve verified by the 5-fold cross-validation, the AUC value of each fold has a slight fluctuation, which proves the robustness of the DapNet-HLA. The codes and datasets are accessible at https://github.com/JYY625/DapNet-HLA.",
+                "authors": [
+                    {
+                        "name": "Jing Y."
+                    },
+                    {
+                        "name": "Wang H."
+                    },
+                    {
+                        "name": "Zhang S."
+                    }
+                ],
+                "date": "2023-04-01T00:00:00Z",
+                "journal": "Analytical Biochemistry",
+                "title": "DapNet-HLA: Adaptive dual-attention mechanism network based on deep learning to predict non-classical HLA binding sites"
+            },
+            "pmid": "36740003"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/dartpaths/dartpaths.biotools.json b/data/dartpaths/dartpaths.biotools.json
new file mode 100644
index 0000000000000..94dfa7ebdae00
--- /dev/null
+++ b/data/dartpaths/dartpaths.biotools.json
@@ -0,0 +1,140 @@
+{
+    "additionDate": "2023-02-19T10:54:30.384699Z",
+    "biotoolsCURIE": "biotools:dartpaths",
+    "biotoolsID": "dartpaths",
+    "confidence_flag": "tool",
+    "cost": "Free of charge (with restrictions)",
+    "credit": [
+        {
+            "email": "vera.vannoort@kuleuven.be",
+            "name": "Vera van Noort",
+            "orcidid": "https://orcid.org/0000-0002-8436-6602",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "m.wildwater@vivaltes.com",
+            "name": "Marjolein Wildwater",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "The Xpaths algorithms are applied to predict developmental and reproductive toxicity (DART) and implemented into an in silico platform, called DARTpaths.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Pathway analysis",
+                    "uri": "http://edamontology.org/operation_3928"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Text mining",
+                    "uri": "http://edamontology.org/operation_0306"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.vivaltes.com/dartpaths/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-19T10:54:30.387341Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/Xpaths/dartpaths-app"
+        }
+    ],
+    "name": "DARTpaths",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC767",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: Xpaths is a collection of algorithms that allow for the prediction of compound-induced molecular mechanisms of action by integrating phenotypic endpoints of different species; and proposes follow-up tests for model organisms to validate these pathway predictions. The Xpaths algorithms are applied to predict developmental and reproductive toxicity (DART) and implemented into an in silico platform, called DARTpaths. AVAILABILITY AND IMPLEMENTATION: All code is available on GitHub https://github.com/Xpaths/dartpaths-app under Apache license 2.0, detailed overview with demo is available at https://www.vivaltes.com/dartpaths/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Bhalla D."
+                    },
+                    {
+                        "name": "Corradi M."
+                    },
+                    {
+                        "name": "Currie R.A."
+                    },
+                    {
+                        "name": "Deviere E."
+                    },
+                    {
+                        "name": "Krul C."
+                    },
+                    {
+                        "name": "Noothout L."
+                    },
+                    {
+                        "name": "Pieters R."
+                    },
+                    {
+                        "name": "Poppelaars E.S."
+                    },
+                    {
+                        "name": "Rooseboom M."
+                    },
+                    {
+                        "name": "Steijaert M.N."
+                    },
+                    {
+                        "name": "Teunis M."
+                    },
+                    {
+                        "name": "Tomassen W."
+                    },
+                    {
+                        "name": "Wildwater M."
+                    },
+                    {
+                        "name": "van Noort V."
+                    },
+                    {
+                        "name": "van der Voet M."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "DARTpaths, an in silico platform to investigate molecular mechanisms of compounds"
+            },
+            "pmcid": "PMC9825785",
+            "pmid": "36477801"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        }
+    ]
+}
diff --git a/data/dartr/dartr.biotools.json b/data/dartr/dartr.biotools.json
new file mode 100644
index 0000000000000..fcfc1c43c93ef
--- /dev/null
+++ b/data/dartr/dartr.biotools.json
@@ -0,0 +1,153 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-18T22:42:25.401698Z",
+    "biotoolsCURIE": "biotools:dartr",
+    "biotoolsID": "dartr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "luis.mijangos@gmail.com",
+            "name": "Jose Luis Mijangos",
+            "orcidid": "http://orcid.org/0000-0001-6121-4860",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Arthur Georges",
+            "orcidid": "http://orcid.org/0000-0003-2428-0361"
+        },
+        {
+            "name": "Bernd Gruber",
+            "orcidid": "http://orcid.org/0000-0003-0078-8179"
+        },
+        {
+            "name": "Carlo Pacioni",
+            "orcidid": "http://orcid.org/0000-0001-5115-4120"
+        },
+        {
+            "name": "Oliver Berry",
+            "orcidid": "http://orcid.org/0000-0001-7545-5083"
+        }
+    ],
+    "description": "An accessible genetic analysis platform for conservation, ecology, and agriculture.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://cran.r-project.org/web/packages/dartR/dartR.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://georges.biomatix.org/dartR",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-18T22:42:25.404988Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Discussion forum"
+            ],
+            "url": "https://groups.google.com/g/dartr"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://cran.r-project.org/web/packages/dartR/index.html"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/green-striped-gecko/dartR"
+        }
+    ],
+    "name": "dartR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1111/2041-210X.13918",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.Innumerable approaches to analyse genetic data are now available to guide conservation, ecological and agricultural projects. However, streamlined and accessible tools are needed to bring these approaches within the reach of a broader user base. dartR was released in 2018 to lessen the intrinsic complexity of analysing single nucleotide polymorphisms (SNPs) and dominant markers (presence/absence of amplified sequence tags) by providing user-friendly data quality control and marker selection functions. dartR users have grown steadily since its release and provided valuable feedback on their interaction with the package allowing us to enhance dartR capabilities. Here, we present Version 2 of dartR. In this version, we substantially increased the number of available functions from 45 to 144. In addition to improved functionality, we focused on enhancing the user experience by extending plot customisation, function standardisation, increasing user support and function speed. dartR provides functions for various stages in analysing genetic data, from data manipulation to reporting. dartR provides many functions for importing, exporting and linking to other packages, to provide an easy-to-navigate conduit between data generation and analysis options already available via other packages. We also implemented simulation functions whose results can be analysed seamlessly with several other dartR functions. As more methods and approaches mature to inform conservation, we envision that accessible platforms to analyse genetic data will play a crucial role in translating science into practice.",
+                "authors": [
+                    {
+                        "name": "Berry O."
+                    },
+                    {
+                        "name": "Georges A."
+                    },
+                    {
+                        "name": "Gruber B."
+                    },
+                    {
+                        "name": "Mijangos J.L."
+                    },
+                    {
+                        "name": "Pacioni C."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Methods in Ecology and Evolution",
+                "title": "dartR v2: An accessible genetic analysis platform for conservation, ecology and agriculture"
+            }
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Ecology",
+            "uri": "http://edamontology.org/topic_0610"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        }
+    ]
+}
diff --git a/data/data_virtuality/data_virtuality.biotools.json b/data/data_virtuality/data_virtuality.biotools.json
new file mode 100644
index 0000000000000..a016e6b756b32
--- /dev/null
+++ b/data/data_virtuality/data_virtuality.biotools.json
@@ -0,0 +1,56 @@
+{
+    "additionDate": "2023-01-26T12:20:30.937327Z",
+    "biotoolsCURIE": "biotools:data_virtuality",
+    "biotoolsID": "data_virtuality",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "url": "https://support.datavirtuality.com/hc/en-us"
+        }
+    ],
+    "description": "Data Virtualization for Flexible Data Architectures\n\nBuilt by data integration professionals for data integration professionals allowing you to meet ever-changing business needs\n\nPipes automatically gets data from 200+ available sources in your data warehouse. With just a few clicks and without any coding.\n\nPipes Professional enables you to build custom data pipelines with the best-in-class SQL editor",
+    "documentation": [
+        {
+            "note": "In our documentation you find all things Data Virtuality Server, from general information on how it works to step-by-step guides for specific operations. The documentation consists of three parts:\n\n    Administration Guide – which is mostly aimed at administrators\n    User Guide – which includes mostly theoretical information on how the Data Virtuality Server works\n    Reference Guide – which contains technical information on the SQL dialect, procedures, commands, schemas, and other elements used in the Data Virtuality Serve",
+            "type": [
+                "User manual"
+            ],
+            "url": "https://datavirtuality.com/en/docs-and-support/"
+        }
+    ],
+    "download": [
+        {
+            "note": "Acces to free trial",
+            "type": "Other",
+            "url": "https://eu.pipes.datavirtuality.com/#/start-trial"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://datavirtuality.com/en/",
+    "lastUpdate": "2023-02-01T13:15:07.968068Z",
+    "license": "Proprietary",
+    "link": [
+        {
+            "type": [
+                "Service"
+            ],
+            "url": "https://datavirtuality.com/en/"
+        }
+    ],
+    "name": "Data Virtuality",
+    "owner": "iacs-biocomputacion"
+}
diff --git a/data/dater/dater.biotools.json b/data/dater/dater.biotools.json
new file mode 100644
index 0000000000000..34ee6cfd1db8c
--- /dev/null
+++ b/data/dater/dater.biotools.json
@@ -0,0 +1,85 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T17:08:52.298176Z",
+    "biotoolsCURIE": "biotools:dater",
+    "biotoolsID": "dater",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Mukul S Bansal"
+        }
+    ],
+    "description": "DaTeR (short for \"Dating Trees using Relative constraints\") is a program for improved dating of microbial species phylogenies using relative time constraints (e.g., obtained from high-confidence horizontal gene transfer events).",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phylogenetic inference",
+                    "uri": "http://edamontology.org/operation_0323"
+                },
+                {
+                    "term": "Phylogenetic tree editing",
+                    "uri": "http://edamontology.org/operation_0326"
+                },
+                {
+                    "term": "Tree dating",
+                    "uri": "http://edamontology.org/operation_3942"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://compbio.engr.uconn.edu/software/dater/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T17:08:52.305146Z",
+    "license": "GPL-3.0",
+    "name": "DaTeR",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD084",
+            "metadata": {
+                "abstract": "MOTIVATION: A chronogram is a dated phylogenetic tree whose branch lengths have been scaled to represent time. Such chronograms are computed based on available date estimates (e.g. from dated fossils), which provide absolute time constraints for one or more nodes of an input undated phylogeny, coupled with an appropriate underlying model for evolutionary rates variation along the branches of the phylogeny. However, traditional methods for phylogenetic dating cannot take into account relative time constraints, such as those provided by inferred horizontal transfer events. In many cases, chronograms computed using only absolute time constraints are inconsistent with known relative time constraints. RESULTS: In this work, we introduce a new approach, Dating Trees using Relative constraints (DaTeR), for phylogenetic dating that can take into account both absolute and relative time constraints. The key idea is to use existing Bayesian approaches for phylogenetic dating to sample posterior chronograms satisfying desired absolute time constraints, minimally adjust or 'error-correct' these sampled chronograms to satisfy all given relative time constraints, and aggregate across all error-corrected chronograms. DaTeR uses a constrained optimization framework for the error-correction step, finding minimal deviations from previously assigned dates or branch lengths. We applied DaTeR to a biological dataset of 170 Cyanobacterial taxa and a reliable set of 24 transfer-based relative constraints, under six different molecular dating models. Our extensive analysis of this dataset demonstrates that DaTeR is both highly effective and scalable and that its application can significantly improve estimated chronograms. AVAILABILITY AND IMPLEMENTATION: Freely available from https://compbio.engr.uconn.edu/software/dater/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Bansal M.S."
+                    },
+                    {
+                        "name": "Fournier G.P."
+                    },
+                    {
+                        "name": "Mondal A."
+                    },
+                    {
+                        "name": "Payette J.G."
+                    },
+                    {
+                        "name": "Rangel L.T."
+                    }
+                ],
+                "date": "2023-02-03T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "DaTeR: error-correcting phylogenetic chronograms using relative time constraints"
+            },
+            "pmid": "36752504"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Molecular evolution",
+            "uri": "http://edamontology.org/topic_3945"
+        },
+        {
+            "term": "Phylogeny",
+            "uri": "http://edamontology.org/topic_0084"
+        }
+    ]
+}
diff --git a/data/dbcan-seq/dbcan-seq.biotools.json b/data/dbcan-seq/dbcan-seq.biotools.json
new file mode 100644
index 0000000000000..8ee4fe6662f4d
--- /dev/null
+++ b/data/dbcan-seq/dbcan-seq.biotools.json
@@ -0,0 +1,121 @@
+{
+    "additionDate": "2023-01-27T17:17:43.449911Z",
+    "biotoolsCURIE": "biotools:dbcan-seq",
+    "biotoolsID": "dbcan-seq",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yyin@unl.edu",
+            "name": "Yanbin Yin",
+            "orcidid": "https://orcid.org/0000-0001-7667-881X",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A online database dbCAN-seq to provide pre-computed carbohydrate-active enzyme (CAZymes) sequence and annotation data for 5,349 bacterial genomes.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://bcb.unl.edu/dbCAN_seq",
+    "lastUpdate": "2023-01-27T17:17:43.452400Z",
+    "license": "Other",
+    "name": "dbCAN-seq",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/nar/gkx894",
+            "metadata": {
+                "abstract": "© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.Carbohydrate-active enzyme (CAZymes) are not only the most important enzymes for bioenergy and agricultural industries, but also very important for human health, in that human gut microbiota encode hundreds of CAZyme genes in their genomes for degrading various dietary and host carbohydrates. We have built an online database dbCAN-seq (http://cys.bios.niu.edu/dbCAN-seq) to provide pre-computed CAZyme sequence and annotation data for 5,349 bacterial genomes. Compared to the other CAZyme resources, dbCAN-seq has the following new features: (i) a convenient download page to allow batch download of all the sequence and annotation data; (ii) an annotation page for every CAZyme to provide the most comprehensive annotation data; (iii) a metadata page to organize the bacterial genomes according to species metadata such as disease, habitat, oxygen requirement, temperature, metabolism; (iv) a very fast tool to identify physically linked CAZyme gene clusters (CGCs) and (v) a powerful search function to allow fast and efficient data query. With these unique utilities, dbCAN-seq will become a valuable web resource for CAZyme research, with a focus complementary to dbCAN (automated CAZyme annotation server) and CAZy (CAZyme family classification and reference database).",
+                "authors": [
+                    {
+                        "name": "Entwistle S."
+                    },
+                    {
+                        "name": "Huang L."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Wu P."
+                    },
+                    {
+                        "name": "Yang Z."
+                    },
+                    {
+                        "name": "Yi H."
+                    },
+                    {
+                        "name": "Yin Y."
+                    },
+                    {
+                        "name": "Yohe T."
+                    },
+                    {
+                        "name": "Zhang H."
+                    }
+                ],
+                "citationCount": 133,
+                "date": "2018-01-01T00:00:00Z",
+                "journal": "Nucleic Acids Research",
+                "title": "DbCAN-seq: A database of carbohydrate-active enzyme (CAZyme) sequence and annotation"
+            },
+            "pmcid": "PMC5753378",
+            "pmid": "30053267"
+        },
+        {
+            "doi": "10.1093/NAR/GKAC1068",
+            "pmcid": "PMC9825555",
+            "pmid": "36399503"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Carbohydrates",
+            "uri": "http://edamontology.org/topic_0152"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/dbdipy/dbdipy.biotools.json b/data/dbdipy/dbdipy.biotools.json
new file mode 100644
index 0000000000000..28e3a30d443b1
--- /dev/null
+++ b/data/dbdipy/dbdipy.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T17:11:33.713068Z",
+    "biotoolsCURIE": "biotools:dbdipy",
+    "biotoolsID": "dbdipy",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "leopold.weidner@tum.de",
+            "name": "Leopold Weidner",
+            "orcidid": "https://orcid.org/0000-0002-6801-3647",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "schmitt-kopplin@tum.de",
+            "name": "Philippe Schmitt-Kopplin",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "a Python library for processing of untargeted datasets from real-time plasma ionization mass spectrometry.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pypi.org/project/DBDIpy",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T17:11:33.719550Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/leopold-weidner/DBDIpy"
+        }
+    ],
+    "name": "DBDIpy",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD088",
+            "metadata": {
+                "abstract": "MOTIVATION: Plasma ionization is rapidly gaining popularity for mass spectrometry (MS)-based studies of volatiles and aerosols. However, data from plasma ionization are delicate to interpret as competing ionization pathways in the plasma create numerous ion species. There is no tool for detection of adducts and in-source fragments from plasma ionization data yet, which makes data evaluation ambiguous. SUMMARY: We developed DBDIpy, a Python library for processing and formal analysis of untargeted, time-sensitive plasma ionization MS datasets. Its core functionality lies in the identification of in-source fragments and identification of rivaling ionization pathways of the same analytes in time-sensitive datasets. It further contains elementary functions for processing of untargeted metabolomics data and interfaces to an established ecosystem for analysis of MS data in Python. AVAILABILITY AND IMPLEMENTATION: DBDIpy is implemented in Python (Version ≥ 3.7) and can be downloaded from PyPI the Python package repository (https://pypi.org/project/DBDIpy) or from GitHub (https://github.com/leopold-weidner/DBDIpy). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Hemmler D."
+                    },
+                    {
+                        "name": "Rychlik M."
+                    },
+                    {
+                        "name": "Schmitt-Kopplin P."
+                    },
+                    {
+                        "name": "Weidner L."
+                    }
+                ],
+                "date": "2023-02-03T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "DBDIpy: a Python library for processing of untargeted datasets from real-time plasma ionization mass spectrometry"
+            },
+            "pmcid": "PMC9942549",
+            "pmid": "36786403"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        }
+    ]
+}
diff --git a/data/dcgn/dcgn.biotools.json b/data/dcgn/dcgn.biotools.json
new file mode 100644
index 0000000000000..eba8846b8bd41
--- /dev/null
+++ b/data/dcgn/dcgn.biotools.json
@@ -0,0 +1,123 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-10T00:27:30.089586Z",
+    "biotoolsCURIE": "biotools:dcgn",
+    "biotoolsID": "dcgn",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "luojunwei@hpu.edu.cn",
+            "name": "Junwei Luo",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Huimin Luo"
+        },
+        {
+            "name": "Jiawei Shi"
+        },
+        {
+            "name": "Jiquan Shen"
+        }
+    ],
+    "description": "Deep learning approach for cancer subtype classification using high-dimensional gene expression data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/shijwe/DCGN",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-10T00:27:30.092432Z",
+    "license": "Not licensed",
+    "name": "DCGN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-04980-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Motivation: Studies have shown that classifying cancer subtypes can provide valuable information for a range of cancer research, from aetiology and tumour biology to prognosis and personalized treatment. Current methods usually adopt gene expression data to perform cancer subtype classification. However, cancer samples are scarce, and the high-dimensional features of their gene expression data are too sparse to allow most methods to achieve desirable classification results. Results: In this paper, we propose a deep learning approach by combining a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU): our approach, DCGN, aims to achieve nonlinear dimensionality reduction and learn features to eliminate irrelevant factors in gene expression data. Specifically, DCGN first uses the synthetic minority oversampling technique algorithm to equalize data. The CNN can handle high-dimensional data without stress and extract important local features, and the BiGRU can analyse deep features and retain their important information; the DCGN captures key features by combining both neural networks to overcome the challenges of small sample sizes and sparse, high-dimensional features. In the experiments, we compared the DCGN to seven other cancer subtype classification methods using breast and bladder cancer gene expression datasets. The experimental results show that the DCGN performs better than the other seven methods and can provide more satisfactory classification results.",
+                "authors": [
+                    {
+                        "name": "Liu X."
+                    },
+                    {
+                        "name": "Luo H."
+                    },
+                    {
+                        "name": "Luo J."
+                    },
+                    {
+                        "name": "Shen J."
+                    },
+                    {
+                        "name": "Shi J."
+                    },
+                    {
+                        "name": "Wu Z."
+                    },
+                    {
+                        "name": "Yan C."
+                    },
+                    {
+                        "name": "Zhai H."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "Deep learning approach for cancer subtype classification using high-dimensional gene expression data"
+            },
+            "pmcid": "PMC9575247",
+            "pmid": "36253710"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Taxonomy",
+            "uri": "http://edamontology.org/topic_0637"
+        }
+    ]
+}
diff --git a/data/dchic/dchic.biotools.json b/data/dchic/dchic.biotools.json
new file mode 100644
index 0000000000000..5bfaf508e604a
--- /dev/null
+++ b/data/dchic/dchic.biotools.json
@@ -0,0 +1,102 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T17:22:19.132216Z",
+    "biotoolsCURIE": "biotools:dchic",
+    "biotoolsID": "dchic",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "abhijit@lji.org",
+            "name": "Abhijit Chakraborty",
+            "orcidid": "https://orcid.org/0000-0002-1500-3699",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "ferhatay@lji.org",
+            "name": "Ferhat Ay",
+            "orcidid": "https://orcid.org/0000-0002-0708-6914",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A tool for differential compartment analysis of Hi-C datasets",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ay-lab/dcHiC",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-27T17:22:19.134688Z",
+    "license": "MIT",
+    "name": "dcHiC",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41467-022-34626-6",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).The compartmental organization of mammalian genomes and its changes play important roles in distinct biological processes. Here, we introduce dcHiC, which utilizes a multivariate distance measure to identify significant changes in compartmentalization among multiple contact maps. Evaluating dcHiC on four collections of bulk and single-cell contact maps from in vitro mouse neural differentiation (n = 3), mouse hematopoiesis (n = 10), human LCLs (n = 20) and post-natal mouse brain development (n = 3 stages), we show its effectiveness and sensitivity in detecting biologically relevant changes, including those orthogonally validated. dcHiC reported regions with dynamically regulated genes associated with cell identity, along with correlated changes in chromatin states, subcompartments, replication timing and lamin association. With its efficient implementation, dcHiC enables high-resolution compartment analysis as well as standalone browser visualization, differential interaction identification and time-series clustering. dcHiC is an essential addition to the Hi-C analysis toolbox for the ever-growing number of bulk and single-cell contact maps. Available at: https://github.com/ay-lab/dcHiC.",
+                "authors": [
+                    {
+                        "name": "Ay F."
+                    },
+                    {
+                        "name": "Chakraborty A."
+                    },
+                    {
+                        "name": "Wang J.G."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "dcHiC detects differential compartments across multiple Hi-C datasets"
+            },
+            "pmcid": "PMC9652325",
+            "pmid": "36369226"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/dcifer/dcifer.biotools.json b/data/dcifer/dcifer.biotools.json
new file mode 100644
index 0000000000000..2868763a7fc5c
--- /dev/null
+++ b/data/dcifer/dcifer.biotools.json
@@ -0,0 +1,102 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-18T22:59:27.936164Z",
+    "biotoolsCURIE": "biotools:dcifer",
+    "biotoolsID": "dcifer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Boris Gerlovin"
+        },
+        {
+            "name": "Bryan Greenhouse"
+        },
+        {
+            "name": "Isabel Rodríguez-Barraquer"
+        },
+        {
+            "name": "Inna Gerlovina",
+            "orcidid": "http://orcid.org/0000-0002-7772-7473"
+        }
+    ],
+    "description": "an IBD-based method to calculate genetic distance between polyclonal infections.",
+    "documentation": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://cran.r-project.org/web/packages/dcifer/vignettes/vignetteDcifer.pdf"
+        },
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://cran.r-project.org/web/packages/dcifer/dcifer.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://eppicenter.github.io/dcifer/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-18T22:59:27.938753Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://cran.r-project.org/web/packages/dcifer/index.html"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/EPPIcenter/dcifer"
+        }
+    ],
+    "name": "Dcifer",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/genetics/iyac126",
+            "pmcid": "PMC9526043",
+            "pmid": "36000888"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        },
+        {
+            "term": "Structure analysis",
+            "uri": "http://edamontology.org/topic_0081"
+        }
+    ]
+}
diff --git a/data/dcpha/dcpha.biotools.json b/data/dcpha/dcpha.biotools.json
new file mode 100644
index 0000000000000..e34fd72933dd7
--- /dev/null
+++ b/data/dcpha/dcpha.biotools.json
@@ -0,0 +1,91 @@
+{
+    "additionDate": "2023-03-17T17:15:59.824126Z",
+    "biotoolsCURIE": "biotools:dcpha",
+    "biotoolsID": "dcpha",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zengxh@cqupt.edu.cn",
+            "name": "Xianhua Zeng",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Scripts for deep consistency-preserving hash auto-encoders for neuroimage cross-modal retrieval.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Socrates023/DCPHA",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T17:15:59.829701Z",
+    "license": "Not licensed",
+    "name": "DCPHA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41598-023-29320-6",
+            "metadata": {
+                "abstract": "Cross-modal hashing is an efficient method to embed high-dimensional heterogeneous modal feature descriptors into a consistency-preserving Hamming space with low-dimensional. Most existing cross-modal hashing methods have been able to bridge the heterogeneous modality gap, but there are still two challenges resulting in limited retrieval accuracy: (1) ignoring the continuous similarity of samples on manifold; (2) lack of discriminability of hash codes with the same semantics. To cope with these problems, we propose a Deep Consistency-Preserving Hash Auto-encoders model, called DCPHA, based on the multi-manifold property of the feature distribution. Specifically, DCPHA consists of a pair of asymmetric auto-encoders and two semantics-preserving attention branches working in the encoding and decoding stages, respectively. When the number of input medical image modalities is greater than 2, the encoder is a multiple pseudo-Siamese network designed to extract specific modality features of different medical image modalities. In addition, we define the continuous similarity of heterogeneous and homogeneous samples on Riemann manifold from the perspective of multiple sub-manifolds, respectively, and the two constraints, i.e., multi-semantic consistency and multi-manifold similarity-preserving, are embedded in the learning of hash codes to obtain high-quality hash codes with consistency-preserving. The extensive experiments show that the proposed DCPHA has the most stable and state-of-the-art performance. We make code and models publicly available: https://github.com/Socrates023/DCPHA.",
+                "authors": [
+                    {
+                        "name": "Wang X."
+                    },
+                    {
+                        "name": "Zeng X."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "Deep consistency-preserving hash auto-encoders for neuroimage cross-modal retrieval"
+            },
+            "pmcid": "PMC9911775",
+            "pmid": "36759692"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Tomography",
+            "uri": "http://edamontology.org/topic_3452"
+        }
+    ]
+}
diff --git a/data/dcsau_net/dcsau_net.biotools.json b/data/dcsau_net/dcsau_net.biotools.json
new file mode 100644
index 0000000000000..f947087af2e89
--- /dev/null
+++ b/data/dcsau_net/dcsau_net.biotools.json
@@ -0,0 +1,76 @@
+{
+    "additionDate": "2023-03-17T17:19:02.291920Z",
+    "biotoolsCURIE": "biotools:dcsau_net",
+    "biotoolsID": "dcsau_net",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Wenting Duan"
+        }
+    ],
+    "description": "A deeper and more compact split-attention U-Net for medical image segmentation.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/xq141839/DCSAU-Net",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T17:19:02.297788Z",
+    "license": "Apache-2.0",
+    "name": "DCSAU-Net",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2023.106626",
+            "metadata": {
+                "abstract": "Deep learning architecture with convolutional neural network achieves outstanding success in the field of computer vision. Where U-Net has made a great breakthrough in biomedical image segmentation and has been widely applied in a wide range of practical scenarios. However, the equal design of every downsampling layer in the encoder part and simply stacked convolutions do not allow U-Net to extract sufficient information of features from different depths. The increasing complexity of medical images brings new challenges to the existing methods. In this paper, we propose a deeper and more compact split-attention u-shape network, which efficiently utilises low-level and high-level semantic information based on two frameworks: primary feature conservation and compact split-attention block. We evaluate the proposed model on CVC-ClinicDB, 2018 Data Science Bowl, ISIC-2018, SegPC-2021 and BraTS-2021 datasets. As a result, our proposed model displays better performance than other state-of-the-art methods in terms of the mean intersection over union and dice coefficient. More significantly, the proposed model demonstrates excellent segmentation performance on challenging images. The code for our work and more technical details can be found at https://github.com/xq141839/DCSAU-Net.",
+                "authors": [
+                    {
+                        "name": "Duan W."
+                    },
+                    {
+                        "name": "HE N."
+                    },
+                    {
+                        "name": "Ma Z."
+                    },
+                    {
+                        "name": "Xu Q."
+                    }
+                ],
+                "date": "2023-03-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation"
+            },
+            "pmid": "36736096"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        }
+    ]
+}
diff --git a/data/deep_ksuccsite/deep_ksuccsite.biotools.json b/data/deep_ksuccsite/deep_ksuccsite.biotools.json
new file mode 100644
index 0000000000000..92b80073e6347
--- /dev/null
+++ b/data/deep_ksuccsite/deep_ksuccsite.biotools.json
@@ -0,0 +1,115 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-10T00:20:40.161230Z",
+    "biotoolsCURIE": "biotools:deep_ksuccsite",
+    "biotoolsID": "deep_ksuccsite",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "liuxin@xzhmu.edu.cn",
+            "name": "Xin Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "liuymito@xzhmu.edu.cn",
+            "name": "Yong Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Lin-Lin Xu"
+        },
+        {
+            "name": "Liang Wang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A novel deep learning method for the identification of lysine succinylation sites.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "PTM localisation",
+                    "uri": "http://edamontology.org/operation_3755"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/flyinsky6/Deep_KsuccSite",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-10T00:20:40.163712Z",
+    "license": "Not licensed",
+    "name": "Deep_KsuccSite",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FGENE.2022.1007618",
+            "metadata": {
+                "abstract": "Copyright © 2022 Liu, Xu, Lu, Yang, Gu, Wang and Liu.Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. Therefore, it is necessary to construct an efficient computational method to prediction the presence of Ksucc sites in protein sequences. In this study, we proposed a novel and effective predictor for the identification of Ksucc sites based on deep learning algorithms that was termed as Deep_KsuccSite. The predictor adopted Composition, Transition, and Distribution (CTD) Composition (CTDC), Enhanced Grouped Amino Acid Composition (EGAAC), Amphiphilic Pseudo-Amino Acid Composition (APAAC), and Embedding Encoding methods to encode peptides, then constructed three base classifiers using one-dimensional (1D) convolutional neural network (CNN) and 2D-CNN, and finally utilized voting method to get the final results. K-fold cross-validation and independent testing showed that Deep_KsuccSite could serve as an effective tool to identify Ksucc sites in protein sequences. In addition, the ablation experiment results based on voting, feature combination, and model architecture showed that Deep_KsuccSite could make full use of the information of different features to construct an effective classifier. Taken together, we developed Deep_KsuccSite in this study, which was based on deep learning algorithm and could achieved better prediction accuracy than current methods for lysine succinylation sites. The code and dataset involved in this methodological study are permanently available at the URL https://github.com/flyinsky6/Deep_KsuccSite.",
+                "authors": [
+                    {
+                        "name": "Gu X.-Y."
+                    },
+                    {
+                        "name": "Liu X."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Lu Y.-P."
+                    },
+                    {
+                        "name": "Wang L."
+                    },
+                    {
+                        "name": "Xu L.-L."
+                    },
+                    {
+                        "name": "Yang T."
+                    }
+                ],
+                "date": "2022-09-29T00:00:00Z",
+                "journal": "Frontiers in Genetics",
+                "title": "Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites"
+            },
+            "pmcid": "PMC9557156",
+            "pmid": "36246655"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein modifications",
+            "uri": "http://edamontology.org/topic_0601"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/deepad/deepad.biotools.json b/data/deepad/deepad.biotools.json
new file mode 100644
index 0000000000000..ed651072aee8a
--- /dev/null
+++ b/data/deepad/deepad.biotools.json
@@ -0,0 +1,107 @@
+{
+    "additionDate": "2023-03-17T17:24:20.701026Z",
+    "biotoolsCURIE": "biotools:deepad",
+    "biotoolsID": "deepad",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "krishdesaiedu@gmail.com",
+            "name": "Krish Desai",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "sumaddurycollege2024@gmail.com",
+            "name": "Sucheer Maddury",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A deep learning application for predicting amyloid standardized uptake value ratio through PET for Alzheimer's prognosis.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://deepad.herokuapp.com/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T17:24:20.705901Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/sumaddury/Amyloid-PET-SUVR-Quantification"
+        }
+    ],
+    "name": "DeepAD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FRAI.2023.1091506",
+            "metadata": {
+                "abstract": "Introduction: Amyloid deposition is a vital biomarker in the process of Alzheimer's diagnosis. 18F-florbetapir PET scans can provide valuable imaging data to determine cortical amyloid quantities. However, the process is labor and doctor intensive, requiring extremely specialized education and resources that may not be accessible to everyone, making the amyloid calculation process inefficient. Deep learning is a rising tool in Alzheimer's research which could be used to determine amyloid deposition. Materials and methods: Using data from the Alzheimer's Disease Neuroimaging Initiative, we identified 2,980 patients with PET imaging, clinical, and genetic data. We tested various ResNet, EfficientNet, and RegNet convolutional neural networks and later combined the best performing model with Gradient Boosting Decision Tree algorithms to predict standardized uptake value ratio (SUVR) of amyloid in each patient session. We tried several configurations to find the best model tuning for regression-to-SUVR. Results: We found that the RegNet X064 architecture combined with a grid search-tuned Gradient Boosting Decision Tree with 3 axial input slices and clinical and genetic data achieved the lowest loss. Using the mean-absolute-error metric, the loss converged to an MAE of 0.0441, equating to 96.4% accuracy across the 596-patient test set. Discussion: We showed that this method is more consistent and accessible in comparison to human readers from previous studies, with lower margins of error and substantially faster calculation times. We implemented our deep learning model on to a web application named DeepAD which allows our diagnostic tool to be accessible. DeepAD could be used in hospitals and clinics with resource limitations for amyloid deposition and shows promise for more imaging tasks as well.",
+                "authors": [
+                    {
+                        "name": "Desai K."
+                    },
+                    {
+                        "name": "Maddury S."
+                    }
+                ],
+                "date": "2023-02-06T00:00:00Z",
+                "journal": "Frontiers in Artificial Intelligence",
+                "title": "DeepAD: A deep learning application for predicting amyloid standardized uptake value ratio through PET for Alzheimer's prognosis"
+            },
+            "pmcid": "PMC9939778",
+            "pmid": "36815006"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Tomography",
+            "uri": "http://edamontology.org/topic_3452"
+        }
+    ]
+}
diff --git a/data/deepbrainipp/deepbrainipp.biotools.json b/data/deepbrainipp/deepbrainipp.biotools.json
new file mode 100644
index 0000000000000..c911fc8244214
--- /dev/null
+++ b/data/deepbrainipp/deepbrainipp.biotools.json
@@ -0,0 +1,85 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-10T00:15:44.025632Z",
+    "biotoolsCURIE": "biotools:deepbrainipp",
+    "biotoolsID": "deepbrainipp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "khaled.khairy@stjude.org",
+            "name": "Khaled Khairy",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "shahinur.alam@stjude.org",
+            "name": "Shahinur Alam",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Stanislav S. Zakharenko"
+        },
+        {
+            "name": "Tae-Yeon Eom"
+        }
+    ],
+    "description": "An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/stjude/DeepBrainIPP",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-10T00:15:44.028070Z",
+    "license": "Apache-2.0",
+    "name": "DeepBrainIPP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FBINF.2022.865443",
+            "pmcid": "PMC9580949",
+            "pmid": "36304320"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Computer science",
+            "uri": "http://edamontology.org/topic_3316"
+        },
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        }
+    ]
+}
diff --git a/data/deepbsrpred/deepbsrpred.biotools.json b/data/deepbsrpred/deepbsrpred.biotools.json
new file mode 100644
index 0000000000000..ed64860ecc932
--- /dev/null
+++ b/data/deepbsrpred/deepbsrpred.biotools.json
@@ -0,0 +1,99 @@
+{
+    "additionDate": "2023-02-19T11:04:52.521258Z",
+    "biotoolsCURIE": "biotools:deepbsrpred",
+    "biotoolsID": "deepbsrpred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "gromiha@iitm.ac.in",
+            "name": "M. Michael Gromiha",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "DeepBSRPred, a binding site residue prediction method using protein sequence and predicted structures from AlphaFold2.",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://web.iitm.ac.in/bioinfo2/deepbsrpred/download.html"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Binding site prediction",
+                    "uri": "http://edamontology.org/operation_2575"
+                },
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                },
+                {
+                    "term": "Protein property calculation",
+                    "uri": "http://edamontology.org/operation_0250"
+                },
+                {
+                    "term": "Protein structure prediction",
+                    "uri": "http://edamontology.org/operation_0474"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://web.iitm.ac.in/bioinfo2/deepbsrpred/index.html",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-19T11:04:52.523772Z",
+    "license": "Other",
+    "name": "DeepBSRPred",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1007/S00726-022-03228-3",
+            "metadata": {
+                "abstract": "© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.Motivation: Proteins–protein interactions (PPIs) are important to govern several cellular activities. Amino acid residues, which are located at the interface are known as the binding sites and the information about binding sites helps to understand the binding affinities and functions of protein–protein complexes. Results: We have developed a deep neural network-based method, DeepBSRPred, for predicting the binding sites using protein sequence information and predicted structures from AlphaFold2. Specific sequence and structure-based features include position-specific scoring matrix (PSSM), solvent accessible surface area, conservation score and amino acid properties, and residue depth, respectively. Our method predicted the binding sites with an average F1 score of 0.73 in a dataset of 1236 proteins. Further, we compared the performance with other existing methods in the literature using four benchmark datasets and our method outperformed those methods. Availability and implementation: The DeepBSRPred web server can be found at https://web.iitm.ac.in/bioinfo2/deepbsrpred/index.html, along with all datasets used in this study. The trained models, the DeepBSRPred standalone source code, and the feature computation pipeline are freely available at https://web.iitm.ac.in/bioinfo2/deepbsrpred/download.html.",
+                "authors": [
+                    {
+                        "name": "Gromiha M.M."
+                    },
+                    {
+                        "name": "Nikam R."
+                    },
+                    {
+                        "name": "Yugandhar K."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Amino Acids",
+                "title": "DeepBSRPred: deep learning-based binding site residue prediction for proteins"
+            },
+            "pmid": "36574037"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein binding sites",
+            "uri": "http://edamontology.org/topic_3534"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/deepcas13/deepcas13.biotools.json b/data/deepcas13/deepcas13.biotools.json
new file mode 100644
index 0000000000000..07f57b1a149de
--- /dev/null
+++ b/data/deepcas13/deepcas13.biotools.json
@@ -0,0 +1,133 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T17:28:42.073044Z",
+    "biotoolsCURIE": "biotools:deepcas13",
+    "biotoolsID": "deepcas13",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "feiteng@mail.neu.edu.cn",
+            "name": "Teng Fei",
+            "orcidid": "https://orcid.org/0000-0001-9620-0450",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "wli2@childrensnational.org",
+            "name": "Wei Li",
+            "orcidid": "https://orcid.org/0000-0002-2163-7903",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A tool for modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches.",
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://bitbucket.org/weililab/deepcas13/src/master/"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "RNA secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0278"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://deepcas13.weililab.org",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T17:28:42.077681Z",
+    "license": "MIT",
+    "name": "DeepCas13",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41467-023-36316-3",
+            "metadata": {
+                "abstract": "A major challenge in the application of the CRISPR-Cas13d system is to accurately predict its guide-dependent on-target and off-target effect. Here, we perform CRISPR-Cas13d proliferation screens and design a deep learning model, named DeepCas13, to predict the on-target activity from guide sequences and secondary structures. DeepCas13 outperforms existing methods to predict the efficiency of guides targeting both protein-coding and non-coding RNAs. Guides targeting non-essential genes display off-target viability effects, which are closely related to their on-target efficiencies. Choosing proper negative control guides during normalization mitigates the associated false positives in proliferation screens. We apply DeepCas13 to the guides targeting lncRNAs, and identify lncRNAs that affect cell viability and proliferation in multiple cell lines. The higher prediction accuracy of DeepCas13 over existing methods is extensively confirmed via a secondary CRISPR-Cas13d screen and quantitative RT-PCR experiments. DeepCas13 is freely accessible via http://deepcas13.weililab.org.",
+                "authors": [
+                    {
+                        "name": "Chao L."
+                    },
+                    {
+                        "name": "Cheng X."
+                    },
+                    {
+                        "name": "Fei T."
+                    },
+                    {
+                        "name": "Li W."
+                    },
+                    {
+                        "name": "Li Z."
+                    },
+                    {
+                        "name": "Li Z."
+                    },
+                    {
+                        "name": "Peng J."
+                    },
+                    {
+                        "name": "Shan R."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Zhang H."
+                    },
+                    {
+                        "name": "Zhao W."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches"
+            },
+            "pmcid": "PMC9912244",
+            "pmid": "36765063"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/deepcausality/deepcausality.biotools.json b/data/deepcausality/deepcausality.biotools.json
new file mode 100644
index 0000000000000..02d8ce174b245
--- /dev/null
+++ b/data/deepcausality/deepcausality.biotools.json
@@ -0,0 +1,102 @@
+{
+    "additionDate": "2023-02-19T11:13:13.890672Z",
+    "biotoolsCURIE": "biotools:deepcausality",
+    "biotoolsID": "deepcausality",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Zhichao.Liu@fda.hhs.gov",
+            "name": "Zhichao Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xwxu@ualr.edu",
+            "name": "Xiaowei Xu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A general causal inference framework named DeepCausality to empirically estimate the causal factors for suspected endpoints embedded in the free text.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Named-entity and concept recognition",
+                    "uri": "http://edamontology.org/operation_3280"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/XingqiaoWang/https-github.com-XingqiaoWang-DeepCausality-LiverTox",
+    "language": [
+        "Perl",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-19T11:13:13.893139Z",
+    "license": "Not licensed",
+    "name": "DeepCausality",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FRAI.2022.999289",
+            "metadata": {
+                "abstract": "Copyright © 2022 Wang, Xu, Tong, Liu and Liu.Causality plays an essential role in multiple scientific disciplines, including the social, behavioral, and biological sciences and portions of statistics and artificial intelligence. Manual-based causality assessment from a large number of free text-based documents is very time-consuming, labor-intensive, and sometimes even impractical. Herein, we proposed a general causal inference framework named DeepCausality to empirically estimate the causal factors for suspected endpoints embedded in the free text. The proposed DeepCausality seamlessly incorporates AI-powered language models, named entity recognition and Judea Pearl's Do-calculus, into a general framework for causal inference to fulfill different domain-specific applications. We exemplified the utility of the proposed DeepCausality framework by employing the LiverTox database to estimate idiosyncratic drug-induced liver injury (DILI)-related causal terms and generate a knowledge-based causal tree for idiosyncratic DILI patient stratification. Consequently, the DeepCausality yielded a prediction performance with an accuracy of 0.92 and an F-score of 0.84 for the DILI prediction. Notably, 90% of causal terms enriched by the DeepCausality were consistent with the clinical causal terms defined by the American College of Gastroenterology (ACG) clinical guideline for evaluating suspected idiosyncratic DILI (iDILI). Furthermore, we observed a high concordance of 0.91 between the iDILI severity scores generated by DeepCausality and domain experts. Altogether, the proposed DeepCausality framework could be a promising solution for causality assessment from free text and is publicly available through https://github.com/XingqiaoWang/https-github.com-XingqiaoWang-DeepCausality-LiverTox.",
+                "authors": [
+                    {
+                        "name": "Liu Q."
+                    },
+                    {
+                        "name": "Liu Z."
+                    },
+                    {
+                        "name": "Tong W."
+                    },
+                    {
+                        "name": "Wang X."
+                    },
+                    {
+                        "name": "Xu X."
+                    }
+                ],
+                "date": "2022-12-06T00:00:00Z",
+                "journal": "Frontiers in Artificial Intelligence",
+                "title": "DeepCausality: A general AI-powered causal inference framework for free text: A case study of LiverTox"
+            },
+            "pmcid": "PMC9763446",
+            "pmid": "36561659"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Workbench"
+    ],
+    "topic": [
+        {
+            "term": "Gastroenterology",
+            "uri": "http://edamontology.org/topic_3409"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        }
+    ]
+}
diff --git a/data/deepcelless/deepcelless.biotools.json b/data/deepcelless/deepcelless.biotools.json
new file mode 100644
index 0000000000000..d8d754025b0e7
--- /dev/null
+++ b/data/deepcelless/deepcelless.biotools.json
@@ -0,0 +1,114 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-19T11:17:09.391899Z",
+    "biotoolsCURIE": "biotools:deepcelless",
+    "biotoolsID": "deepcelless",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "limin@mail.csu.edu.cn",
+            "name": "Min Li",
+            "orcidid": "https://orcid.org/0000-0002-0188-1394",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                },
+                {
+                    "term": "Subcellular localisation prediction",
+                    "uri": "http://edamontology.org/operation_2489"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://csuligroup.com:8000/DeepCellEss",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-19T11:17:09.394506Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/CSUBioGroup/DeepCellEss"
+        }
+    ],
+    "name": "DeepCellEss",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC779",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions. RESULTS: In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes a convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-attention mechanism is used to provide residue-level model interpretability. For model construction, we collected extremely large-scale benchmark datasets across 323 cell lines. Extensive computational experiments demonstrate that DeepCellEss yields effective prediction performance for different cell lines and outperforms existing sequence-based methods as well as network-based centrality measures. Finally, we conducted some case studies to illustrate the necessity of considering specific cell lines and the superiority of DeepCellEss. We believe that DeepCellEss can serve as a useful tool for predicting essential proteins across different cell lines. AVAILABILITY AND IMPLEMENTATION: The DeepCellEss web server is available at http://csuligroup.com:8000/DeepCellEss. The source code and data underlying this study can be obtained from https://github.com/CSUBioGroup/DeepCellEss. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Li M."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Wu F.-X."
+                    },
+                    {
+                        "name": "Zeng M."
+                    },
+                    {
+                        "name": "Zhang F."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning"
+            },
+            "pmcid": "PMC9825760",
+            "pmid": "36458923"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Proteins",
+            "uri": "http://edamontology.org/topic_0078"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/deeplncpro/deeplncpro.biotools.json b/data/deeplncpro/deeplncpro.biotools.json
new file mode 100644
index 0000000000000..4d37f573c2416
--- /dev/null
+++ b/data/deeplncpro/deeplncpro.biotools.json
@@ -0,0 +1,116 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-10T00:01:53.657080Z",
+    "biotoolsCURIE": "biotools:deeplncpro",
+    "biotoolsID": "deeplncpro",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhaoqi@lnu.edu.cn",
+            "name": "Qi Zhao",
+            "orcidid": "https://orcid.org/0000-0001-9713-1864",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "greatchen@ncst.edu.cn",
+            "name": "Wei Chen",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Fulei Nie"
+        },
+        {
+            "name": "Qiang Tang"
+        },
+        {
+            "name": "Tianyang Zhang"
+        }
+    ],
+    "description": "An interpretable convolutional neural network model for identifying long non-coding RNA promoters.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Promoter prediction",
+                    "uri": "http://edamontology.org/operation_0440"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/zhangtian-yang/DeepLncPro",
+    "language": [
+        "JavaScript",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-10T00:01:53.660093Z",
+    "license": "MIT",
+    "name": "DeepLncPro",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC447",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Long non-coding RNA (lncRNA) plays important roles in a series of biological processes. The transcription of lncRNA is regulated by its promoter. Hence, accurate identification of lncRNA promoter will be helpful to understand its regulatory mechanisms. Since experimental techniques remain time consuming for gnome-wide promoter identification, developing computational tools to identify promoters are necessary. However, only few computational methods have been proposed for lncRNA promoter prediction and their performances still have room to be improved. In the present work, a convolutional neural network based model, called DeepLncPro, was proposed to identify lncRNA promoters in human and mouse. Comparative results demonstrated that DeepLncPro was superior to both state-of-the-art machine learning methods and existing models for identifying lncRNA promoters. Furthermore, DeepLncPro has the ability to extract and analyze transcription factor binding motifs from lncRNAs, which made it become an interpretable model. These results indicate that the DeepLncPro can server as a powerful tool for identifying lncRNA promoters. An open-source tool for DeepLncPro was provided at https://github.com/zhangtian-yang/DeepLncPro.",
+                "authors": [
+                    {
+                        "name": "Chen W."
+                    },
+                    {
+                        "name": "Nie F."
+                    },
+                    {
+                        "name": "Tang Q."
+                    },
+                    {
+                        "name": "Zhang T."
+                    },
+                    {
+                        "name": "Zhao Q."
+                    }
+                ],
+                "date": "2022-11-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "DeepLncPro: an interpretable convolutional neural network model for identifying long non-coding RNA promoters"
+            },
+            "pmid": "36209437"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/deepmr/deepmr.biotools.json b/data/deepmr/deepmr.biotools.json
new file mode 100644
index 0000000000000..107c3098522b0
--- /dev/null
+++ b/data/deepmr/deepmr.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T23:54:22.048627Z",
+    "biotoolsCURIE": "biotools:deepmr",
+    "biotoolsID": "deepmr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "stephenmalina@gmail.com",
+            "name": "Stephen Malina",
+            "orcidid": "https://orcid.org/0000-0002-7383-0094",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Daniel Cizin"
+        },
+        {
+            "name": "David A. Knowles"
+        }
+    ],
+    "description": "Investigating the causal knowledge of genomic deep learning models.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Modelling and simulation",
+                    "uri": "http://edamontology.org/operation_2426"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Sequence motif analysis",
+                    "uri": "http://edamontology.org/operation_2404"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/an1lam/deepmr",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T23:54:22.051389Z",
+    "license": "Not licensed",
+    "name": "DeepMR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/journal.pcbi.1009880",
+            "metadata": {
+                "abstract": "Copyright: © 2022 Malina et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Multi-task deep learning (DL) models can accurately predict diverse genomic marks from sequence, but whether these models learn the causal relationships between genomic marks is unknown. Here, we describe Deep Mendelian Randomization (DeepMR), a method for estimating causal relationships between genomic marks learned by genomic DL models. By combining Mendelian randomization with in silico mutagenesis, DeepMR obtains local (locus specific) and global estimates of (an assumed) linear causal relationship between marks. In a simulation designed to test recovery of pairwise causal relations between transcription factors (TFs), DeepMR gives accurate and unbiased estimates of the ‘true’ global causal effect, but its coverage decays in the presence of sequence-dependent confounding. We then apply DeepMR to examine the global relationships learned by a state-of-the-art DL model, BPNet, between TFs involved in reprogramming. DeepMR’s causal effect estimates validate previously hypothesized relationships between TFs and suggest new relationships for future investigation.",
+                "authors": [
+                    {
+                        "name": "Cizin D."
+                    },
+                    {
+                        "name": "Knowles D.A."
+                    },
+                    {
+                        "name": "Malina S."
+                    }
+                ],
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models"
+            },
+            "pmcid": "PMC9624391",
+            "pmid": "36265006"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/deeppervar/deeppervar.biotools.json b/data/deeppervar/deeppervar.biotools.json
new file mode 100644
index 0000000000000..36f5667907633
--- /dev/null
+++ b/data/deeppervar/deeppervar.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-18T23:06:10.846569Z",
+    "biotoolsCURIE": "biotools:deeppervar",
+    "biotoolsID": "deeppervar",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chen61@iu.edu",
+            "name": "Li Chen",
+            "orcidid": "http://orcid.org/0000-0001-9372-5606",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ye Wang"
+        }
+    ],
+    "description": "A multimodal deep learning framework for functional interpretation of genetic variants in personal genome.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Collapsing methods",
+                    "uri": "http://edamontology.org/operation_3791"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                },
+                {
+                    "term": "Variant prioritisation",
+                    "uri": "http://edamontology.org/operation_3226"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/lichen-lab/DeepPerVar",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-18T23:06:10.849797Z",
+    "license": "Not licensed",
+    "name": "DeepPerVar",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac696",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.MOTIVATION: Understanding the functional consequence of genetic variants, especially the non-coding ones, is important but particularly challenging. Genome-wide association studies (GWAS) or quantitative trait locus analyses may be subject to limited statistical power and linkage disequilibrium, and thus are less optimal to pinpoint the causal variants. Moreover, most existing machine-learning approaches, which exploit the functional annotations to interpret and prioritize putative causal variants, cannot accommodate the heterogeneity of personal genetic variations and traits in a population study, targeting a specific disease. RESULTS: By leveraging paired whole-genome sequencing data and epigenetic functional assays in a population study, we propose a multi-modal deep learning framework to predict genome-wide quantitative epigenetic signals by considering both personal genetic variations and traits. The proposed approach can further evaluate the functional consequence of non-coding variants on an individual level by quantifying the allelic difference of predicted epigenetic signals. By applying the approach to the ROSMAP cohort studying Alzheimer's disease (AD), we demonstrate that the proposed approach can accurately predict quantitative genome-wide epigenetic signals and in key genomic regions of AD causal genes, learn canonical motifs reported to regulate gene expression of AD causal genes, improve the partitioning heritability analysis and prioritize putative causal variants in a GWAS risk locus. Finally, we release the proposed deep learning model as a stand-alone Python toolkit and a web server. AVAILABILITY AND IMPLEMENTATION: https://github.com/lichen-lab/DeepPerVar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Chen L."
+                    },
+                    {
+                        "name": "Wang Y."
+                    }
+                ],
+                "date": "2022-12-13T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome"
+            },
+            "pmcid": "PMC9750124",
+            "pmid": "36271868"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        }
+    ]
+}
diff --git a/data/deepphewas/deepphewas.biotools.json b/data/deepphewas/deepphewas.biotools.json
new file mode 100644
index 0000000000000..554290081bad7
--- /dev/null
+++ b/data/deepphewas/deepphewas.biotools.json
@@ -0,0 +1,60 @@
+{
+    "additionDate": "2023-03-18T08:28:17.899136Z",
+    "biotoolsCURIE": "biotools:deepphewas",
+    "biotoolsID": "deepphewas",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "richard.packer@leicester.ac.uk",
+            "name": "Richard J Packer",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An R package for phenotype generation and association analysis for phenome-wide association studies.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Richard-Packer/DeepPheWAS",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-18T08:28:17.903801Z",
+    "license": "GPL-3.0",
+    "name": "DeepPheWAS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD073",
+            "pmid": "36744935"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Phenomics",
+            "uri": "http://edamontology.org/topic_3298"
+        }
+    ]
+}
diff --git a/data/deepprotacs/deepprotacs.biotools.json b/data/deepprotacs/deepprotacs.biotools.json
new file mode 100644
index 0000000000000..bd143b86c10d6
--- /dev/null
+++ b/data/deepprotacs/deepprotacs.biotools.json
@@ -0,0 +1,147 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T10:20:01.252252Z",
+    "biotoolsCURIE": "biotools:deepprotacs",
+    "biotoolsID": "deepprotacs",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "baifang@shanghaitech.edu.cn",
+            "name": "Fang Bai",
+            "orcidid": "https://orcid.org/0000-0003-1468-5568",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "gaoshh@shanghaitech.edu.cn",
+            "name": "Shenghua Gao",
+            "orcidid": "https://orcid.org/0000-0003-1626-2040",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "yang.xiaobao@gluetacs.com",
+            "name": "Xiaobao Yang",
+            "orcidid": "https://orcid.org/0000-0001-5266-7673",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "DeepPROTACs is a deep learning-based targeted degradation predictor for proteolysis targeting chimera (PROTACs).",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://bailab.siais.shanghaitech.edu.cn/services/deepprotacs/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T10:20:01.255819Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/fenglei104/DeepPROTACs"
+        }
+    ],
+    "name": "DeepPROTACs",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41467-022-34807-3",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).The rational design of PROTACs is difficult due to their obscure structure-activity relationship. This study introduces a deep neural network model - DeepPROTACs to help design potent PROTACs molecules. It can predict the degradation capacity of a proposed PROTAC molecule based on structures of given target protein and E3 ligase. The experimental dataset is mainly collected from PROTAC-DB and appropriately labeled according to the DC50 and Dmax values. In the model of DeepPROTACs, the ligands as well as the ligand binding pockets are generated and represented with graphs and fed into Graph Convolutional Networks for feature extraction. While SMILES representations of linkers are fed into a Bidirectional Long Short-Term Memory layer to generate the features. Experiments show that DeepPROTACs model achieves 77.95% average prediction accuracy and 0.8470 area under receiver operating characteristic curve on the test set. DeepPROTACs is available online at a web server (https://bailab.siais.shanghaitech.edu.cn/services/deepprotacs/) and at github (https://github.com/fenglei104/DeepPROTACs).",
+                "authors": [
+                    {
+                        "name": "Bai F."
+                    },
+                    {
+                        "name": "Dai Z."
+                    },
+                    {
+                        "name": "Gao S."
+                    },
+                    {
+                        "name": "Hu Q."
+                    },
+                    {
+                        "name": "Li F."
+                    },
+                    {
+                        "name": "Liu Z."
+                    },
+                    {
+                        "name": "Ma X."
+                    },
+                    {
+                        "name": "Sun R."
+                    },
+                    {
+                        "name": "Tian S."
+                    },
+                    {
+                        "name": "Wu S."
+                    },
+                    {
+                        "name": "Yang X."
+                    },
+                    {
+                        "name": "Zhang X."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs"
+            },
+            "pmcid": "PMC9681730",
+            "pmid": "36414666"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Computational chemistry",
+            "uri": "http://edamontology.org/topic_3332"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/deeppseudomsi/deeppseudomsi.biotools.json b/data/deeppseudomsi/deeppseudomsi.biotools.json
new file mode 100644
index 0000000000000..30b9e5e5a1d6c
--- /dev/null
+++ b/data/deeppseudomsi/deeppseudomsi.biotools.json
@@ -0,0 +1,141 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-22T01:44:44.863292Z",
+    "biotoolsCURIE": "biotools:deeppseudomsi",
+    "biotoolsID": "deeppseudomsi",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mirabela.rusu@stanford.edu",
+            "name": "Mirabela Rusu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "mpsnyder@stanford.edu",
+            "name": "Michael P. Snyder",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Wei Shao"
+        },
+        {
+            "name": "Xiaotao Shen",
+            "orcidid": "http://orcid.org/0000-0002-9608-9964"
+        }
+    ],
+    "description": "Deep Learning-based Pseudo-Mass Spectrometry Imaging Analysis for Precision Medicine.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://www.deeppseudomsi.org/tutorial/use_deeppseudomsi/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Natural product identification",
+                    "uri": "http://edamontology.org/operation_3803"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.deeppseudomsi.org/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-22T01:44:44.866484Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/jaspershen/deepPseudoMSI"
+        }
+    ],
+    "name": "deepPseudoMSI",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bib/bbac331",
+            "metadata": {
+                "abstract": "© 2022 The Author(s).Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides systematic profiling of metabolic. Yet, its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite identification, information loss and low reproducibility. Here, we present the deep-learning-based Pseudo-Mass Spectrometry Imaging (deepPseudoMSI) project (https://www.deeppseudomsi.org/), which converts LC-MS raw data to pseudo-MS images and then processes them by deep learning for precision medicine, such as disease diagnosis. Extensive tests based on real data demonstrated the superiority of deepPseudoMSI over traditional approaches and the capacity of our method to achieve an accurate individualized diagnosis. Our framework lays the foundation for future metabolic-based precision medicine.",
+                "authors": [
+                    {
+                        "name": "Chen S."
+                    },
+                    {
+                        "name": "Liang L."
+                    },
+                    {
+                        "name": "Rusu M."
+                    },
+                    {
+                        "name": "Shao W."
+                    },
+                    {
+                        "name": "Shen X."
+                    },
+                    {
+                        "name": "Snyder M.P."
+                    },
+                    {
+                        "name": "Wang C."
+                    },
+                    {
+                        "name": "Zhang S."
+                    }
+                ],
+                "date": "2022-09-01T00:00:00Z",
+                "journal": "Briefings in Bioinformatics",
+                "title": "Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine"
+            },
+            "pmid": "35947990"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        }
+    ]
+}
diff --git a/data/deeprmsd_vina/deeprmsd_vina.biotools.json b/data/deeprmsd_vina/deeprmsd_vina.biotools.json
new file mode 100644
index 0000000000000..ab238d3c39170
--- /dev/null
+++ b/data/deeprmsd_vina/deeprmsd_vina.biotools.json
@@ -0,0 +1,115 @@
+{
+    "additionDate": "2023-02-19T11:21:43.157959Z",
+    "biotoolsCURIE": "biotools:deeprmsd_vina",
+    "biotoolsID": "deeprmsd_vina",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "astrozheng@gmail.com",
+            "name": "Weifeng Li",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "lwf@sdu.edu.cn",
+            "name": "Liangzhen Zheng",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "DeepRMSD+Vina is a computational framework that integrates ligand binding pose optimization and screening.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Backbone modelling",
+                    "uri": "http://edamontology.org/operation_0479"
+                },
+                {
+                    "term": "Molecular docking",
+                    "uri": "http://edamontology.org/operation_0478"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/zchwang/DeepRMSD-Vina_Optimization",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-19T11:21:43.160725Z",
+    "license": "Not licensed",
+    "name": "DeepRMSD+Vina",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC520",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein-ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein-ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization.",
+                "authors": [
+                    {
+                        "name": "Kong A.W.-K."
+                    },
+                    {
+                        "name": "Li W."
+                    },
+                    {
+                        "name": "Lin M."
+                    },
+                    {
+                        "name": "Mu Y."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Wang Z."
+                    },
+                    {
+                        "name": "Wang Z."
+                    },
+                    {
+                        "name": "Wei Y."
+                    },
+                    {
+                        "name": "Zheng L."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function"
+            },
+            "pmid": "36502369"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Workbench"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Physics",
+            "uri": "http://edamontology.org/topic_3318"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        }
+    ]
+}
diff --git a/data/deepscm/deepscm.biotools.json b/data/deepscm/deepscm.biotools.json
new file mode 100644
index 0000000000000..e3b09edbb3484
--- /dev/null
+++ b/data/deepscm/deepscm.biotools.json
@@ -0,0 +1,80 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T01:32:11.520410Z",
+    "biotoolsCURIE": "biotools:deepscm",
+    "biotoolsID": "deepscm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Pin-Kuang Lai",
+            "orcidid": "http://orcid.org/0000-0003-2894-3900"
+        }
+    ],
+    "description": "An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Lailabcode/DeepSCM",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-17T01:32:11.523818Z",
+    "license": "CC-BY-NC-3.0",
+    "name": "DeepSCM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.csbj.2022.04.035",
+            "metadata": {
+                "abstract": "© 2022 The Author(s)Predicting high concentration antibody viscosity is essential for developing subcutaneous administration. Computer simulations provide promising tools to reach this aim. One such model is the spatial charge map (SCM) proposed by Agrawal and coworkers (mAbs. 2015, 8(1):43–48). SCM applies molecular dynamics simulations to calculate a score for the screening of antibody viscosity at high concentrations. However, molecular dynamics simulations are computationally costly and require structural information, a significant application bottleneck. In this work, high throughput computing was performed to calculate the SCM scores for 6596 nonredundant antibody variable regions. A convolutional neural network surrogate model, DeepSCM, requiring only sequence information, was then developed based on this dataset. The linear correlation coefficient of the DeepSCM and SCM scores achieved 0.9 on the test set (N = 1320). The DeepSCM model was applied to screen the viscosity of 38 therapeutic antibodies that SCM correctly classified and resulted in only one misclassification. The DeepSCM model will facilitate high concentration antibody viscosity screening. The code and parameters are freely available at https://github.com/Lailabcode/DeepSCM.",
+                "authors": [
+                    {
+                        "name": "Lai P.-K."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity"
+            },
+            "pmcid": "PMC9092385",
+            "pmid": "35832619"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular dynamics",
+            "uri": "http://edamontology.org/topic_0176"
+        }
+    ]
+}
diff --git a/data/deepsmirud/deepsmirud.biotools.json b/data/deepsmirud/deepsmirud.biotools.json
new file mode 100644
index 0000000000000..de07115daa1ef
--- /dev/null
+++ b/data/deepsmirud/deepsmirud.biotools.json
@@ -0,0 +1,130 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-28T13:56:40.265602Z",
+    "biotoolsCURIE": "biotools:deepsmirud",
+    "biotoolsID": "deepsmirud",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "li.deng@helmholtz-muenchen.de",
+            "name": "Li Deng",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xiawang@nwafu.edu.cn",
+            "name": "Xia Wang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jianfeng Sun",
+            "orcidid": "http://orcid.org/0000-0002-1274-5080"
+        },
+        {
+            "name": "Jinlong Ru",
+            "orcidid": "http://orcid.org/0000-0002-6757-6018"
+        }
+    ],
+    "description": "Precise prediction of regulatory effects on miRNA expression mediated by small molecular compounds using competing deep learning frameworks.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Relation extraction",
+                    "uri": "http://edamontology.org/operation_3625"
+                },
+                {
+                    "term": "miRNA expression analysis",
+                    "uri": "http://edamontology.org/operation_3792"
+                },
+                {
+                    "term": "miRNA target prediction",
+                    "uri": "http://edamontology.org/operation_0463"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/2003100127/deepsmirud",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-28T13:56:40.268080Z",
+    "license": "MIT",
+    "name": "DeepsmirUD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/ijms24031878",
+            "metadata": {
+                "abstract": "© 2023 by the authors.Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA–cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs.",
+                "authors": [
+                    {
+                        "name": "Chen S."
+                    },
+                    {
+                        "name": "Chen Z."
+                    },
+                    {
+                        "name": "Cribbs A.P."
+                    },
+                    {
+                        "name": "Deng L."
+                    },
+                    {
+                        "name": "Qi F."
+                    },
+                    {
+                        "name": "Ramos-Mucci L."
+                    },
+                    {
+                        "name": "Ru J."
+                    },
+                    {
+                        "name": "Sun J."
+                    },
+                    {
+                        "name": "Wang X."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "International Journal of Molecular Sciences",
+                "title": "DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning"
+            },
+            "pmcid": "PMC9915273",
+            "pmid": "36768205"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Pharmacogenomics",
+            "uri": "http://edamontology.org/topic_0208"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/deepst/deepst.biotools.json b/data/deepst/deepst.biotools.json
new file mode 100644
index 0000000000000..e72f024efbea4
--- /dev/null
+++ b/data/deepst/deepst.biotools.json
@@ -0,0 +1,83 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T23:43:31.608438Z",
+    "biotoolsCURIE": "biotools:deepst",
+    "biotoolsID": "deepst",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "qhjiang@hit.edu.cn",
+            "name": "Qinghua Jiang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Chang Xu"
+        },
+        {
+            "name": "Guohua Wang"
+        },
+        {
+            "name": "Xiyun Jin",
+            "orcidid": "https://orcid.org/0000-0003-2795-6451"
+        }
+    ],
+    "description": "Identifying spatial domains in spatial transcriptomics by deep learning.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/JiangBioLab/DeepST",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T23:43:31.611123Z",
+    "license": "MIT",
+    "name": "DeepST",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC901",
+            "pmid": "36250636"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/deeptoa/deeptoa.biotools.json b/data/deeptoa/deeptoa.biotools.json
new file mode 100644
index 0000000000000..3895ffba6d611
--- /dev/null
+++ b/data/deeptoa/deeptoa.biotools.json
@@ -0,0 +1,104 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-18T23:15:04.647499Z",
+    "biotoolsCURIE": "biotools:deeptoa",
+    "biotoolsID": "deeptoa",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "daniel.huson@uni-tuebingen.de",
+            "name": "Daniel H. Huson",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Anupam Gautam"
+        },
+        {
+            "name": "Wenhuan Zeng"
+        }
+    ],
+    "description": "An Ensemble Deep-Learning Approach to Predicting the Theater of Activity of a Microbiome.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Taxonomic classification",
+                    "uri": "http://edamontology.org/operation_3460"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://plabase.cs.uni-tuebingen.de/deeptoa/",
+    "lastUpdate": "2023-01-18T23:15:04.650085Z",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "http://ab.inf.uni-tuebingen.de/software/deeptoa"
+        }
+    ],
+    "name": "DeepToA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac584",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.MOTIVATION: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a 'theater of activity' (ToA). An important question is, to what degree does the taxonomic and functional content of the former depend on the (details of the) latter? Here, we investigate a related technical question: Given a taxonomic and/or functional profile estimated from metagenomic sequencing data, how to predict the associated ToA? We present a deep-learning approach to this question. We use both taxonomic and functional profiles as input. We apply node2vec to embed hierarchical taxonomic profiles into numerical vectors. We then perform dimension reduction using clustering, to address the sparseness of the taxonomic data and thus make the problem more amenable to deep-learning algorithms. Functional features are combined with textual descriptions of protein families or domains. We present an ensemble deep-learning framework DeepToA for predicting the ToA of amicrobial community, based on taxonomic and functional profiles. We use SHAP (SHapley Additive exPlanations) values to determine which taxonomic and functional features are important for the prediction. RESULTS: Based on 7560 metagenomic profiles downloaded from MGnify, classified into 10 different theaters of activity, we demonstrate that DeepToA has an accuracy of 98.30%. We show that adding textual information to functional features increases the accuracy. AVAILABILITY AND IMPLEMENTATION: Our approach is available at http://ab.inf.uni-tuebingen.de/software/deeptoa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Gautam A."
+                    },
+                    {
+                        "name": "Huson D.H."
+                    },
+                    {
+                        "name": "Zeng W."
+                    }
+                ],
+                "date": "2022-10-14T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome"
+            },
+            "pmid": "36029249"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Metagenomic sequencing",
+            "uri": "http://edamontology.org/topic_3837"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        }
+    ]
+}
diff --git a/data/deeptp/deeptp.biotools.json b/data/deeptp/deeptp.biotools.json
new file mode 100644
index 0000000000000..483718acc375d
--- /dev/null
+++ b/data/deeptp/deeptp.biotools.json
@@ -0,0 +1,114 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T08:32:48.899699Z",
+    "biotoolsCURIE": "biotools:deeptp",
+    "biotoolsID": "deeptp",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "wyyan@suda.edu.cn",
+            "name": "Wenying Yan",
+            "orcidid": "https://orcid.org/0000-0001-5016-575X",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "yyang@suda.edu.cn",
+            "name": "Yang Yang",
+            "orcidid": "https://orcid.org/0000-0002-4397-8215",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A Deep Learning Model for Thermophilic Protein Prediction.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein feature detection",
+                    "uri": "http://edamontology.org/operation_3092"
+                },
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                },
+                {
+                    "term": "Sequence feature detection",
+                    "uri": "http://edamontology.org/operation_0253"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.YangLab-MI.org.cn/DeepTP",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T08:32:48.903981Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/ZhaoDove/DeepTP_predictor"
+        }
+    ],
+    "name": "DeepTP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3390/IJMS24032217",
+            "metadata": {
+                "abstract": "Thermophilic proteins have important value in the fields of biopharmaceuticals and enzyme engineering. Most existing thermophilic protein prediction models are based on traditional machine learning algorithms and do not fully utilize protein sequence information. To solve this problem, a deep learning model based on self-attention and multiple-channel feature fusion was proposed to predict thermophilic proteins, called DeepTP. First, a large new dataset consisting of 20,842 proteins was constructed. Second, a convolutional neural network and bidirectional long short-term memory network were used to extract the hidden features in protein sequences. Different weights were then assigned to features through self-attention, and finally, biological features were integrated to build a prediction model. In a performance comparison with existing methods, DeepTP had better performance and scalability in an independent balanced test set and validation set, with AUC values of 0.944 and 0.801, respectively. In the unbalanced test set, DeepTP had an average precision (AP) of 0.536. The tool is freely available.",
+                "authors": [
+                    {
+                        "name": "Yan W."
+                    },
+                    {
+                        "name": "Yang Y."
+                    },
+                    {
+                        "name": "Zhao J."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "International Journal of Molecular Sciences",
+                "title": "DeepTP: A Deep Learning Model for Thermophilic Protein Prediction"
+            },
+            "pmcid": "PMC9917291",
+            "pmid": "36768540"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Enzymes",
+            "uri": "http://edamontology.org/topic_0821"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein sites, features and motifs",
+            "uri": "http://edamontology.org/topic_3510"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/deeptss/deeptss.biotools.json b/data/deeptss/deeptss.biotools.json
new file mode 100644
index 0000000000000..c06b9b8f26003
--- /dev/null
+++ b/data/deeptss/deeptss.biotools.json
@@ -0,0 +1,109 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-19T11:25:17.521690Z",
+    "biotoolsCURIE": "biotools:deeptss",
+    "biotoolsID": "deeptss",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jim.grigor@gmail.com",
+            "name": "Dimitris Grigoriadis",
+            "orcidid": "https://orcid.org/0000-0002-2386-5272",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "arhatzig@uth.gr",
+            "name": "Artemis G. Hatzigeorgiou",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "DeepTSS, a novel computational method for processing CAGE samples, that combines genomic signal processing (GSP), structural DNA features, evolutionary conservation evidence and raw DNA sequence with Deep Learning (DL) to provide single-nucleotide TSS predictions with unprecedented levels of performance.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Gene regulatory network analysis",
+                    "uri": "http://edamontology.org/operation_1781"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/DianaLaboratory/DeepTSS",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-19T11:25:17.524182Z",
+    "license": "MIT",
+    "name": "DeepTSS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-04945-Y",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: The widespread usage of Cap Analysis of Gene Expression (CAGE) has led to numerous breakthroughs in understanding the transcription mechanisms. Recent evidence in the literature, however, suggests that CAGE suffers from transcriptional and technical noise. Regardless of the sample quality, there is a significant number of CAGE peaks that are not associated with transcription initiation events. This type of signal is typically attributed to technical noise and more frequently to random five-prime capping or transcription bioproducts. Thus, the need for computational methods emerges, that can accurately increase the signal-to-noise ratio in CAGE data, resulting in error-free transcription start site (TSS) annotation and quantification of regulatory region usage. In this study, we present DeepTSS, a novel computational method for processing CAGE samples, that combines genomic signal processing (GSP), structural DNA features, evolutionary conservation evidence and raw DNA sequence with Deep Learning (DL) to provide single-nucleotide TSS predictions with unprecedented levels of performance. Results: To evaluate DeepTSS, we utilized experimental data, protein-coding gene annotations and computationally-derived genome segmentations by chromatin states. DeepTSS was found to outperform existing algorithms on all benchmarks, achieving 98% precision and 96% sensitivity (accuracy 95.4%) on the protein-coding gene strategy, with 96.66% of its positive predictions overlapping active chromatin, 98.27% and 92.04% co-localized with at least one transcription factor and H3K4me3 peak. Conclusions: CAGE is a key protocol in deciphering the language of transcription, however, as every experimental protocol, it suffers from biological and technical noise that can severely affect downstream analyses. DeepTSS is a novel DL-based method for effectively removing noisy CAGE signal. In contrast to existing software, DeepTSS does not require feature selection since the embedded convolutional layers can readily identify patterns and only utilize the important ones for the classification task. This study highlights the key role that DL can play in Molecular Biology, by removing the inherent flaws of experimental protocols, that form the backbone of contemporary research. Here, we show how DeepTSS can unleash the full potential of an already popular and mature method such as CAGE, and push the boundaries of coding and non-coding gene expression regulator research even further.",
+                "authors": [
+                    {
+                        "name": "Georgakilas G.K."
+                    },
+                    {
+                        "name": "Grigoriadis D."
+                    },
+                    {
+                        "name": "Hatzigeorgiou A.G."
+                    },
+                    {
+                        "name": "Perdikopanis N."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "DeepTSS: multi-branch convolutional neural network for transcription start site identification from CAGE data"
+            },
+            "pmcid": "PMC9743497",
+            "pmid": "36510136"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/defined-proteins/defined-proteins.biotools.json b/data/defined-proteins/defined-proteins.biotools.json
new file mode 100644
index 0000000000000..69e8f9188df12
--- /dev/null
+++ b/data/defined-proteins/defined-proteins.biotools.json
@@ -0,0 +1,93 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T23:36:26.057526Z",
+    "biotoolsCURIE": "biotools:defined-proteins",
+    "biotoolsID": "defined-proteins",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "igorb@bii.a-star.edu.sg",
+            "name": "Igor N. Berezovsky",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alexander Goncearenco"
+        },
+        {
+            "name": "Melvin Yin"
+        }
+    ],
+    "description": "Deriving and Using Descriptors of Elementary Functions in Rational Protein Design.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Backbone modelling",
+                    "uri": "http://edamontology.org/operation_0479"
+                },
+                {
+                    "term": "Protein design",
+                    "uri": "http://edamontology.org/operation_4008"
+                },
+                {
+                    "term": "Protein structure validation",
+                    "uri": "http://edamontology.org/operation_0321"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MelvinYin/Defined_Proteins",
+    "language": [
+        "C++",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T23:36:26.061606Z",
+    "license": "Not licensed",
+    "name": "DEFINED-PROTEINS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FBINF.2021.657529",
+            "pmcid": "PMC9581014",
+            "pmid": "36303771"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Protein folding, stability and design",
+            "uri": "http://edamontology.org/topic_0130"
+        },
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        },
+        {
+            "term": "Protein structural motifs and surfaces",
+            "uri": "http://edamontology.org/topic_0166"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/denodo/denodo.biotools.json b/data/denodo/denodo.biotools.json
new file mode 100644
index 0000000000000..96f9929233509
--- /dev/null
+++ b/data/denodo/denodo.biotools.json
@@ -0,0 +1,59 @@
+{
+    "additionDate": "2023-01-26T12:00:23.194508Z",
+    "biotoolsCURIE": "biotools:denodo",
+    "biotoolsID": "denodo",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "email": "support@denodo.com",
+            "name": "Angel Viña",
+            "note": "In 1999, Angel Viña, who was then a professor at the University of A Coruña, recognized that data repositories were likely to grow unsustainably large if businesses continued to integrate data in the traditional manner. He envisioned a data integration strategy based on data virtualization, a modern strategy in which users could integrate data without replicating it, and thus, Denodo was born.",
+            "typeEntity": "Person",
+            "url": "https://support.denodo.com/?utm_source=Denodo-web&utm_medium=Try-Denodo"
+        }
+    ],
+    "description": "For every organization data and its related infrastructure is constantly evolving. As a result, enterprise data will always remain distributed. The Denodo Platform gives IT organizations the flexibility to evolve their data strategies, migrating to the cloud, or logically unifying data warehouses and data lakes, without affecting business. The Denodo Platform also accelerates data provisioning through reduced data replication, it enables consistent security and governance across multiple systems, and it gives your business users the flexibility to choose their preferred applications. The only way you can accomplish this is through a logical data fabric powered by data virtualization. The Denodo Platform is the only solution that can meet this need. Read about the benefits of the Denodo Platform in this Forrester TEI Repo",
+    "documentation": [
+        {
+            "note": "Resources Denodo offers an extensive library of data virtualization resources aimed at helping you unleash the true value of your data.",
+            "type": [
+                "Other"
+            ],
+            "url": "https://www.denodo.com/en/resources?utm_source=Denodo-web&utm_medium=Try-Denodo"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://www.denodo.com/en/denodo-platform/denodo-express?utm_source=Denodo-web&utm_medium=Try-Denodo"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.denodo.com/en",
+    "lastUpdate": "2023-02-01T13:14:47.714709Z",
+    "license": "Proprietary",
+    "link": [
+        {
+            "type": [
+                "Technical monitoring"
+            ],
+            "url": "https://www.denodo.com/en/denodo-platform/services/overview"
+        }
+    ],
+    "name": "Denodo",
+    "owner": "iacs-biocomputacion"
+}
diff --git a/data/denvis/denvis.biotools.json b/data/denvis/denvis.biotools.json
new file mode 100644
index 0000000000000..0f86413fa11b2
--- /dev/null
+++ b/data/denvis/denvis.biotools.json
@@ -0,0 +1,113 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T01:45:10.917049Z",
+    "biotoolsCURIE": "biotools:denvis",
+    "biotoolsID": "denvis",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "a.krasoulis@deeplab.ai",
+            "name": "Agamemnon Krasoulis",
+            "orcidid": "http://orcid.org/0000-0002-0468-0627",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Stavros Theodorakis"
+        },
+        {
+            "name": "Nick Antonopoulos",
+            "orcidid": "http://orcid.org/0000-0002-3175-8338"
+        },
+        {
+            "name": "Vassilis Pitsikalis",
+            "orcidid": "http://orcid.org/0000-0002-1593-7491"
+        }
+    ],
+    "description": "Scalable and high-throughput virtual screening using graph neural networks with atomic and surface protein pocket features.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Protein-protein docking",
+                    "uri": "http://edamontology.org/operation_3899"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/deeplab-ai/denvis",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-17T01:45:10.919752Z",
+    "license": "GPL-3.0",
+    "name": "DENVIS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/acs.jcim.2c01057",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society.Computational methods for virtual screening can dramatically accelerate early-stage drug discovery by identifying potential hits for a specified target. Docking algorithms traditionally use physics-based simulations to address this challenge by estimating the binding orientation of a query protein-ligand pair and a corresponding binding affinity score. Over the recent years, classical and modern machine learning architectures have shown potential for outperforming traditional docking algorithms. Nevertheless, most learning-based algorithms still rely on the availability of the protein-ligand complex binding pose, typically estimated via docking simulations, which leads to a severe slowdown of the overall virtual screening process. A family of algorithms processing target information at the amino acid sequence level avoid this requirement, however, at the cost of processing protein data at a higher representation level. We introduce deep neural virtual screening (DENVIS), an end-to-end pipeline for virtual screening using graph neural networks (GNNs). By performing experiments on two benchmark databases, we show that our method performs competitively to several docking-based, machine learning-based, and hybrid docking/machine learning-based algorithms. By avoiding the intermediate docking step, DENVIS exhibits several orders of magnitude faster screening times (i.e., higher throughput) than both docking-based and hybrid models. When compared to an amino acid sequence-based machine learning model with comparable screening times, DENVIS achieves dramatically better performance. Some key elements of our approach include protein pocket modeling using a combination of atomic and surface features, the use of model ensembles, and data augmentation via artificial negative sampling during model training. In summary, DENVIS achieves competitive to state-of-the-art virtual screening performance, while offering the potential to scale to billions of molecules using minimal computational resources.",
+                "authors": [
+                    {
+                        "name": "Antonopoulos N."
+                    },
+                    {
+                        "name": "Krasoulis A."
+                    },
+                    {
+                        "name": "Pitsikalis V."
+                    },
+                    {
+                        "name": "Theodorakis S."
+                    }
+                ],
+                "date": "2022-10-10T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features"
+            },
+            "pmid": "36154119"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Protein structural motifs and surfaces",
+            "uri": "http://edamontology.org/topic_0166"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/detectimports/detectimports.biotools.json b/data/detectimports/detectimports.biotools.json
new file mode 100644
index 0000000000000..e56072fb78966
--- /dev/null
+++ b/data/detectimports/detectimports.biotools.json
@@ -0,0 +1,95 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T10:22:15.826620Z",
+    "biotoolsCURIE": "biotools:detectimports",
+    "biotoolsID": "detectimports",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xavier.didelot@warwick.ac.uk",
+            "name": "Xavier Didelot",
+            "orcidid": "https://orcid.org/0000-0003-1885-500X",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "DetectImports is a R package aimed at distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phylogenetic inference",
+                    "uri": "http://edamontology.org/operation_0323"
+                },
+                {
+                    "term": "Statistical inference",
+                    "uri": "http://edamontology.org/operation_3658"
+                },
+                {
+                    "term": "Tree dating",
+                    "uri": "http://edamontology.org/operation_3942"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/xavierdidelot/DetectImports",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-28T10:22:15.830446Z",
+    "license": "MIT",
+    "name": "DetectImports",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC761",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The ability to distinguish imported cases from locally acquired cases has important consequences for the selection of public health control strategies. Genomic data can be useful for this, for example, using a phylogeographic analysis in which genomic data from multiple locations are compared to determine likely migration events between locations. However, these methods typically require good samples of genomes from all locations, which is rarely available. RESULTS: Here, we propose an alternative approach that only uses genomic data from a location of interest. By comparing each new case with previous cases from the same location, we are able to detect imported cases, as they have a different genealogical distribution than that of locally acquired cases. We show that, when variations in the size of the local population are accounted for, our method has good sensitivity and excellent specificity for the detection of imports. We applied our method to data simulated under the structured coalescent model and demonstrate relatively good performance even when the local population has the same size as the external population. Finally, we applied our method to several recent genomic datasets from both bacterial and viral pathogens, and show that it can, in a matter of seconds or minutes, deliver important insights on the number of imports to a geographically limited sample of a pathogen population. AVAILABILITY AND IMPLEMENTATION: The R package DetectImports is freely available from https://github.com/xavierdidelot/DetectImports. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Didelot X."
+                    },
+                    {
+                        "name": "Helekal D."
+                    },
+                    {
+                        "name": "Kendall M."
+                    },
+                    {
+                        "name": "Ribeca P."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease"
+            },
+            "pmcid": "PMC9805578",
+            "pmid": "36440957"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Phylogeny",
+            "uri": "http://edamontology.org/topic_0084"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        }
+    ]
+}
diff --git a/data/dfinder/dfinder.biotools.json b/data/dfinder/dfinder.biotools.json
new file mode 100644
index 0000000000000..0e9cf3c1ae38b
--- /dev/null
+++ b/data/dfinder/dfinder.biotools.json
@@ -0,0 +1,108 @@
+{
+    "additionDate": "2023-02-19T11:29:22.508609Z",
+    "biotoolsCURIE": "biotools:dfinder",
+    "biotoolsID": "dfinder",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "jiajiepeng@nwpu.edu.cn",
+            "name": "Jiajie Peng",
+            "orcidid": "https://orcid.org/0000-0002-3857-7927",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A end-to-end graph embedding-based method named DFinder to identify DFIs. DFinder combines node attribute features and topological structure features to learn the representations of drugs and food constituents.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Text mining",
+                    "uri": "http://edamontology.org/operation_0306"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/23AIBox/23AIBox-DFinder",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-19T11:29:22.511118Z",
+    "license": "Not licensed",
+    "name": "DFinder",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC837",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Drug-food interactions (DFIs) occur when some constituents of food affect the bioaccessibility or efficacy of the drug by involving in drug pharmacodynamic and/or pharmacokinetic processes. Many computational methods have achieved remarkable results in link prediction tasks between biological entities, which show the potential of computational methods in discovering novel DFIs. However, there are few computational approaches that pay attention to DFI identification. This is mainly due to the lack of DFI data. In addition, food is generally made up of a variety of chemical substances. The complexity of food makes it difficult to generate accurate feature representations for food. Therefore, it is urgent to develop effective computational approaches for learning the food feature representation and predicting DFIs. RESULTS: In this article, we first collect DFI data from DrugBank and PubMed, respectively, to construct two datasets, named DrugBank-DFI and PubMed-DFI. Based on these two datasets, two DFI networks are constructed. Then, we propose a novel end-to-end graph embedding-based method named DFinder to identify DFIs. DFinder combines node attribute features and topological structure features to learn the representations of drugs and food constituents. In topology space, we adopt a simplified graph convolution network-based method to learn the topological structure features. In feature space, we use a deep neural network to extract attribute features from the original node attributes. The evaluation results indicate that DFinder performs better than other baseline methods. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/23AIBox/23AIBox-DFinder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Peng J."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Wang T."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Xiao Y."
+                    },
+                    {
+                        "name": "Yang J."
+                    },
+                    {
+                        "name": "Zeng X."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "DFinder: a novel end-to-end graph embedding-based method to identify drug-food interactions"
+            },
+            "pmcid": "PMC9828147",
+            "pmid": "36579885"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biotherapeutics",
+            "uri": "http://edamontology.org/topic_3374"
+        },
+        {
+            "term": "Drug metabolism",
+            "uri": "http://edamontology.org/topic_3375"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Pharmacology",
+            "uri": "http://edamontology.org/topic_0202"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/dgmp/dgmp.biotools.json b/data/dgmp/dgmp.biotools.json
new file mode 100644
index 0000000000000..9fc1f8204b954
--- /dev/null
+++ b/data/dgmp/dgmp.biotools.json
@@ -0,0 +1,84 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-19T11:35:33.394979Z",
+    "biotoolsCURIE": "biotools:dgmp",
+    "biotoolsID": "dgmp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Shao-Wu Zhang"
+        }
+    ],
+    "description": "A method to identify cancer driver genes by jointing directed graph convolutional network (DGCN) and multilayer perceptron (MLP).",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene prediction",
+                    "uri": "http://edamontology.org/operation_2454"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/NWPU-903PR/DGMP",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-19T11:35:33.397576Z",
+    "license": "MIT",
+    "name": "DGMP",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.GPB.2022.11.004",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsIdentification of cancer driver genes plays an important role in precision oncology research, which is helpful to understand cancer initiation and progression. However, most existing computational methods mainly used the protein–protein interaction (PPI) networks, or treated the directed gene regulatory networks (GRNs) as the undirected gene–gene association networks to identify the cancer driver genes, which will lose the unique structure regulatory information in the directed GRNs, and then affect the outcome of the cancer driver gene identification. Here, based on the multi-omics pan-cancer data (i.e., gene expression, mutation, copy number variation, and DNA methylation), we propose a novel method (called DGMP) to identify cancer driver genes by jointing directed graph convolutional network (DGCN) and multilayer perceptron (MLP). DGMP learns the multi-omics features of genes as well as the topological structure features in GRN with the DGCN model and uses MLP to weigh more on gene features for mitigating the bias toward the graph topological features in the DGCN learning process. The results on three GRNs show that DGMP outperforms other existing state-of-the-art methods. The ablation experimental results on the DawnNet network indicate that introducing MLP into DGCN can offset the performance degradation of DGCN, and jointing MLP and DGCN can effectively improve the performance of identifying cancer driver genes. DGMP can identify not only the highly mutated cancer driver genes but also the driver genes harboring other kinds of alterations (e.g., differential expression and aberrant DNA methylation) or genes involved in GRNs with other cancer genes. The source code of DGMP can be freely downloaded from https://github.com/NWPU-903PR/DGMP.",
+                "authors": [
+                    {
+                        "name": "Xu J.-Y."
+                    },
+                    {
+                        "name": "Zhang S.-W."
+                    },
+                    {
+                        "name": "Zhang T."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Genomics, Proteomics and Bioinformatics",
+                "title": "DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data"
+            },
+            "pmid": "36464123"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/dhu-pred/dhu-pred.biotools.json b/data/dhu-pred/dhu-pred.biotools.json
new file mode 100644
index 0000000000000..579c03f74d52e
--- /dev/null
+++ b/data/dhu-pred/dhu-pred.biotools.json
@@ -0,0 +1,97 @@
+{
+    "additionDate": "2023-02-11T07:29:43.250052Z",
+    "biotoolsCURIE": "biotools:dhu-pred",
+    "biotoolsID": "dhu-pred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tkhliefh@qu.edu.sa",
+            "name": "Tamim Alkhalifah",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Accurate prediction of dihydrouridine sites using position and composition variant features on diverse classifiers.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                },
+                {
+                    "term": "Sequence feature detection",
+                    "uri": "http://edamontology.org/operation_0253"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/taseersuleman/DHU-Prediction-app",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-11T07:29:43.252863Z",
+    "license": "Not licensed",
+    "name": "DHU-Pred",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.7717/PEERJ.14104",
+            "metadata": {
+                "abstract": "© 2022 Suleman et al.Background. Dihydrouridine (D) is a modified transfer RNA post-transcriptional modification (PTM) that occurs abundantly in bacteria, eukaryotes, and archaea. The D modification assists in the stability and conformational flexibility of tRNA. The D modification is also responsible for pulmonary carcinogenesis in humans. Objective. For the detection ofDsites, mass spectrometry and site-directed mutagenesis have been developed. However, both are labor-intensive and time-consuming methods. The availability of sequence data has provided the opportunity to build computational models for enhancing the identification of D sites. Based on the sequence data, the DHU-Pred model was proposed in this study to find possible D sites. Methodology. The model was built by employing comprehensive machine learning and feature extraction approaches. It was then validated using in-demand evaluation metrics and rigorous experimentation and testing approaches. Results. The DHU-Pred revealed an accuracy score of 96.9%, which was considerably higher compared to the existing D site predictors. Availability and Implementation. A user-friendly web server for the proposed model was also developed and is freely available for the researchers.",
+                "authors": [
+                    {
+                        "name": "Alkhalifah T."
+                    },
+                    {
+                        "name": "Alturise F."
+                    },
+                    {
+                        "name": "Khan Y.D."
+                    },
+                    {
+                        "name": "Suleman M.T."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-10-27T00:00:00Z",
+                "journal": "PeerJ",
+                "title": "DHU-Pred: accurate prediction of dihydrouridine sites using position and composition variant features on diverse classifiers"
+            },
+            "pmcid": "PMC9618264",
+            "pmid": "36320563"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein modifications",
+            "uri": "http://edamontology.org/topic_0601"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        }
+    ]
+}
diff --git a/data/diadpredictor/diadpredictor.biotools.json b/data/diadpredictor/diadpredictor.biotools.json
new file mode 100644
index 0000000000000..854c642247cbc
--- /dev/null
+++ b/data/diadpredictor/diadpredictor.biotools.json
@@ -0,0 +1,110 @@
+{
+    "additionDate": "2023-01-28T10:28:33.762748Z",
+    "biotoolsCURIE": "biotools:diadpredictor",
+    "biotoolsID": "diadpredictor",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "lixiao1688@163.com",
+            "name": "Xiao Li",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "DIADpredictor: in silico prediction for drug-induced autoimmune diseases (DIAD) with machine learning.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Nucleic acid structure prediction",
+                    "uri": "http://edamontology.org/operation_0475"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://diad.sapredictor.cn/",
+    "lastUpdate": "2023-01-28T10:28:33.765346Z",
+    "license": "Other",
+    "name": "DIADpredictor",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FIMMU.2022.1015409",
+            "metadata": {
+                "abstract": "Copyright © 2022 Guo, Zhang, Zhang, Hua, Zhang, Cui, Huang and Li.The incidence and complexity of drug-induced autoimmune diseases (DIAD) have been on the rise in recent years, which may lead to serious or fatal consequences. Besides, many environmental and industrial chemicals can also cause DIAD. However, there are few effective approaches to estimate the DIAD potential of drugs and other chemicals currently, and the structural characteristics and mechanism of action of DIAD compounds have not been clarified. In this study, we developed the in silico models for chemical DIAD prediction and investigated the structural characteristics of DIAD chemicals based on the reliable drug data on human autoimmune diseases. We collected 148 medications which were reported can cause DIAD clinically and 450 medications that clearly do not cause DIAD. Several different machine learning algorithms and molecular fingerprints were combined to develop the in silico models. The best performed model provided the good overall accuracy on validation set with 76.26%. The model was made freely available on the website http://diad.sapredictor.cn/. To further investigate the differences in structural characteristics between DIAD chemicals and non-DIAD chemicals, several key physicochemical properties were analyzed. The results showed that AlogP, molecular polar surface area (MPSA), and the number of hydrogen bond donors (nHDon) were significantly different between the DIAD and non-DIAD structures. They may be related to the DIAD toxicity of chemicals. In addition, 14 structural alerts (SA) for DIAD toxicity were detected from predefined substructures. The SAs may be helpful to explain the mechanism of action of drug induced autoimmune disease, and can used to identify the chemicals with potential DIAD toxicity. The structural alerts have been integrated in a structural alert-based web server SApredictor (http://www.sapredictor.cn). We hope the results could provide useful information for the recognition of DIAD chemicals and the insights of structural characteristics for chemical DIAD toxicity.",
+                "authors": [
+                    {
+                        "name": "Cui X."
+                    },
+                    {
+                        "name": "Guo H."
+                    },
+                    {
+                        "name": "Hua Y."
+                    },
+                    {
+                        "name": "Huang X."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Zhang P."
+                    },
+                    {
+                        "name": "Zhang P."
+                    },
+                    {
+                        "name": "Zhang R."
+                    }
+                ],
+                "date": "2022-10-24T00:00:00Z",
+                "journal": "Frontiers in Immunology",
+                "title": "Modeling and insights into the structural characteristics of drug-induced autoimmune diseases"
+            },
+            "pmcid": "PMC9637949",
+            "pmid": "36353637"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Toxicology",
+            "uri": "http://edamontology.org/topic_2840"
+        }
+    ]
+}
diff --git a/data/diagnomass/diagnomass.biotools.json b/data/diagnomass/diagnomass.biotools.json
new file mode 100644
index 0000000000000..c58d403586c3c
--- /dev/null
+++ b/data/diagnomass/diagnomass.biotools.json
@@ -0,0 +1,117 @@
+{
+    "additionDate": "2023-03-18T08:46:01.612851Z",
+    "biotoolsCURIE": "biotools:diagnomass",
+    "biotoolsID": "diagnomass",
+    "confidence_flag": "tool",
+    "cost": "Free of charge (with restrictions)",
+    "credit": [
+        {
+            "name": "Paulo C. Carvalho"
+        }
+    ],
+    "description": "A proteomics hub for pinpointing discriminative spectral clusters.",
+    "documentation": [
+        {
+            "type": [
+                "Training material"
+            ],
+            "url": "https://www.diagnomass.com/how/use.html"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                },
+                {
+                    "term": "Protein identification",
+                    "uri": "http://edamontology.org/operation_3767"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.diagnomass.com",
+    "lastUpdate": "2023-03-18T08:46:01.618018Z",
+    "name": "DiagnoMass",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.JPROT.2023.104853",
+            "metadata": {
+                "abstract": "Motivation: There are several well-established paradigms for identifying and pinpointing discriminative peptides/proteins using shotgun proteomic data; examples are peptide-spectrum matching, de novo sequencing, open searches, and even hybrid approaches. Such an arsenal of complementary paradigms can provide deep data coverage, albeit some unidentified discriminative peptides remain. Results: We present DiagnoMass, software tool that groups similar spectra into spectral clusters and then shortlists those clusters that are discriminative for biological conditions. DiagnoMass then communicates with proteomic tools to attempt the identification of such clusters. We demonstrate the effectiveness of DiagnoMass by analyzing proteomic data from Escherichia coli, Salmonella, and Shigella, listing many high-quality discriminative spectral clusters that had thus far remained unidentified by widely adopted proteomic tools. DiagnoMass can also classify proteomic profiles. We anticipate the use of DiagnoMass as a vital tool for pinpointing biomarkers. Availability: DiagnoMass and related documentation, including a usage protocol, are available at http://www.diagnomass.com.",
+                "authors": [
+                    {
+                        "name": "Barbosa V.C."
+                    },
+                    {
+                        "name": "Batthyany C."
+                    },
+                    {
+                        "name": "Camillo-Andrade A.C."
+                    },
+                    {
+                        "name": "Carvalho P.C."
+                    },
+                    {
+                        "name": "Chamot-Rooke J."
+                    },
+                    {
+                        "name": "Duran R."
+                    },
+                    {
+                        "name": "Fischer J.D.S.D.G."
+                    },
+                    {
+                        "name": "Gozzo F.C."
+                    },
+                    {
+                        "name": "Lima D.B."
+                    },
+                    {
+                        "name": "Santos M.D.M."
+                    },
+                    {
+                        "name": "Souza T.A.C.B."
+                    },
+                    {
+                        "name": "Valente R.H."
+                    }
+                ],
+                "date": "2023-04-15T00:00:00Z",
+                "journal": "Journal of Proteomics",
+                "title": "DiagnoMass: A proteomics hub for pinpointing discriminative spectral clusters"
+            },
+            "pmid": "36804625"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/diamin/diamin.biotools.json b/data/diamin/diamin.biotools.json
new file mode 100644
index 0000000000000..71e52b9f28af2
--- /dev/null
+++ b/data/diamin/diamin.biotools.json
@@ -0,0 +1,101 @@
+{
+    "additionDate": "2023-01-28T10:38:36.212207Z",
+    "biotoolsCURIE": "biotools:diamin",
+    "biotoolsID": "diamin",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "simona.rombo@unipa.it",
+            "name": "Simona E. Rombo",
+            "orcidid": "https://orcid.org/0000-0003-3833-835X",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "umberto.ferraro@uniroma1.it",
+            "name": "Umberto Ferraro Petrillo",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "DIAMIN is a high-level software library to facilitate the development of distributed applications for the efficient analysis of large-scale molecular interaction networks.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Protein-protein interaction analysis",
+                    "uri": "http://edamontology.org/operation_2949"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ldirocco/DIAMIN",
+    "language": [
+        "Java",
+        "Scala"
+    ],
+    "lastUpdate": "2023-01-28T10:38:36.215212Z",
+    "license": "GPL-3.0",
+    "name": "DIAMIN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05026-W",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Huge amounts of molecular interaction data are continuously produced and stored in public databases. Although many bioinformatics tools have been proposed in the literature for their analysis, based on their modeling through different types of biological networks, several problems still remain unsolved when the problem turns on a large scale. Results: We propose DIAMIN, that is, a high-level software library to facilitate the development of applications for the efficient analysis of large-scale molecular interaction networks. DIAMIN relies on distributed computing, and it is implemented in Java upon the framework Apache Spark. It delivers a set of functionalities implementing different tasks on an abstract representation of very large graphs, providing a built-in support for methods and algorithms commonly used to analyze these networks. DIAMIN has been tested on data retrieved from two of the most used molecular interactions databases, resulting to be highly efficient and scalable. As shown by different provided examples, DIAMIN can be exploited by users without any distributed programming experience, in order to perform various types of data analysis, and to implement new algorithms based on its primitives. Conclusions: The proposed DIAMIN has been proved to be successful in allowing users to solve specific biological problems that can be modeled relying on biological networks, by using its functionalities. The software is freely available and this will hopefully allow its rapid diffusion through the scientific community, to solve both specific data analysis and more complex tasks.",
+                "authors": [
+                    {
+                        "name": "Di Rocco L."
+                    },
+                    {
+                        "name": "Ferraro Petrillo U."
+                    },
+                    {
+                        "name": "Rombo S.E."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "DIAMIN: a software library for the distributed analysis of large-scale molecular interaction networks"
+            },
+            "pmcid": "PMC9652854",
+            "pmid": "36368948"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Biological databases",
+            "uri": "http://edamontology.org/topic_3071"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        }
+    ]
+}
diff --git a/data/diffbrainnet/diffbrainnet.biotools.json b/data/diffbrainnet/diffbrainnet.biotools.json
new file mode 100644
index 0000000000000..00b25ee667379
--- /dev/null
+++ b/data/diffbrainnet/diffbrainnet.biotools.json
@@ -0,0 +1,156 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-22T01:56:00.789649Z",
+    "biotoolsCURIE": "biotools:diffbrainnet",
+    "biotoolsID": "diffbrainnet",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "arloth@psych.mpg.de",
+            "name": "Janine Knauer-Arloth",
+            "orcidid": "http://orcid.org/0000-0003-3825-4279",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "binder@psych.mpg.de",
+            "name": "Elisabeth B. Binder",
+            "orcidid": "http://orcid.org/0000-0001-7088-6618",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Anthi C. Krontira",
+            "orcidid": "http://orcid.org/0000-0003-0125-0215"
+        },
+        {
+            "name": "Nathalie Gerstner",
+            "orcidid": "http://orcid.org/0000-0002-3111-5949"
+        }
+    ],
+    "description": "Differential analyses add new insights into the response to glucocorticoids at the level of genes, networks and brain regions.",
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://hub.docker.com/r/ngerst/diffbrainnet"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Gene regulatory network analysis",
+                    "uri": "http://edamontology.org/operation_1781"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://diffbrainnet.psych.mpg.de/app/diffbrainnet",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-22T01:56:00.792204Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://github.molgen.mpg.de/mpip/DiffBrainNet_ShinyApp"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.molgen.mpg.de/mpip/DiffBrainNet"
+        }
+    ],
+    "name": "DiffBrainNet",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.ynstr.2022.100496",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsGenome-wide gene expression analyses are invaluable tools for studying biological and disease processes, allowing a hypothesis-free comparison of expression profiles. Traditionally, transcriptomic analysis has focused on gene-level effects found by differential expression. In recent years, network analysis has emerged as an important additional level of investigation, providing information on molecular connectivity, especially for diseases associated with a large number of linked effects of smaller magnitude, like neuropsychiatric disorders. Here, we describe how combined differential expression and prior-knowledge-based differential network analysis can be used to explore complex datasets. As an example, we analyze the transcriptional responses following administration of the glucocorticoid/stress receptor agonist dexamethasone in 8 mouse brain regions important for stress processing. By applying a combination of differential network- and expression-analyses, we find that these explain distinct but complementary biological mechanisms of the glucocorticoid responses. Additionally, network analysis identifies new differentially connected partners of risk genes and can be used to generate hypotheses on molecular pathways affected. With DiffBrainNet (http://diffbrainnet.psych.mpg.de), we provide an analysis framework and a publicly available resource for the study of the transcriptional landscape of the mouse brain which can identify molecular pathways important for basic functioning and response to glucocorticoids in a brain-region specific manner.",
+                "authors": [
+                    {
+                        "name": "Binder E.B."
+                    },
+                    {
+                        "name": "Cruceanu C."
+                    },
+                    {
+                        "name": "Gerstner N."
+                    },
+                    {
+                        "name": "Knauer-Arloth J."
+                    },
+                    {
+                        "name": "Krontira A.C."
+                    },
+                    {
+                        "name": "Putz B."
+                    },
+                    {
+                        "name": "Rex-Haffner M."
+                    },
+                    {
+                        "name": "Roeh S."
+                    },
+                    {
+                        "name": "Sauer S."
+                    },
+                    {
+                        "name": "Schmidt M.V."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Neurobiology of Stress",
+                "title": "DiffBrainNet: Differential analyses add new insights into the response to glucocorticoids at the level of genes, networks and brain regions"
+            },
+            "pmcid": "PMC9755029",
+            "pmid": "36532379"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/difir_ct/difir_ct.biotools.json b/data/difir_ct/difir_ct.biotools.json
new file mode 100644
index 0000000000000..47e43cddaa98a
--- /dev/null
+++ b/data/difir_ct/difir_ct.biotools.json
@@ -0,0 +1,82 @@
+{
+    "additionDate": "2023-02-19T11:41:40.004602Z",
+    "biotoolsCURIE": "biotools:difir_ct",
+    "biotoolsID": "difir_ct",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Francisco Contijoch",
+            "orcidid": "https://orcid.org/0000-0001-9616-3274"
+        }
+    ],
+    "description": "Distance field representation to resolve motion artifacts in computed tomography",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://kunalmgupta.github.io/projects/DiFiR-CT.html",
+    "lastUpdate": "2023-02-19T11:41:40.007255Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://github.com/KunalMGupta/DIFIR-CT"
+        }
+    ],
+    "name": "DiFiR-CT",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1002/MP.16157",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.Background: Motion during data acquisition leads to artifacts in computed tomography (CT) reconstructions. In cases such as cardiac imaging, not only is motion unavoidable, but evaluating the motion of the object is of clinical interest. Reducing motion artifacts has typically been achieved by developing systems with faster gantry rotation or via algorithms which measure and/or estimate the displacement. However, these approaches have had limited success due to both physical constraints as well as the challenge of estimating non-rigid, temporally varying, and patient-specific motion fields. Purpose: To develop a novel reconstruction method which generates time-resolved, artifact-free images without estimation or explicit modeling of the motion. Methods: We describe an analysis-by-synthesis approach which progressively regresses a solution consistent with the acquired sinogram. In our method, we focus on the movement of object boundaries. Not only are the boundaries the source of image artifacts, but object boundaries can simultaneously be used to represent both the object as well as its motion over time without the need for an explicit motion model. We represent the object boundaries via a signed distance function (SDF) which can be efficiently modeled using neural networks. As a result, optimization can be performed under spatial and temporal smoothness constraints without the need for explicit motion estimation. Results: We illustrate the utility of DiFiR-CT in three imaging scenarios with increasing motion complexity: translation of a small circle, heart-like change in an ellipse's diameter, and a complex topological deformation. Compared to filtered backprojection, DiFiR-CT provides high quality image reconstruction for all three motions without hyperparameter tuning or change to the architecture. We also evaluate DiFiR-CT's robustness to noise in the acquired sinogram and found its reconstruction to be accurate across a wide range of noise levels. Lastly, we demonstrate how the approach could be used for multi-intensity scenes and illustrate the importance of the initial segmentation providing a realistic initialization. Code and supplemental movies are available at https://kunalmgupta.github.io/projects/DiFiR-CT.html. Conclusions: Projection data can be used to accurately estimate a temporally-evolving scene without the need for explicit motion estimation using a neural implicit representation and analysis-by-synthesis approach.",
+                "authors": [
+                    {
+                        "name": "Chen Z."
+                    },
+                    {
+                        "name": "Colvert B."
+                    },
+                    {
+                        "name": "Contijoch F."
+                    },
+                    {
+                        "name": "Gupta K."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Medical Physics",
+                "title": "DiFiR-CT: Distance field representation to resolve motion artifacts in computed tomography"
+            },
+            "pmid": "36515381"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Data acquisition",
+            "uri": "http://edamontology.org/topic_3077"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Tomography",
+            "uri": "http://edamontology.org/topic_3452"
+        }
+    ]
+}
diff --git a/data/dimple/dimple.biotools.json b/data/dimple/dimple.biotools.json
new file mode 100644
index 0000000000000..eded959859bc2
--- /dev/null
+++ b/data/dimple/dimple.biotools.json
@@ -0,0 +1,127 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T08:49:26.222642Z",
+    "biotoolsCURIE": "biotools:dimple",
+    "biotoolsID": "dimple",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "willow.coyote-maestas@ucsf.edu",
+            "name": "Willow Coyote-Maestas",
+            "orcidid": "https://orcid.org/0000-0001-9614-5340",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Deep insertion, deletion, and missense mutation libraries for exploring protein variation in evolution, disease, and biology.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Demultiplexing",
+                    "uri": "http://edamontology.org/operation_3933"
+                },
+                {
+                    "term": "Indel detection",
+                    "uri": "http://edamontology.org/operation_0452"
+                },
+                {
+                    "term": "PCR primer design",
+                    "uri": "http://edamontology.org/operation_0308"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/odcambc/DIMPLE",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T08:49:26.227252Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/odcambc/DIMPLE_manuscript_figures"
+        }
+    ],
+    "name": "DIMPLE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S13059-023-02880-6",
+            "metadata": {
+                "abstract": "Insertions and deletions (indels) enable evolution and cause disease. Due to technical challenges, indels are left out of most mutational scans, limiting our understanding of them in disease, biology, and evolution. We develop a low cost and bias method, DIMPLE, for systematically generating deletions, insertions, and missense mutations in genes, which we test on a range of targets, including Kir2.1. We use DIMPLE to study how indels impact potassium channel structure, disease, and evolution. We find deletions are most disruptive overall, beta sheets are most sensitive to indels, and flexible loops are sensitive to deletions yet tolerate insertions.",
+                "authors": [
+                    {
+                        "name": "Coyote-Maestas W."
+                    },
+                    {
+                        "name": "Fraser J.S."
+                    },
+                    {
+                        "name": "Grimes P.R."
+                    },
+                    {
+                        "name": "Macdonald C.B."
+                    },
+                    {
+                        "name": "Nedrud D."
+                    },
+                    {
+                        "name": "Trinidad D."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Genome Biology",
+                "title": "DIMPLE: deep insertion, deletion, and missense mutation libraries for exploring protein variation in evolution, disease, and biology"
+            },
+            "pmcid": "PMC9951526",
+            "pmid": "36829241"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Desktop application",
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Probes and primers",
+            "uri": "http://edamontology.org/topic_0632"
+        }
+    ]
+}
diff --git a/data/dira/dira.biotools.json b/data/dira/dira.biotools.json
new file mode 100644
index 0000000000000..1d9979d30ac1a
--- /dev/null
+++ b/data/dira/dira.biotools.json
@@ -0,0 +1,101 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T23:21:01.200222Z",
+    "biotoolsCURIE": "biotools:dira",
+    "biotoolsID": "dira",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "fhaghigh@asu.edu",
+            "name": "Fatemeh Haghighi",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jianming Liang"
+        },
+        {
+            "name": "Michael B Gotway"
+        },
+        {
+            "name": "Mohammad Reza Hosseinzadeh Taher",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Backbone modelling",
+                    "uri": "http://edamontology.org/operation_0479"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/JLiangLab/DiRA",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T23:21:01.203814Z",
+    "license": "Other",
+    "name": "DiRA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1109/CVPR52688.2022.02016",
+            "metadata": {
+                "abstract": "© 2022 IEEE.Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, omit their synergistic effects on each other in a ternary setup, which, we envision, can sig-nificantly benefit deep semantic representation learning. To realize this vision, we have developed DiRA, thefirstframework that unites discriminative, restorative, and adversarial learning in a unified manner to collaboratively glean complementary visual information from unlabeled medical images for fine-grained semantic representation learning. Our extensive experiments demonstrate that DiRA (1) encourages collaborative learning among three learning ingredients, resulting in more generalizable representation across organs, diseases, and modalities; (2) outperforms fully supervised ImageNet models and increases robustness in small data regimes, reducing annotation cost across multiple medical imaging applications; (3) learns fine-grained semantic representation, facilitating accurate lesion localization with only image-level annotation; and (4) enhances state-of-the-art restorative approaches, revealing that DiRA is a general mechanism for united representation learning. All code and pretrained models are available at https://github.com/JLiangLab/DiRA.",
+                "authors": [
+                    {
+                        "name": "Gotway M.B."
+                    },
+                    {
+                        "name": "Haghighi F."
+                    },
+                    {
+                        "name": "Liang J."
+                    },
+                    {
+                        "name": "Taher M.R.H."
+                    }
+                ],
+                "citationCount": 3,
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
+                "title": "DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis"
+            },
+            "pmcid": "PMC9615927",
+            "pmid": "36313959"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        }
+    ]
+}
diff --git a/data/directrmdb/directrmdb.biotools.json b/data/directrmdb/directrmdb.biotools.json
new file mode 100644
index 0000000000000..7ac25092366bf
--- /dev/null
+++ b/data/directrmdb/directrmdb.biotools.json
@@ -0,0 +1,134 @@
+{
+    "additionDate": "2023-01-28T10:44:32.704567Z",
+    "biotoolsCURIE": "biotools:directrmdb",
+    "biotoolsID": "directrmdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "kunqi.chen@fjmu.edu.cn",
+            "name": "Kunqi Chen",
+            "orcidid": "https://orcid.org/0000-0002-6025-8957",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "daiyun.huang@liverpool.ac.uk",
+            "name": "Daiyun Hang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhen.wei01@xjtlu.edu.cn",
+            "name": "Zhen Wei",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A database of post-transcriptional RNA modifications unveiled from direct RNA sequencing technology.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "RNA secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0278"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.rnamd.org/directRMDB/",
+    "lastUpdate": "2023-01-28T10:44:32.708677Z",
+    "license": "Other",
+    "name": "DirectRMDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1061",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.With advanced technologies to map RNA modifications, our understanding of them has been revolutionized, and they are seen to be far more widespread and important than previously thought. Current next-generation sequencing (NGS)-based modification profiling methods are blind to RNA modifications and thus require selective chemical treatment or antibody immunoprecipitation methods for particular modification types. They also face the problem of short read length, isoform ambiguities, biases and artifacts. Direct RNA sequencing (DRS) technologies, commercialized by Oxford Nanopore Technologies (ONT), enable the direct interrogation of any given modification present in individual transcripts and promise to address the limitations of previous NGS-based methods. Here, we present the first ONT-based database of quantitative RNA modification profiles, DirectRMDB, which includes 16 types of modification and a total of 904,712 modification sites in 25 species identified from 39 independent studies. In addition to standard functions adopted by existing databases, such as gene annotations and post-transcriptional association analysis, we provide a fresh view of RNA modifications, which enables exploration of the epitranscriptome in an isoform-specific manner. The DirectRMDB database is freely available at: http://www.rnamd.org/directRMDB/.",
+                "authors": [
+                    {
+                        "name": "Chen K."
+                    },
+                    {
+                        "name": "Hang D."
+                    },
+                    {
+                        "name": "Jia G."
+                    },
+                    {
+                        "name": "Jiang J."
+                    },
+                    {
+                        "name": "Ma J."
+                    },
+                    {
+                        "name": "Meng J."
+                    },
+                    {
+                        "name": "Rigden D.J."
+                    },
+                    {
+                        "name": "Song B."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Wei Z."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "de Magalhaes J.P."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "DirectRMDB: a database of post-transcriptional RNA modifications unveiled from direct RNA sequencing technology"
+            },
+            "pmcid": "PMC9825532",
+            "pmid": "36382409"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/discanvis/discanvis.biotools.json b/data/discanvis/discanvis.biotools.json
new file mode 100644
index 0000000000000..0a2b441b8981e
--- /dev/null
+++ b/data/discanvis/discanvis.biotools.json
@@ -0,0 +1,106 @@
+{
+    "additionDate": "2023-02-19T11:46:34.811442Z",
+    "biotoolsCURIE": "biotools:discanvis",
+    "biotoolsID": "discanvis",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "zsuzsanna.dosztanyi@ttk.elte.hu",
+            "name": "Zsuzsanna Dosztányi",
+            "orcidid": "https://orcid.org/0000-0002-3624-5937",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Visualizing integrated structural and functional annotations to better understand the effect of cancer mutations located within disordered proteins.",
+    "documentation": [
+        {
+            "type": [
+                "API documentation"
+            ],
+            "url": "https://discanvis.elte.hu/access_data"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Protein disorder prediction",
+                    "uri": "http://edamontology.org/operation_3904"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://discanvis.elte.hu/",
+    "lastUpdate": "2023-02-19T11:46:34.813891Z",
+    "license": "Not licensed",
+    "name": "DisCanVis",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1002/PRO.4522",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.Intrinsically disordered proteins (IDPs) play important roles in a wide range of biological processes and have been associated with various diseases, including cancer. In the last few years, cancer genome projects have systematically collected genetic variations underlying multiple cancer types. In parallel, the number and different types of disordered proteins characterized by experimental methods have also significantly increased. Nevertheless, the role of IDPs in various types of cancer is still not well understood. In this work, we present DisCanVis, a novel visualization tool for cancer mutations with a special focus on IDPs. In order to aid the interpretation of observed mutations, genome level information is combined with information about the structural and functional properties of proteins. The web server enables users to inspect individual proteins, collect examples with existing annotations of protein disorder and associated function or to discover currently uncharacterized examples with likely disease relevance. Through a REST API interface and precompiled tables the analysis can be extended to a group of proteins.",
+                "authors": [
+                    {
+                        "name": "Deutsch N."
+                    },
+                    {
+                        "name": "Dosztanyi Z."
+                    },
+                    {
+                        "name": "Erdos G."
+                    },
+                    {
+                        "name": "Pajkos M."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Protein Science",
+                "title": "DisCanVis: Visualizing integrated structural and functional annotations to better understand the effect of cancer mutations located within disordered proteins"
+            },
+            "pmcid": "PMC9793970",
+            "pmid": "36452990"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Protein disordered structure",
+            "uri": "http://edamontology.org/topic_3538"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        }
+    ]
+}
diff --git a/data/disco_qr/disco_qr.biotools.json b/data/disco_qr/disco_qr.biotools.json
new file mode 100644
index 0000000000000..910dd2b45edb5
--- /dev/null
+++ b/data/disco_qr/disco_qr.biotools.json
@@ -0,0 +1,79 @@
+{
+    "additionDate": "2023-03-18T08:57:13.242556Z",
+    "biotoolsCURIE": "biotools:disco_qr",
+    "biotoolsID": "disco_qr",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "warnow@illinois.edu",
+            "name": "Tandy Warnow",
+            "orcidid": "https://orcid.org/0000-0001-7717-3514",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Yasamin Tabatabaee"
+        }
+    ],
+    "description": "DISCO+QR, a new approach to rooting species trees that first uses DISCO to address GDL and then uses QR to perform rooting in the presence of incomplete lineage sorting.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene tree construction",
+                    "uri": "http://edamontology.org/operation_0553"
+                },
+                {
+                    "term": "Phylogenetic tree reconciliation",
+                    "uri": "http://edamontology.org/operation_3947"
+                },
+                {
+                    "term": "Species tree construction",
+                    "uri": "http://edamontology.org/operation_0544"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/JSdoubleL/DISCO",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T08:57:35.081175Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/ytabatabaee/Quintet-Rooting"
+        }
+    ],
+    "name": "DISCO+QR",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOADV/VBAD015",
+            "pmcid": "PMC9923442",
+            "pmid": "36789293"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene and protein families",
+            "uri": "http://edamontology.org/topic_0623"
+        },
+        {
+            "term": "Phylogeny",
+            "uri": "http://edamontology.org/topic_0084"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/disprot/disprot.biotools.json b/data/disprot/disprot.biotools.json
index ffe4a88c59ca8..9c96334d88782 100644
--- a/data/disprot/disprot.biotools.json
+++ b/data/disprot/disprot.biotools.json
@@ -11,7 +11,7 @@
             "typeRole": [
                 "Primary contact"
             ],
-            "url": "http://protein.bio.unipd.it"
+            "url": "https://biocomputingup.it/"
         },
         {
             "name": "ELIXIR-ITA-PADOVA",
@@ -113,8 +113,8 @@
             ]
         }
     ],
-    "homepage": "http://www.disprot.org/",
-    "lastUpdate": "2022-02-10T13:09:54.461208Z",
+    "homepage": "https://disprot.org/",
+    "lastUpdate": "2023-02-28T14:18:41.820859Z",
     "license": "CC-BY-NC-1.0",
     "maturity": "Mature",
     "name": "Database of protein disorder (DisProt)",
@@ -132,12 +132,25 @@
     "owner": "ELIXIR-ITA-PADOVA",
     "publication": [
         {
-            "doi": "10.1093/nar/gkw1279",
+            "doi": "10.1093/nar/gkab1082",
             "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure.",
                 "authors": [
+                    {
+                        "name": "Acs V."
+                    },
                     {
                         "name": "Aspromonte M.C."
                     },
+                    {
+                        "name": "Bassot C."
+                    },
+                    {
+                        "name": "Chasapi A."
+                    },
+                    {
+                        "name": "Chemes L.B."
+                    },
                     {
                         "name": "Davey N.E."
                     },
@@ -145,25 +158,46 @@
                         "name": "Davidovic R."
                     },
                     {
-                        "name": "Dosztanyi Z."
+                        "name": "Dobson L."
                     },
                     {
-                        "name": "Dunker A.K."
+                        "name": "Dosztanyi Z."
                     },
                     {
                         "name": "Elofsson A."
                     },
                     {
-                        "name": "Gasparini A."
+                        "name": "Erdos G."
+                    },
+                    {
+                        "name": "Farahi N."
+                    },
+                    {
+                        "name": "Ficho E."
+                    },
+                    {
+                        "name": "Gaudet P."
+                    },
+                    {
+                        "name": "Giglio M."
+                    },
+                    {
+                        "name": "Glavina J."
                     },
                     {
                         "name": "Hatos A."
                     },
                     {
-                        "name": "Kajava A.V."
+                        "name": "Iglesias V."
+                    },
+                    {
+                        "name": "Iserte J."
+                    },
+                    {
+                        "name": "Kalman Z."
                     },
                     {
-                        "name": "Kalmar L."
+                        "name": "Lambrughi M."
                     },
                     {
                         "name": "Lazar T."
@@ -178,94 +212,125 @@
                         "name": "Macedo-Ribeiro S."
                     },
                     {
-                        "name": "Macossay-Castillo M."
+                        "name": "Maiani E."
+                    },
+                    {
+                        "name": "Marchetti J."
+                    },
+                    {
+                        "name": "Marino-Buslje C."
                     },
                     {
                         "name": "Meszaros A."
                     },
                     {
-                        "name": "Micetic I."
+                        "name": "Meszaros B."
                     },
                     {
                         "name": "Minervini G."
                     },
                     {
-                        "name": "Murvai N."
+                        "name": "Monzon A.M."
                     },
                     {
-                        "name": "Necci M."
+                        "name": "Nadendla S."
                     },
                     {
-                        "name": "Oldfield C.J."
+                        "name": "Nilsson J.F."
                     },
                     {
-                        "name": "Piovesan D."
+                        "name": "Novotny M."
                     },
                     {
-                        "name": "Pujols J."
+                        "name": "Ouzounis C.A."
                     },
                     {
-                        "name": "Quaglia F."
+                        "name": "Pajkos M."
                     },
                     {
-                        "name": "Roche D.B."
+                        "name": "Palopoli N."
                     },
                     {
-                        "name": "Salladini E."
+                        "name": "Pancsa R."
                     },
                     {
-                        "name": "Schad E."
+                        "name": "Papaleo E."
                     },
                     {
-                        "name": "Schramm A."
+                        "name": "Parisi G."
                     },
                     {
-                        "name": "Szabo B."
+                        "name": "Pena-Diaz S."
                     },
                     {
-                        "name": "Tabaro F."
+                        "name": "Pereira P.J.B."
                     },
                     {
-                        "name": "Tantos A."
+                        "name": "Piovesan D."
                     },
                     {
-                        "name": "Tompa P."
+                        "name": "Pozzati G."
                     },
                     {
-                        "name": "Tonello F."
+                        "name": "Promponas V.J."
                     },
                     {
-                        "name": "Tosatto S.C."
+                        "name": "Pujols J."
                     },
                     {
-                        "name": "Tsirigos K.D."
+                        "name": "Quaglia F."
                     },
                     {
-                        "name": "Uversky V.N."
+                        "name": "Rocha A.C.S."
                     },
                     {
-                        "name": "Veljkovic N."
+                        "name": "Salas M."
                     },
                     {
-                        "name": "Ventura S."
+                        "name": "Salladini E."
+                    },
+                    {
+                        "name": "Santos J."
+                    },
+                    {
+                        "name": "Sawicki L.R."
+                    },
+                    {
+                        "name": "Schad E."
                     },
                     {
-                        "name": "Vranken W."
+                        "name": "Shenoy A."
                     },
                     {
-                        "name": "Warholm P."
+                        "name": "Szaniszlo T."
+                    },
+                    {
+                        "name": "Tompa P."
+                    },
+                    {
+                        "name": "Tosatto S.C.E."
+                    },
+                    {
+                        "name": "Tsirigos K.D."
+                    },
+                    {
+                        "name": "Veljkovic N."
+                    },
+                    {
+                        "name": "Ventura S."
                     }
                 ],
-                "citationCount": 19,
-                "date": "2017-01-04T00:00:00Z",
-                "journal": "Nucleic acids research",
-                "title": "Corrigendum: DisProt 7.0: a major update of the database of disordered proteins"
+                "citationCount": 32,
+                "date": "2022-01-07T00:00:00Z",
+                "journal": "Nucleic Acids Research",
+                "title": "DisProt in 2022: Improved quality and accessibility of protein intrinsic disorder annotation"
             },
-            "pmcid": "PMC5210598",
-            "pmid": "27965415",
+            "pmcid": "PMC8728214",
+            "pmid": "34850135",
             "type": [
                 "Primary"
-            ]
+            ],
+            "version": "9"
         }
     ],
     "toolType": [
@@ -281,6 +346,6 @@
     ],
     "validated": 1,
     "version": [
-        "7.0"
+        "9.0"
     ]
 }
diff --git a/data/distilprotbert/distilprotbert.biotools.json b/data/distilprotbert/distilprotbert.biotools.json
new file mode 100644
index 0000000000000..b116829cc369d
--- /dev/null
+++ b/data/distilprotbert/distilprotbert.biotools.json
@@ -0,0 +1,105 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T22:30:58.147285Z",
+    "biotoolsCURIE": "biotools:distilprotbert",
+    "biotoolsID": "distilprotbert",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yaron.geffen@biu.ac.il",
+            "name": "Yaron Geffen",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Yanay Ofran"
+        },
+        {
+            "name": "Ron Unger",
+            "orcidid": "http://orcid.org/0000-0003-4153-3922"
+        }
+    ],
+    "description": "A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                },
+                {
+                    "term": "Protein structure validation",
+                    "uri": "http://edamontology.org/operation_0321"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/yarongef/DistilProtBert",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T22:30:58.149837Z",
+    "license": "MIT",
+    "name": "DistilProtBert",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac474",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.SUMMARY: Recently, deep learning models, initially developed in the field of natural language processing (NLP), were applied successfully to analyze protein sequences. A major drawback of these models is their size in terms of the number of parameters needed to be fitted and the amount of computational resources they require. Recently, 'distilled' models using the concept of student and teacher networks have been widely used in NLP. Here, we adapted this concept to the problem of protein sequence analysis, by developing DistilProtBert, a distilled version of the successful ProtBert model. Implementing this approach, we reduced the size of the network and the running time by 50%, and the computational resources needed for pretraining by 98% relative to ProtBert model. Using two published tasks, we showed that the performance of the distilled model approaches that of the full model. We next tested the ability of DistilProtBert to distinguish between real and random protein sequences. The task is highly challenging if the composition is maintained on the level of singlet, doublet and triplet amino acids. Indeed, traditional machine-learning algorithms have difficulties with this task. Here, we show that DistilProtBert preforms very well on singlet, doublet and even triplet-shuffled versions of the human proteome, with AUC of 0.92, 0.91 and 0.87, respectively. Finally, we suggest that by examining the small number of false-positive classifications (i.e. shuffled sequences classified as proteins by DistilProtBert), we may be able to identify de novo potential natural-like proteins based on random shuffling of amino acid sequences. AVAILABILITY AND IMPLEMENTATION: https://github.com/yarongef/DistilProtBert.",
+                "authors": [
+                    {
+                        "name": "Geffen Y."
+                    },
+                    {
+                        "name": "Ofran Y."
+                    },
+                    {
+                        "name": "Unger R."
+                    }
+                ],
+                "date": "2022-09-16T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "DistilProtBert: a distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts"
+            },
+            "pmid": "36124789"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/divcom/divcom.biotools.json b/data/divcom/divcom.biotools.json
new file mode 100644
index 0000000000000..d322538ff2198
--- /dev/null
+++ b/data/divcom/divcom.biotools.json
@@ -0,0 +1,77 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T23:13:54.707010Z",
+    "biotoolsCURIE": "biotools:divcom",
+    "biotoolsID": "divcom",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ilias.lagkouvardos@tum.de",
+            "name": "Ilias Lagkouvardos",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Evangelia Intze"
+        }
+    ],
+    "description": "A Tool for Systematic Partition of Groups of Microbial Profiles Into Intrinsic Subclusters and Distance-Based Subgroup Comparisons.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Statistical calculation",
+                    "uri": "http://edamontology.org/operation_2238"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Lagkouvardos/DivCom",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-09T23:13:54.711087Z",
+    "license": "MIT",
+    "name": "DivCom",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FBINF.2022.864382",
+            "pmcid": "PMC9580884",
+            "pmid": "36304338"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        }
+    ]
+}
diff --git a/data/divik/divik.biotools.json b/data/divik/divik.biotools.json
new file mode 100644
index 0000000000000..4604968ee9e32
--- /dev/null
+++ b/data/divik/divik.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-19T11:50:30.435957Z",
+    "biotoolsCURIE": "biotools:divik",
+    "biotoolsID": "divik",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Joanna.Polanska@polsl.pl",
+            "name": "Joanna Polanska",
+            "orcidid": "https://orcid.org/0000-0001-8004-9864",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A scalable stepwise algorithm with local data-driven feature space adaptation for segmenting high-dimensional datasets.",
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://hub.docker.com/r/gmrukwa/divik"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pypi.org/project/divik/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-19T11:50:30.438605Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/gmrukwa/divik/"
+        }
+    ],
+    "name": "DiviK",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05093-Z",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible—therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, automated analyses require experience setting the algorithms’ hyperparameters and expert knowledge about the analysed biological processes. Moreover, feature engineering is needed to obtain valuable results because of the numerous features measured. Results: We propose DiviK: a scalable stepwise algorithm with local data-driven feature space adaptation for segmenting high-dimensional datasets. The algorithm is compared to the optional solutions (regular k-means, spatial and spectral approaches) combined with different feature engineering techniques (None, PCA, EXIMS, UMAP, Neural Ions). Three quality indices: Dice Index, Rand Index and EXIMS score, focusing on the overall composition of the clustering, coverage of the tumour region and spatial cluster consistency, are used to assess the quality of unsupervised analyses. Algorithms were validated on mass spectrometry imaging (MSI) datasets—2D human cancer tissue samples and 3D mouse kidney images. DiviK algorithm performed the best among the four clustering algorithms compared (overall quality score 1.24, 0.58 and 162 for d(0, 0, 0), d(1, 1, 1) and the sum of ranks, respectively), with spectral clustering being mostly second. Feature engineering techniques impact the overall clustering results less than the algorithms themselves (partial η2 effect size: 0.141 versus 0.345, Kendall’s concordance index: 0.424 versus 0.138 for d(0, 0, 0)). Conclusions: DiviK could be the default choice in the exploration of MSI data. Thanks to its unique, GMM-based local optimisation of the feature space and deglomerative schema, DiviK results do not strongly depend on the feature engineering technique applied and can reveal the hidden structure in a tissue sample. Additionally, DiviK shows high scalability, and it can process at once the big omics data with more than 1.5 mln instances and a few thousand features. Finally, due to its simplicity, DiviK is easily generalisable to an even more flexible framework. Therefore, it is helpful for other -omics data (as single cell spatial transcriptomic) or tabular data in general (including medical images after appropriate embedding). A generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik.",
+                "authors": [
+                    {
+                        "name": "Mrukwa G."
+                    },
+                    {
+                        "name": "Polanska J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data"
+            },
+            "pmcid": "PMC9743550",
+            "pmid": "36503372"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/dla-ranker/dla-ranker.biotools.json b/data/dla-ranker/dla-ranker.biotools.json
new file mode 100644
index 0000000000000..f15911196a7c6
--- /dev/null
+++ b/data/dla-ranker/dla-ranker.biotools.json
@@ -0,0 +1,117 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-18T23:41:33.879010Z",
+    "biotoolsCURIE": "biotools:dla-ranker",
+    "biotoolsID": "dla-ranker",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "alessandra.carbone@sorbonne-universite.fr",
+            "name": "Alessandra Carbone",
+            "orcidid": "http://orcid.org/0000-0003-2098-5743",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "elodie.laine@sorbonne-universite.fr",
+            "name": "Elodie Laine",
+            "orcidid": "http://orcid.org/0000-0003-4870-6304",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Simon Crouzet",
+            "orcidid": "http://orcid.org/0000-0002-5012-4621"
+        },
+        {
+            "name": "Yasser Mohseni Behbahani",
+            "orcidid": "http://orcid.org/0000-0003-0254-6595"
+        }
+    ],
+    "description": "Deep Local Analysis evaluates protein docking conformations with Locally oriented Cubes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                },
+                {
+                    "term": "Protein structure prediction",
+                    "uri": "http://edamontology.org/operation_0474"
+                },
+                {
+                    "term": "Protein-protein docking",
+                    "uri": "http://edamontology.org/operation_3899"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://gitlab.lcqb.upmc.fr/dla-ranker/DLA-Ranker.git",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-18T23:41:33.881381Z",
+    "license": "Not licensed",
+    "name": "DLA-Ranker",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac551",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: With the recent advances in protein 3D structure prediction, protein interactions are becoming more central than ever before. Here, we address the problem of determining how proteins interact with one another. More specifically, we investigate the possibility of discriminating near-native protein complex conformations from incorrect ones by exploiting local environments around interfacial residues. RESULTS: Deep Local Analysis (DLA)-Ranker is a deep learning framework applying 3D convolutions to a set of locally oriented cubes representing the protein interface. It explicitly considers the local geometry of the interfacial residues along with their neighboring atoms and the regions of the interface with different solvent accessibility. We assessed its performance on three docking benchmarks made of half a million acceptable and incorrect conformations. We show that DLA-Ranker successfully identifies near-native conformations from ensembles generated by molecular docking. It surpasses or competes with other deep learning-based scoring functions. We also showcase its usefulness to discover alternative interfaces. AVAILABILITY AND IMPLEMENTATION: http://gitlab.lcqb.upmc.fr/dla-ranker/DLA-Ranker.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Carbone A."
+                    },
+                    {
+                        "name": "Crouzet S."
+                    },
+                    {
+                        "name": "Laine E."
+                    },
+                    {
+                        "name": "Mohseni Behbahani Y."
+                    }
+                ],
+                "date": "2022-09-30T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Deep Local Analysis evaluates protein docking conformations with locally oriented cubes"
+            },
+            "pmcid": "PMC9525006",
+            "pmid": "35962985"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein structural motifs and surfaces",
+            "uri": "http://edamontology.org/topic_0166"
+        },
+        {
+            "term": "Protein targeting and localisation",
+            "uri": "http://edamontology.org/topic_0140"
+        }
+    ]
+}
diff --git a/data/dloopcaller/dloopcaller.biotools.json b/data/dloopcaller/dloopcaller.biotools.json
new file mode 100644
index 0000000000000..63c739379146b
--- /dev/null
+++ b/data/dloopcaller/dloopcaller.biotools.json
@@ -0,0 +1,125 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T23:10:22.353243Z",
+    "biotoolsCURIE": "biotools:dloopcaller",
+    "biotoolsID": "dloopcaller",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dshuang@eias.ac.cn",
+            "name": "De-Shuang Huang",
+            "orcidid": "https://orcid.org/0000-0002-6759-2691",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Kyungsook Han",
+            "orcidid": "https://orcid.org/0000-0001-9900-6741"
+        },
+        {
+            "name": "Siguo Wang",
+            "orcidid": "https://orcid.org/0000-0002-3244-3629"
+        },
+        {
+            "name": "Qinhu Zhang",
+            "orcidid": "https://orcid.org/0000-0002-4232-7736",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Loop modelling",
+                    "uri": "http://edamontology.org/operation_0481"
+                },
+                {
+                    "term": "Promoter prediction",
+                    "uri": "http://edamontology.org/operation_0440"
+                },
+                {
+                    "term": "Residue contact prediction",
+                    "uri": "http://edamontology.org/operation_0272"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/wangguoguoa/DLoopCaller",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T23:10:22.357322Z",
+    "license": "Not licensed",
+    "name": "DLoopCaller",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PCBI.1010572",
+            "metadata": {
+                "abstract": "Copyright: © 2022 Wang et al.In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide contact interactions is crucial for dissecting three-dimensional(3D) genome structure and function. Here, we present a deep learning method to predict genome-wide chromatin loops, called DLoopCaller, by combining accessible chromatin landscapes and raw Hi-C contact maps. Some available orthogonal data ChIA-PET/HiChIP and Capture Hi-C were used to generate positive samples with a wider contact matrix which provides the possibility to find more potential genome-wide chromatin loops. The experimental results demonstrate that DLoopCaller effectively improves the accuracy of predicting genome-wide chromatin loops compared to the state-of-the-art method Peakachu. Moreover, compared to two of most popular loop callers, such as HiCCUPS and Fit-Hi-C, DLoopCaller identifies some unique interactions. We conclude that a combination of chromatin landscapes on the one-dimensional genome contributes to understanding the 3D genome organization, and the identified chromatin loops reveal cell-type specificity and transcription factor motif co-enrichment across different cell lines and species.",
+                "authors": [
+                    {
+                        "name": "Cui Z."
+                    },
+                    {
+                        "name": "Guo Z."
+                    },
+                    {
+                        "name": "Han K."
+                    },
+                    {
+                        "name": "He Y."
+                    },
+                    {
+                        "name": "Huang D.-S."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Zhang Q."
+                    }
+                ],
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes"
+            },
+            "pmcid": "PMC9581407",
+            "pmid": "36206320"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Chromosome conformation capture",
+            "uri": "http://edamontology.org/topic_3940"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Tomography",
+            "uri": "http://edamontology.org/topic_3452"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/dmfpred/dmfpred.biotools.json b/data/dmfpred/dmfpred.biotools.json
new file mode 100644
index 0000000000000..4adc36af2f9f9
--- /dev/null
+++ b/data/dmfpred/dmfpred.biotools.json
@@ -0,0 +1,102 @@
+{
+    "additionDate": "2023-01-28T10:48:27.016998Z",
+    "biotoolsCURIE": "biotools:dmfpred",
+    "biotoolsID": "dmfpred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "bliu@bliulab.net",
+            "name": "Bin Liu",
+            "orcidid": "https://orcid.org/0000-0001-6314-0762",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Predicting protein disorder molecular functions based on protein cubic language model.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein disorder prediction",
+                    "uri": "http://edamontology.org/operation_3904"
+                },
+                {
+                    "term": "Protein function prediction",
+                    "uri": "http://edamontology.org/operation_1777"
+                },
+                {
+                    "term": "Protein structure validation",
+                    "uri": "http://edamontology.org/operation_0321"
+                },
+                {
+                    "term": "Sequence motif recognition",
+                    "uri": "http://edamontology.org/operation_0239"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bliulab.net/DMFpred/",
+    "lastUpdate": "2023-01-28T10:48:27.019761Z",
+    "license": "Not licensed",
+    "name": "DMFpred",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PCBI.1010668",
+            "metadata": {
+                "abstract": "© 2022 Pang, Liu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Intrinsically disordered proteins and regions (IDP/IDRs) are widespread in living organisms and perform various essential molecular functions. These functions are summarized as six general categories, including entropic chain, assembler, scavenger, effector, display site, and chaperone. The alteration of IDP functions is responsible for many human diseases. Therefore, identifying the function of disordered proteins is helpful for the studies of drug target discovery and rational drug design. Experimental identification of the molecular functions of IDP in the wet lab is an expensive and laborious procedure that is not applicable on a large scale. Some computational methods have been proposed and mainly focus on predicting the entropic chain function of IDRs, while the computational predictive methods for the remaining five important categories of disordered molecular functions are desired. Motivated by the growing numbers of experimental annotated functional sequences and the need to expand the coverage of disordered protein function predictors, we proposed DMFpred for disordered molecular functions prediction, covering disordered assembler, scavenger, effector, display site and chaperone. DMFpred employs the Protein Cubic Language Model (PCLM), which incorporates three protein language models for characterizing sequences, structural and functional features of proteins, and attention-based alignment for understanding the relationship among three captured features and generating a joint representation of proteins. The PCLM was pre-trained with large-scaled IDR sequences and finetuned with functional annotation sequences for molecular function prediction. The predictive performance evaluation on five categories of functional and multi-functional residues suggested that DMFpred provides high-quality predictions. The web-server of DMFpred can be freely accessed from http://bliulab.net/DMFpred/. Copyright:",
+                "authors": [
+                    {
+                        "name": "Liu B."
+                    },
+                    {
+                        "name": "Pang Y."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "DMFpred: Predicting protein disorder molecular functions based on protein cubic language model"
+            },
+            "pmcid": "PMC9674156",
+            "pmid": "36315580"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Protein disordered structure",
+            "uri": "http://edamontology.org/topic_3538"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ]
+}
diff --git a/data/dmgn/dmgn.biotools.json b/data/dmgn/dmgn.biotools.json
new file mode 100644
index 0000000000000..8e59487145067
--- /dev/null
+++ b/data/dmgn/dmgn.biotools.json
@@ -0,0 +1,109 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-28T13:52:31.570468Z",
+    "biotoolsCURIE": "biotools:dmgn",
+    "biotoolsID": "dmgn",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "helenxhfu@hotmail.com",
+            "name": "Xiaohang Fu",
+            "orcidid": "http://orcid.org/0000-0003-2101-1326",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ellis Patrick",
+            "orcidid": "http://orcid.org/0000-0002-5253-4747"
+        },
+        {
+            "name": "Jean Y. H. Yang",
+            "orcidid": "http://orcid.org/0000-0002-5271-2603"
+        },
+        {
+            "name": "Jinman Kim",
+            "orcidid": "http://orcid.org/0000-0001-5960-1060"
+        }
+    ],
+    "description": "Deep Multimodal Graph-Based Network for Survival Prediction from Highly Multiplexed Images and Patient Variables.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Incident curve plotting",
+                    "uri": "http://edamontology.org/operation_3503"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/xhelenfu/DMGN_Survival_Prediction",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-28T13:52:31.573253Z",
+    "license": "MIT",
+    "name": "DMGN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.compbiomed.2023.106576",
+            "metadata": {
+                "abstract": "© 2023 The Author(s)The spatial architecture of the tumour microenvironment and phenotypic heterogeneity of tumour cells have been shown to be associated with cancer prognosis and clinical outcomes, including survival. Recent advances in highly multiplexed imaging, including imaging mass cytometry (IMC), capture spatially resolved, high-dimensional maps that quantify dozens of disease-relevant biomarkers at single-cell resolution, that contain potential to inform patient-specific prognosis. Existing automated methods for predicting survival, on the other hand, typically do not leverage spatial phenotype information captured at the single-cell level. Furthermore, there is no end-to-end method designed to leverage the rich information in whole IMC images and all marker channels, and aggregate this information with clinical data in a complementary manner to predict survival with enhanced accuracy. To that end, we present a deep multimodal graph-based network (DMGN) with two modules: (1) a multimodal graph-based module that considers relationships between spatial phenotype information in all image regions and all clinical variables adaptively, and (2) a clinical embedding module that automatically generates embeddings specialised for each clinical variable to enhance multimodal aggregation. We demonstrate that our modules are consistently effective at improving survival prediction performance using two public breast cancer datasets, and that our new approach can outperform state-of-the-art methods in survival prediction.",
+                "authors": [
+                    {
+                        "name": "Feng D.D."
+                    },
+                    {
+                        "name": "Fu X."
+                    },
+                    {
+                        "name": "Kim J."
+                    },
+                    {
+                        "name": "Patrick E."
+                    },
+                    {
+                        "name": "Yang J.Y.H."
+                    }
+                ],
+                "date": "2023-03-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "Deep multimodal graph-based network for survival prediction from highly multiplexed images and patient variables"
+            },
+            "pmid": "36736097"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Cytometry",
+            "uri": "http://edamontology.org/topic_3934"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        }
+    ]
+}
diff --git a/data/dmiso/dmiso.biotools.json b/data/dmiso/dmiso.biotools.json
new file mode 100644
index 0000000000000..bd047a19797c7
--- /dev/null
+++ b/data/dmiso/dmiso.biotools.json
@@ -0,0 +1,120 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-18T23:52:58.666563Z",
+    "biotoolsCURIE": "biotools:dmiso",
+    "biotoolsID": "dmiso",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xiaoman@mail.ucf.edu",
+            "name": "Xiaoman Li",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Amlan Talukder"
+        },
+        {
+            "name": "Haiyan Hu"
+        },
+        {
+            "name": "Wencai Zhang"
+        }
+    ],
+    "description": "A Deep Learning Method for MiRNA/IsomiR Target Detection.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "http://hulab.ucf.edu/research/projects/DMISO/README.md"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Chimera detection",
+                    "uri": "http://edamontology.org/operation_0450"
+                },
+                {
+                    "term": "miRNA expression analysis",
+                    "uri": "http://edamontology.org/operation_3792"
+                },
+                {
+                    "term": "miRNA target prediction",
+                    "uri": "http://edamontology.org/operation_0463"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://hulab.ucf.edu/research/projects/DMISO",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-18T23:52:58.669177Z",
+    "license": "Not licensed",
+    "name": "DMISO",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/s41598-022-14890-8",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Accurate identification of microRNA (miRNA) targets at base-pair resolution has been an open problem for over a decade. The recent discovery of miRNA isoforms (isomiRs) adds more complexity to this problem. Despite the existence of many methods, none considers isomiRs, and their performance is still suboptimal. We hypothesize that by taking the isomiR–mRNA interactions into account and applying a deep learning model to study miRNA–mRNA interaction features, we may improve the accuracy of miRNA target predictions. We developed a deep learning tool called DMISO to capture the intricate features of miRNA/isomiR–mRNA interactions. Based on tenfold cross-validation, DMISO showed high precision (95%) and recall (90%). Evaluated on three independent datasets, DMISO had superior performance to five tools, including three popular conventional tools and two recently developed deep learning-based tools. By applying two popular feature interpretation strategies, we demonstrated the importance of the miRNA regions other than their seeds and the potential contribution of the RNA-binding motifs within miRNAs/isomiRs and mRNAs to the miRNA/isomiR–mRNA interactions.",
+                "authors": [
+                    {
+                        "name": "Hu H."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Talukder A."
+                    },
+                    {
+                        "name": "Zhang W."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "A deep learning method for miRNA/isomiR target detection"
+            },
+            "pmcid": "PMC9226005",
+            "pmid": "35739186"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ]
+}
diff --git a/data/dmppred/dmppred.biotools.json b/data/dmppred/dmppred.biotools.json
index e43afa4801472..1c471be5cd23c 100644
--- a/data/dmppred/dmppred.biotools.json
+++ b/data/dmppred/dmppred.biotools.json
@@ -1,22 +1,38 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2022-10-03T05:03:46.539729Z",
     "biotoolsCURIE": "biotools:dmppred",
     "biotoolsID": "dmppred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "credit": [
+        {
+            "name": "Anjali Dhall",
+            "orcidid": "https://orcid.org/0000-0002-0400-2084"
+        },
+        {
+            "name": "Nishant Kumar",
+            "orcidid": "https://orcid.org/0000-0001-7781-9602"
+        },
+        {
+            "name": "Ritu Tomer",
+            "orcidid": "https://orcid.org/0000-0002-6171-8660"
+        },
+        {
+            "name": "Shubham Choudhury",
+            "orcidid": "https://orcid.org/0000-0002-4509-4683"
+        },
+        {
+            "name": "Sumeet Patiyal",
+            "orcidid": "https://orcid.org/0000-0003-1358-292X"
+        },
         {
             "name": "Dr Gajendra P.S. Raghava",
+            "orcidid": "https://orcid.org/0000-0002-8902-2876",
             "url": "https://webs.iiitd.edu.in/raghava/dmppred/index.php"
         }
     ],
     "description": "Dmppred: Prediction of T1DM associated peptides",
-    "documentation": [
-        {
-            "type": [
-                "General"
-            ],
-            "url": "https://webs.iiitd.edu.in/raghava/dmppred/index.php"
-        }
-    ],
     "editPermission": {
         "type": "private"
     },
@@ -24,22 +40,22 @@
         {
             "operation": [
                 {
-                    "term": "Analysis",
-                    "uri": "http://edamontology.org/operation_2945"
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                },
+                {
+                    "term": "Protein signal peptide detection",
+                    "uri": "http://edamontology.org/operation_0418"
                 }
             ]
         }
     ],
     "homepage": "https://webs.iiitd.edu.in/raghava/dmppred/index.php",
-    "lastUpdate": "2022-10-03T05:04:17.470992Z",
-    "link": [
-        {
-            "type": [
-                "Software catalogue"
-            ],
-            "url": "https://webs.iiitd.edu.in/raghava/dmppred/index.php"
-        }
-    ],
+    "lastUpdate": "2023-02-28T13:47:50.337562Z",
     "name": "dmppred",
     "operatingSystem": [
         "Linux",
@@ -47,6 +63,12 @@
         "Windows"
     ],
     "owner": "raghavagps",
+    "publication": [
+        {
+            "doi": "10.1093/bib/bbac525",
+            "pmid": "36524996"
+        }
+    ],
     "toolType": [
         "Web application"
     ],
diff --git a/data/dna-mp/dna-mp.biotools.json b/data/dna-mp/dna-mp.biotools.json
new file mode 100644
index 0000000000000..71cadca08712a
--- /dev/null
+++ b/data/dna-mp/dna-mp.biotools.json
@@ -0,0 +1,103 @@
+{
+    "additionDate": "2023-02-19T11:58:55.306792Z",
+    "biotoolsCURIE": "biotools:dna-mp",
+    "biotoolsID": "dna-mp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Muhammad_Nabeel.Asim@dfki.de",
+            "name": "Muhammad Nabeel Asim",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A generalized DNA modifications predictor for multiple species based on powerful sequence encoding method.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://sds_genetic_analysis.opendfki.de/DNA_Modifications/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-19T11:58:55.309436Z",
+    "license": "Other",
+    "name": "DNA-MP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC546",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable of detecting three different types of DNA modifications; however, type-specific and generalized modifications predictors produce limited performance across multiple species mainly due to the use of ineffective sequence encoding methods. The paper in hand presents a generalized computational approach \"DNA-MP\" that is competent to more precisely predict three different DNA modifications across multiple species. Proposed DNA-MP approach makes use of a powerful encoding method \"position specific nucleotides occurrence based 117 on modification and non-modification class densities normalized difference\" (POCD-ND) to generate the statistical representations of DNA sequences and a deep forest classifier for modifications prediction. POCD-ND encoder generates statistical representations by extracting position specific distributional information of nucleotides in the DNA sequences. We perform a comprehensive intrinsic and extrinsic evaluation of the proposed encoder and compare its performance with 32 most widely used encoding methods on $17$ benchmark DNA modifications prediction datasets of $12$ different species using $10$ different machine learning classifiers. Overall, with all classifiers, the proposed POCD-ND encoder outperforms existing $32$ different encoders. Furthermore, combinedly over 5-fold cross validation benchmark datasets and independent test sets, proposed DNA-MP predictor outperforms state-of-the-art type-specific and generalized modifications predictors by an average accuracy of 7% across 4mc datasets, 1.35% across 5hmc datasets and 10% for 6ma datasets. To facilitate the scientific community, the DNA-MP web application is available at https://sds_genetic_analysis.opendfki.de/DNA_Modifications/.",
+                "authors": [
+                    {
+                        "name": "Ahmed S."
+                    },
+                    {
+                        "name": "Ali Ibrahim M."
+                    },
+                    {
+                        "name": "Dengel A."
+                    },
+                    {
+                        "name": "Fazeel A."
+                    },
+                    {
+                        "name": "Nabeel Asim M."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method"
+            },
+            "pmid": "36528802"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/dnadna/dnadna.biotools.json b/data/dnadna/dnadna.biotools.json
new file mode 100644
index 0000000000000..a25c5d37ad02d
--- /dev/null
+++ b/data/dnadna/dnadna.biotools.json
@@ -0,0 +1,147 @@
+{
+    "additionDate": "2023-01-28T10:53:22.677732Z",
+    "biotoolsCURIE": "biotools:dnadna",
+    "biotoolsID": "dnadna",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "flora.jay@lri.fr",
+            "name": "Flora Jay",
+            "orcidid": "https://orcid.org/0000-0001-5884-4730",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "jean.cury@normalesup.org",
+            "name": "Jean Cury",
+            "orcidid": "https://orcid.org/0000-0002-6462-8783",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A deep learning framework for population genetics inference.",
+    "documentation": [
+        {
+            "type": [
+                "API documentation"
+            ],
+            "url": "http://mlgenetics.gitlab.io/dnadna/api.html"
+        },
+        {
+            "type": [
+                "Training material"
+            ],
+            "url": "http://mlgenetics.gitlab.io/dnadna/tutorials.html"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Document clustering",
+                    "uri": "http://edamontology.org/operation_3279"
+                },
+                {
+                    "term": "Information extraction",
+                    "uri": "http://edamontology.org/operation_3907"
+                },
+                {
+                    "term": "Nucleic acid melting curve plotting",
+                    "uri": "http://edamontology.org/operation_0458"
+                },
+                {
+                    "term": "Nucleic acid stitch profile plotting",
+                    "uri": "http://edamontology.org/operation_0457"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://mlgenetics.gitlab.io/dnadna/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T10:53:22.682283Z",
+    "license": "CECILL-C",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "http://gitlab.com/mlgenetics/dnadna"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "http://pypi.org/project/dnadna/"
+        }
+    ],
+    "name": "dnadna",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC765",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: We present dnadna, a flexible python-based software for deep learning inference in population genetics. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination and re-usability of neural networks designed for population genetic data. RESULTS: dnadna defines multiple user-friendly workflows. First, users can implement new architectures and tasks, while benefiting from dnadna utility functions, training procedure and test environment, which saves time and decreases the likelihood of bugs. Second, the implemented networks can be re-optimized based on user-specified training sets and/or tasks. Newly implemented architectures and pre-trained networks are easily shareable with the community for further benchmarking or other applications. Finally, users can apply pre-trained networks in order to predict evolutionary history from alternative real or simulated genetic datasets, without requiring extensive knowledge in deep learning or coding in general. dnadna comes with a peer-reviewed, exchangeable neural network, allowing demographic inference from SNP data, that can be used directly or retrained to solve other tasks. Toy networks are also available to ease the exploration of the software, and we expect that the range of available architectures will keep expanding thanks to community contributions. AVAILABILITY AND IMPLEMENTATION: dnadna is a Python (≥3.7) package, its repository is available at gitlab.com/mlgenetics/dnadna and its associated documentation at mlgenetics.gitlab.io/dnadna/.",
+                "authors": [
+                    {
+                        "name": "Bray E.M."
+                    },
+                    {
+                        "name": "Charpiat G."
+                    },
+                    {
+                        "name": "Cury J."
+                    },
+                    {
+                        "name": "Guez J."
+                    },
+                    {
+                        "name": "Jay F."
+                    },
+                    {
+                        "name": "Jobic P."
+                    },
+                    {
+                        "name": "Letournel A.-C."
+                    },
+                    {
+                        "name": "Sanchez T."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "dnadna: a deep learning framework for population genetics inference"
+            },
+            "pmcid": "PMC9825738",
+            "pmid": "36445000"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Population genetics",
+            "uri": "http://edamontology.org/topic_3056"
+        }
+    ]
+}
diff --git a/data/dnasmart/dnasmart.biotools.json b/data/dnasmart/dnasmart.biotools.json
new file mode 100644
index 0000000000000..fd7b8013023ed
--- /dev/null
+++ b/data/dnasmart/dnasmart.biotools.json
@@ -0,0 +1,101 @@
+{
+    "additionDate": "2023-03-18T09:01:16.867286Z",
+    "biotoolsCURIE": "biotools:dnasmart",
+    "biotoolsID": "dnasmart",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chisom.ezekannagha@uni-marburg.de",
+            "name": "Chisom Ezekannagha",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Multiple attribute ranking tool for DNA data storage systems.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "DNA barcoding",
+                    "uri": "http://edamontology.org/operation_3200"
+                },
+                {
+                    "term": "Feature selection",
+                    "uri": "http://edamontology.org/operation_3936"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://dnasmart.mathematik.uni-marburg.de/",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-03-18T09:01:16.871668Z",
+    "license": "BSD-3-Clause",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/sombiri/DNAsmart"
+        }
+    ],
+    "name": "DNAsmart",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2023.02.016",
+            "metadata": {
+                "abstract": "In an ever-growing need for data storage capacity, the Deoxyribonucleic Acid (DNA) molecule gains traction as a new storage medium with a larger capacity, higher density, and a longer lifespan over conventional storage media. To effectively use DNA for data storage, it is important to understand the different methods of encoding information in DNA and compare their effectiveness. This requires evaluating which decoded DNA sequences carry the most encoded information based on various attributes. However, navigating the field of coding theory requires years of experience and domain expertise. For instance, domain experts rely on various mathematical functions and attributes to score and evaluate their encodings. To enable such analytical tasks, we provide an interactive and visual analytical framework for multi-attribute ranking in DNA storage systems. Our framework follows a three-step view with user-settable parameters. It enables users to find the optimal en-/de-coding approaches by setting different weights and combining multiple attributes. We assess the validity of our work through a task-specific user study on domain experts by relying on three tasks. Results indicate that all participants completed their tasks successfully under two minutes, then rated the framework for design choices, perceived usefulness, and intuitiveness. In addition, two real-world use cases are shared and analyzed as direct applications of the proposed tool. DNAsmart enables the ranking of decoded sequences based on multiple attributes. In sum, this work unveils the evaluation of en-/de-coding approaches accessible and tractable through visualization and interactivity to solve comparison and ranking tasks.",
+                "authors": [
+                    {
+                        "name": "Ezekannagha C."
+                    },
+                    {
+                        "name": "Hattab G."
+                    },
+                    {
+                        "name": "Heider D."
+                    },
+                    {
+                        "name": "Welzel M."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "DNAsmart: Multiple attribute ranking tool for DNA data storage systems"
+            },
+            "pmcid": "PMC9957737",
+            "pmid": "36851917"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/dockey/dockey.biotools.json b/data/dockey/dockey.biotools.json
new file mode 100644
index 0000000000000..845f793aae207
--- /dev/null
+++ b/data/dockey/dockey.biotools.json
@@ -0,0 +1,89 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T09:05:19.317528Z",
+    "biotoolsCURIE": "biotools:dockey",
+    "biotoolsID": "dockey",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Kelei Zhao"
+        }
+    ],
+    "description": "An integrated graphic user interface tool for molecular docking and virtual screening.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://dockey.readthedocs.io/en/latest/"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://github.com/lmdu/dockey/releases"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/lmdu/dockey",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T09:05:19.321544Z",
+    "license": "MIT",
+    "name": "Dockey",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAD047",
+            "pmid": "36764832"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/dockground/dockground.biotools.json b/data/dockground/dockground.biotools.json
new file mode 100644
index 0000000000000..478c3241e54e5
--- /dev/null
+++ b/data/dockground/dockground.biotools.json
@@ -0,0 +1,109 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T22:59:50.815808Z",
+    "biotoolsCURIE": "biotools:dockground",
+    "biotoolsID": "dockground",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "pkundro@ku.edu",
+            "name": "Petras J. Kundrotas"
+        },
+        {
+            "email": "vakser@ku.edu",
+            "name": "Ilya A. Vakser",
+            "orcidid": "https://orcid.org/0000-0002-5743-2934"
+        },
+        {
+            "name": "Keeley W. Collins"
+        },
+        {
+            "name": "Matthew M. Copeland"
+        }
+    ],
+    "description": "DOCKGROUND resource for protein recognition studies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                },
+                {
+                    "term": "Protein-protein docking",
+                    "uri": "http://edamontology.org/operation_3899"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://dockground.compbio.ku.edu",
+    "language": [
+        "SQL"
+    ],
+    "lastUpdate": "2023-01-09T22:59:50.818815Z",
+    "name": "Dockground",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/PRO.4481",
+            "metadata": {
+                "abstract": "© 2022 The Protein Society.Structural information of protein–protein interactions is essential for characterization of life processes at the molecular level. While a small fraction of known protein interactions has experimentally determined structures, computational modeling of protein complexes (protein docking) has to fill the gap. The Dockground resource (http://dockground.compbio.ku.edu) provides a collection of datasets for the development and testing of protein docking techniques. Currently, Dockground contains datasets for the bound and the unbound (experimentally determined and simulated) protein structures, model–model complexes, docking decoys of experimentally determined and modeled proteins, and templates for comparative docking. The Dockground bound proteins dataset is a core set, from which other Dockground datasets are generated. It is devised as a relational PostgreSQL database containing information on experimentally determined protein–protein complexes. This report on the Dockground resource describes current status of the datasets, new automated update procedures and further development of the core datasets. We also present a new Dockground interactive web interface, which allows search by various parameters, such as release date, multimeric state, complex type, structure resolution, and so on, visualization of the search results with a number of customizable parameters, as well as downloadable datasets with predefined levels of sequence and structure redundancy.",
+                "authors": [
+                    {
+                        "name": "Collins K.W."
+                    },
+                    {
+                        "name": "Copeland M.M."
+                    },
+                    {
+                        "name": "Kotthoff I."
+                    },
+                    {
+                        "name": "Kundrotas P.J."
+                    },
+                    {
+                        "name": "Singh A."
+                    },
+                    {
+                        "name": "Vakser I.A."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Protein Science",
+                "title": "Dockground resource for protein recognition studies"
+            },
+            "pmcid": "PMC9667896",
+            "pmid": "36281025"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein structure analysis",
+            "uri": "http://edamontology.org/topic_2814"
+        }
+    ]
+}
diff --git a/data/docknet/docknet.biotools.json b/data/docknet/docknet.biotools.json
new file mode 100644
index 0000000000000..5d3eb5cb45979
--- /dev/null
+++ b/data/docknet/docknet.biotools.json
@@ -0,0 +1,117 @@
+{
+    "additionDate": "2023-02-19T12:01:45.987684Z",
+    "biotoolsCURIE": "biotools:docknet",
+    "biotoolsID": "docknet",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jessica.holien@rmit.edu.au",
+            "name": "Jessica K Holien",
+            "orcidid": "https://orcid.org/0000-0002-8735-2871",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An efficient Siamese graph-based neural network method which predicts contact residues between two interacting proteins.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular docking",
+                    "uri": "http://edamontology.org/operation_0478"
+                },
+                {
+                    "term": "Residue contact prediction",
+                    "uri": "http://edamontology.org/operation_0272"
+                },
+                {
+                    "term": "Simulation analysis",
+                    "uri": "http://edamontology.org/operation_0244"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://biosig.lab.uq.edu.au/docknet",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-19T12:01:45.990282Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/npwilliams09/docknet"
+        }
+    ],
+    "name": "DockNet",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC797",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Over 300 000 protein-protein interaction (PPI) pairs have been identified in the human proteome and targeting these is fast becoming the next frontier in drug design. Predicting PPI sites, however, is a challenging task that traditionally requires computationally expensive and time-consuming docking simulations. A major weakness of modern protein docking algorithms is the inability to account for protein flexibility, which ultimately leads to relatively poor results. RESULTS: Here, we propose DockNet, an efficient Siamese graph-based neural network method which predicts contact residues between two interacting proteins. Unlike other methods that only utilize a protein's surface or treat the protein structure as a rigid body, DockNet incorporates the entire protein structure and places no limits on protein flexibility during an interaction. Predictions are modeled at the residue level, based on a diverse set of input node features including residue type, surface accessibility, residue depth, secondary structure, pharmacophore and torsional angles. DockNet is comparable to current state-of-the-art methods, achieving an area under the curve (AUC) value of up to 0.84 on an independent test set (DB5), can be applied to a variety of different protein structures and can be utilized in situations where accurate unbound protein structures cannot be obtained. AVAILABILITY AND IMPLEMENTATION: DockNet is available at https://github.com/npwilliams09/docknet and an easy-to-use webserver at https://biosig.lab.uq.edu.au/docknet. All other data underlying this article are available in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Ascher D.B."
+                    },
+                    {
+                        "name": "Holien J.K."
+                    },
+                    {
+                        "name": "Rodrigues C.H.M."
+                    },
+                    {
+                        "name": "Truong J."
+                    },
+                    {
+                        "name": "Williams N.P."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "DockNet: high-throughput protein-protein interface contact prediction"
+            },
+            "pmcid": "PMC9825772",
+            "pmid": "36484688"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein structural motifs and surfaces",
+            "uri": "http://edamontology.org/topic_0166"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
+}
diff --git a/data/domainmapper/domainmapper.biotools.json b/data/domainmapper/domainmapper.biotools.json
new file mode 100644
index 0000000000000..1024e8ddaff23
--- /dev/null
+++ b/data/domainmapper/domainmapper.biotools.json
@@ -0,0 +1,99 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T22:53:42.593550Z",
+    "biotoolsCURIE": "biotools:domainmapper",
+    "biotoolsID": "domainmapper",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "emanriq1@jhu.edu",
+            "name": "Edgar Manriquez‐Sandoval",
+            "orcidid": "https://orcid.org/0000-0001-7284-1237",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "sdfried@jhu.edu",
+            "name": "Stephen D. Fried",
+            "orcidid": "https://orcid.org/0000-0003-2494-2193",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Accurate domain structure annotation including those with non-contiguous topologies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/FriedLabJHU/DomainMapper",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T22:53:42.596610Z",
+    "license": "Apache-2.0",
+    "name": "DomainMapper",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/PRO.4465",
+            "metadata": {
+                "abstract": "© 2022 The Protein Society.Automated domain annotation is an important tool for structural informatics. These pipelines typically involve searching query sequences against hidden Markov model (HMM) profiles, yielding matches to profiles for various domains. However, domain annotation can be ambiguous or inaccurate when proteins contain domains with non-contiguous residue ranges, and especially when insertional domains are hosted within them. Here, we present DomainMapper, an algorithm that accurately assigns a unique domain structure annotation to a query sequence, including those with complex topologies. We validate our domain assignments using the AlphaFold database and confirm that non-contiguity is pervasive (10.74% of all domains in yeast and 4.52% in human). Using this resource, we find that certain folds have strong propensities to be non-contiguous or insertional across the Tree of Life. DomainMapper is freely available and can be ran as a single command-line function.",
+                "authors": [
+                    {
+                        "name": "Fried S.D."
+                    },
+                    {
+                        "name": "Manriquez-Sandoval E."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Protein Science",
+                "title": "DomainMapper: Accurate domain structure annotation including those with non-contiguous topologies"
+            },
+            "pmcid": "PMC9601794",
+            "pmid": "36208126"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cladistics",
+            "uri": "http://edamontology.org/topic_3944"
+        },
+        {
+            "term": "Informatics",
+            "uri": "http://edamontology.org/topic_0605"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ]
+}
diff --git a/data/doric_12.0/doric_12.0.biotools.json b/data/doric_12.0/doric_12.0.biotools.json
new file mode 100644
index 0000000000000..3ef5eff508634
--- /dev/null
+++ b/data/doric_12.0/doric_12.0.biotools.json
@@ -0,0 +1,85 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T22:47:22.737262Z",
+    "biotoolsCURIE": "biotools:doric_12.0",
+    "biotoolsID": "doric_12.0",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "fgao@tju.edu.cn",
+            "name": "Feng Gao",
+            "orcidid": "https://orcid.org/0000-0002-9563-3841",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Mei-Jing Dong"
+        },
+        {
+            "name": "Hao Luo",
+            "orcidid": "https://orcid.org/0000-0003-2714-8817"
+        }
+    ],
+    "description": "An updated database of replication origins in both complete and draft prokaryotic genomes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://tubic.org/doric/",
+    "lastUpdate": "2023-01-09T22:47:22.741531Z",
+    "name": "DoriC 12.0",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC964",
+            "pmid": "36305822"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ]
+}
diff --git a/data/dragon/dragon.biotools.json b/data/dragon/dragon.biotools.json
new file mode 100644
index 0000000000000..c7e5c99139474
--- /dev/null
+++ b/data/dragon/dragon.biotools.json
@@ -0,0 +1,81 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T16:41:29.437001Z",
+    "biotoolsCURIE": "biotools:dragon",
+    "biotoolsID": "dragon",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "michael.altenbuchinger@bioinf.med.uni-goettingen.de",
+            "name": "Michael Altenbuchinger",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "DRAGON (Determining Regulatory Associations using Graphical models on multi-Omic Networks), calibrates its parameters to achieve an optimal trade-off between the network’s complexity and estimation accuracy, while explicitly accounting for the characteristics of each of the assessed omics ‘layers.’",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene methylation analysis",
+                    "uri": "http://edamontology.org/operation_3207"
+                },
+                {
+                    "term": "Gene regulatory network analysis",
+                    "uri": "http://edamontology.org/operation_1781"
+                },
+                {
+                    "term": "Pathway analysis",
+                    "uri": "http://edamontology.org/operation_3928"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/katehoffshutta/DRAGON-TCGA-BRCA",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-02-20T16:41:29.439586Z",
+    "license": "Not licensed",
+    "name": "DRAGON",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1157",
+            "pmid": "36533448"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/draw/draw.biotools.json b/data/draw/draw.biotools.json
new file mode 100644
index 0000000000000..f95ae422988cd
--- /dev/null
+++ b/data/draw/draw.biotools.json
@@ -0,0 +1,100 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T09:09:17.367328Z",
+    "biotoolsCURIE": "biotools:draw",
+    "biotoolsID": "draw",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mohsen.hooshmand@iasbs.ac.ir",
+            "name": "Mohsen Hooshmand",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "The DRaW is a convolutional neural network to predict new virus-antiviral interactions VAIs from approved antivirals.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Plasmid map drawing",
+                    "uri": "http://edamontology.org/operation_0578"
+                },
+                {
+                    "term": "Restriction map drawing",
+                    "uri": "http://edamontology.org/operation_0575"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/BioinformaticsIASBS/DRaW",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T09:09:17.371602Z",
+    "license": "Not licensed",
+    "name": "DRaW",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-023-05181-8",
+            "metadata": {
+                "abstract": "Background: Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved drugs. Matrix factorization methods have much attention and utilization in DTIs. However, they suffer from some drawbacks. Methods: We explain why matrix factorization is not the best for DTI prediction. Then, we propose a deep learning model (DRaW) to predict DTIs without having input data leakage. We compare our model with several matrix factorization methods and a deep model on three COVID-19 datasets. In addition, to ensure the validation of DRaW, we evaluate it on benchmark datasets. Furthermore, as an external validation, we conduct a docking study on the COVID-19 recommended drugs. Results: In all cases, the results confirm that DRaW outperforms matrix factorization and deep models. The docking results approve the top-ranked recommended drugs for COVID-19. Conclusions: In this paper, we show that it may not be the best choice to use matrix factorization in the DTI prediction. Matrix factorization methods suffer from some intrinsic issues, e.g., sparsity in the domain of bioinformatics applications and fixed-unchanged size of the matrix-related paradigm. Therefore, we propose an alternative method (DRaW) that uses feature vectors rather than matrix factorization and demonstrates better performance than other famous methods on three COVID-19 and four benchmark datasets.",
+                "authors": [
+                    {
+                        "name": "Gharaghani S."
+                    },
+                    {
+                        "name": "Hashemi S.M."
+                    },
+                    {
+                        "name": "Hooshmand M."
+                    },
+                    {
+                        "name": "Zabihian A."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "DRaW: prediction of COVID-19 antivirals by deep learning—an objection on using matrix factorization"
+            },
+            "pmcid": "PMC9931173",
+            "pmid": "36793010"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/drdimont/drdimont.biotools.json b/data/drdimont/drdimont.biotools.json
new file mode 100644
index 0000000000000..ce638c0eaa258
--- /dev/null
+++ b/data/drdimont/drdimont.biotools.json
@@ -0,0 +1,89 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T22:26:35.300973Z",
+    "biotoolsCURIE": "biotools:drdimont",
+    "biotoolsID": "drdimont",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Bernhard Y. Renard",
+            "orcidid": "http://orcid.org/0000-0003-4589-9809"
+        },
+        {
+            "name": "Julian Hugo",
+            "orcidid": "http://orcid.org/0000-0003-3355-1071"
+        },
+        {
+            "name": "Katharina Baum",
+            "orcidid": "http://orcid.org/0000-0001-7256-0566"
+        },
+        {
+            "name": "Pauline Hiort",
+            "orcidid": "http://orcid.org/0000-0002-3530-7358"
+        }
+    ],
+    "description": "Explainable drug response prediction from differential analysis of multi-omics networks.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://cran.r-project.org/web/packages/DrDimont/DrDimont.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cran.r-project.org/package=DrDimont",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-26T22:26:35.303658Z",
+    "license": "MIT",
+    "name": "DrDimont",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac477",
+            "pmcid": "PMC9486584",
+            "pmid": "36124784"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        }
+    ]
+}
diff --git a/data/dreamm/dreamm.biotools.json b/data/dreamm/dreamm.biotools.json
new file mode 100644
index 0000000000000..b3016671484c7
--- /dev/null
+++ b/data/dreamm/dreamm.biotools.json
@@ -0,0 +1,92 @@
+{
+    "additionDate": "2023-01-28T10:57:52.584573Z",
+    "biotoolsCURIE": "biotools:dreamm",
+    "biotoolsID": "dreamm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zcournia@bioacademy.gr",
+            "name": "Zoe Cournia",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A web-based server for drugging protein-membrane interfaces as a novel workflow for targeted drug design.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Binding site prediction",
+                    "uri": "http://edamontology.org/operation_2575"
+                },
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://dreamm.ni4os.eu",
+    "lastUpdate": "2023-01-28T10:57:52.587805Z",
+    "license": "Not licensed",
+    "name": "DREAMM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC680",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: The allosteric modulation of peripheral membrane proteins (PMPs) by targeting protein-membrane interactions with drug-like molecules represents a new promising therapeutic strategy for proteins currently considered undruggable. However, the accessibility of protein-membrane interfaces by small molecules has been so far unexplored, possibly due to the complexity of the interface, the limited protein-membrane structural information and the lack of computational workflows to study it. Herein, we present a pipeline for drugging protein-membrane interfaces using the DREAMM (Drugging pRotein mEmbrAne Machine learning Method) web server. DREAMM works in the back end with a fast and robust ensemble machine learning algorithm for identifying protein-membrane interfaces of PMPs. Additionally, DREAMM also identifies binding pockets in the vicinity of the predicted membrane-penetrating amino acids in protein conformational ensembles provided by the user or generated within DREAMM. AVAILABILITY AND IMPLEMENTATION: DREAMM web server is accessible via https://dreamm.ni4os.eu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Chatzigoulas A."
+                    },
+                    {
+                        "name": "Cournia Z."
+                    }
+                ],
+                "date": "2022-12-13T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "DREAMM: a web-based server for drugging protein-membrane interfaces as a novel workflow for targeted drug design"
+            },
+            "pmcid": "PMC9750117",
+            "pmid": "36355565"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Membrane and lipoproteins",
+            "uri": "http://edamontology.org/topic_0820"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/dresis/dresis.biotools.json b/data/dresis/dresis.biotools.json
new file mode 100644
index 0000000000000..3faf43933178b
--- /dev/null
+++ b/data/dresis/dresis.biotools.json
@@ -0,0 +1,69 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T01:59:16.618787Z",
+    "biotoolsCURIE": "biotools:dresis",
+    "biotoolsID": "dresis",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhufeng@zju.edu.cn",
+            "name": "Feng Zhu",
+            "orcidid": "https://orcid.org/0000-0001-8069-0053",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Xiuna Sun"
+        },
+        {
+            "name": "Yintao Zhang"
+        },
+        {
+            "name": "Yunqing Qiu"
+        }
+    ],
+    "description": "The first comprehensive landscape of drug resistance information.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://idrblab.org/dresis/",
+    "lastUpdate": "2023-01-09T01:59:16.621263Z",
+    "name": "DRESIS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC812",
+            "pmid": "36243960"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Drug development",
+            "uri": "http://edamontology.org/topic_3373"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Drug metabolism",
+            "uri": "http://edamontology.org/topic_3375"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        }
+    ]
+}
diff --git a/data/drnet/drnet.biotools.json b/data/drnet/drnet.biotools.json
new file mode 100644
index 0000000000000..8babcba4d5a74
--- /dev/null
+++ b/data/drnet/drnet.biotools.json
@@ -0,0 +1,104 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T01:54:44.962073Z",
+    "biotoolsCURIE": "biotools:drnet",
+    "biotoolsID": "drnet",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Shuicheng Yan"
+        },
+        {
+            "name": "Zhiyuan Fang"
+        },
+        {
+            "name": "Guangyu Gao",
+            "orcidid": "https://orcid.org/0000-0002-0083-3016"
+        }
+    ],
+    "description": "Double Recalibration Network for Few-Shot Semantic Segmentation.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image annotation",
+                    "uri": "http://edamontology.org/operation_3553"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/fangzy97/drnet",
+    "language": [
+        "Pascal"
+    ],
+    "lastUpdate": "2023-01-09T01:54:44.964682Z",
+    "license": "Not licensed",
+    "name": "DRNet",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1109/TIP.2022.3215905",
+            "metadata": {
+                "abstract": "© 1992-2012 IEEE.Few-shot segmentation aims at learning to segment query images guided by only a few annotated images from the support set. Previous methods rely on mining the feature embedding similarity across the query and the support images to achieve successful segmentation. However, these models tend to perform badly in cases where the query instances have a large variance from the support ones. To enhance model robustness against such intra-class variance, we propose a Double Recalibration Network (DRNet) with two recalibration modules, i.e., the Self-adapted Recalibration (SR) module and the Cross-attended Recalibration (CR) module. In particular, beyond learning robust feature embedding for pixel-wise comparison between support and query as in conventional methods, the DRNet further exploits semantic-aware knowledge embedded in the query image to help segment itself, which we call 'self-adapted recalibration'. More specifically, DRNet first employs guidance from the support set to roughly predict an incomplete but correct initial object region for the query image, and then reversely uses the feature embedding extracted from the incomplete object region to segment the query image. Also, we devise a CR module to refine the feature representation of the query image by propagating the underlying knowledge embedded in the support image's foreground to the query. Instead of foreground global pooling, we refine the response at each pixel in the query feature map by attending to all foreground pixels in the support feature map and taking the weighted average by their similarity; meanwhile, feature maps of the query image are also added back to weighted feature maps as a residual connection. Our DRNet can effectively address the intra-class variance under the few-shot setting with such two recalibration modules, and mine more accurate target regions for query images. We conduct extensive experiments on the popular benchmarks PASCAL- 5i and COCO- 20i. The DRNet with the best configuration achieves the mIoU of 63.6% and 64.9% on PASCAL- 5i and 44.7% and 49.6% on COCO- 20i for 1-shot and 5-shot settings respectively, significantly outperforming the state-of-the-arts without any bells and whistles. Code is available at: https://github.com/fangzy97/drnet.",
+                "authors": [
+                    {
+                        "name": "Fang Z."
+                    },
+                    {
+                        "name": "Gao G."
+                    },
+                    {
+                        "name": "Han C."
+                    },
+                    {
+                        "name": "Liu C.H."
+                    },
+                    {
+                        "name": "Wei Y."
+                    },
+                    {
+                        "name": "Yan S."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "IEEE Transactions on Image Processing",
+                "title": "DRNet: Double Recalibration Network for Few-Shot Semantic Segmentation"
+            },
+            "pmid": "36282824"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        }
+    ]
+}
diff --git a/data/drugmap/drugmap.biotools.json b/data/drugmap/drugmap.biotools.json
new file mode 100644
index 0000000000000..2bbedb1845095
--- /dev/null
+++ b/data/drugmap/drugmap.biotools.json
@@ -0,0 +1,101 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T01:48:18.201517Z",
+    "biotoolsCURIE": "biotools:drugmap",
+    "biotoolsID": "drugmap",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chenyuzong@sz.tsinghua.edu.cn",
+            "name": "Yuzong Chen",
+            "orcidid": "https://orcid.org/0000-0002-5473-8022",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhufeng@zju.edu.cn",
+            "name": "Feng Zhu",
+            "orcidid": "https://orcid.org/0000-0001-8069-0053",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zengsu@zju.edu.cn",
+            "name": "Su Zeng",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Molecular atlas and pharma-information of all drugs.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Keyword",
+                        "uri": "http://edamontology.org/data_0968"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://idrblab.org/drugmap/",
+    "lastUpdate": "2023-01-09T01:48:18.204080Z",
+    "name": "DrugMAP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC813",
+            "pmid": "36243961"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Drug metabolism",
+            "uri": "http://edamontology.org/topic_3375"
+        },
+        {
+            "term": "Pharmacology",
+            "uri": "http://edamontology.org/topic_0202"
+        },
+        {
+            "term": "Pharmacovigilance",
+            "uri": "http://edamontology.org/topic_3378"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/drugnomeai/drugnomeai.biotools.json b/data/drugnomeai/drugnomeai.biotools.json
new file mode 100644
index 0000000000000..adaef53cce171
--- /dev/null
+++ b/data/drugnomeai/drugnomeai.biotools.json
@@ -0,0 +1,141 @@
+{
+    "additionDate": "2023-01-28T11:05:45.743090Z",
+    "biotoolsCURIE": "biotools:drugnomeai",
+    "biotoolsID": "drugnomeai",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "dimitrios.vitsios@astrazeneca.com",
+            "name": "Dimitrios Vitsios",
+            "orcidid": "https://orcid.org/0000-0002-8939-5445",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://drugnomeai.public.cgr.astrazeneca.com",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T11:06:10.118415Z",
+    "license": "MPL-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/astrazeneca-cgr-publications/DrugnomeAI-release"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/astrazeneca-cgr-publications/DrugnomeAI-release/blob/master/drugnome_ai/conf/"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/astrazeneca-cgr-publications/DrugnomeAI-release/blob/master/drugnome_ai/conf/.config"
+        }
+    ],
+    "name": "DrugnomeAI",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S42003-022-04245-4",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a stochastic semi-supervised ML framework to develop DrugnomeAI, which estimates the druggability likelihood for every protein-coding gene in the human exome. DrugnomeAI integrates gene-level properties from 15 sources resulting in 324 features. The tool generates exome-wide predictions based on labelled sets of known drug targets (median AUC: 0.97), highlighting features from protein-protein interaction networks as top predictors. DrugnomeAI provides generic as well as specialised models stratified by disease type or drug therapeutic modality. The top-ranking DrugnomeAI genes were significantly enriched for genes previously selected for clinical development programs (p value < 1 × 10−308) and for genes achieving genome-wide significance in phenome-wide association studies of 450 K UK Biobank exomes for binary (p value = 1.7 × 10−5) and quantitative traits (p value = 1.6 × 10−7). We accompany our method with a web application (http://drugnomeai.public.cgr.astrazeneca.com) to visualise the druggability predictions and the key features that define gene druggability, per disease type and modality.",
+                "authors": [
+                    {
+                        "name": "Dhindsa R.S."
+                    },
+                    {
+                        "name": "Engkvist O."
+                    },
+                    {
+                        "name": "Harper A.R."
+                    },
+                    {
+                        "name": "Hill P."
+                    },
+                    {
+                        "name": "Middleton L."
+                    },
+                    {
+                        "name": "Petrovski S."
+                    },
+                    {
+                        "name": "Raies A."
+                    },
+                    {
+                        "name": "Stainer J."
+                    },
+                    {
+                        "name": "Tulodziecka E."
+                    },
+                    {
+                        "name": "Vitsios D."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Communications Biology",
+                "title": "DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets"
+            },
+            "pmcid": "PMC9700683",
+            "pmid": "36434048"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application",
+        "Workbench"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/drugrep/drugrep.biotools.json b/data/drugrep/drugrep.biotools.json
new file mode 100644
index 0000000000000..e6be37af5c571
--- /dev/null
+++ b/data/drugrep/drugrep.biotools.json
@@ -0,0 +1,134 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T01:43:06.492127Z",
+    "biotoolsCURIE": "biotools:drugrep",
+    "biotoolsID": "drugrep",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "cao@scu.edu.cn",
+            "name": "Yang Cao",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ji-xiang Liu"
+        },
+        {
+            "name": "Jian-hong Gan"
+        }
+    ],
+    "description": "DrugRep is a computer-aided drug discovery online tool for virtual screening of drugs, particularly for drug repurposing.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "PDB ID",
+                        "uri": "http://edamontology.org/data_1127"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Expression data",
+                        "uri": "http://edamontology.org/data_2603"
+                    },
+                    "format": [
+                        {
+                            "term": "PDB",
+                            "uri": "http://edamontology.org/format_1476"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Binding site prediction",
+                    "uri": "http://edamontology.org/operation_2575"
+                },
+                {
+                    "term": "Protein-protein docking",
+                    "uri": "http://edamontology.org/operation_3899"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://cao.labshare.cn/drugrep/",
+    "lastUpdate": "2023-01-09T01:43:06.494676Z",
+    "name": "DrugRep",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41401-022-00996-2",
+            "metadata": {
+                "abstract": "© 2022, The Author(s), under exclusive licence to Shanghai Institute of Materia Medica, Chinese Academy of Sciences and Chinese Pharmacological Society.Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/.",
+                "authors": [
+                    {
+                        "name": "Cao Y."
+                    },
+                    {
+                        "name": "Chen S.-W."
+                    },
+                    {
+                        "name": "Dai W.-T."
+                    },
+                    {
+                        "name": "Gan J.-H."
+                    },
+                    {
+                        "name": "Liu J.-X."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Xiao Z.-X."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Acta Pharmacologica Sinica",
+                "title": "DrugRep: an automatic virtual screening server for drug repurposing"
+            },
+            "pmcid": "PMC9549438",
+            "pmid": "36216900"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Drug development",
+            "uri": "http://edamontology.org/topic_3373"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Drug metabolism",
+            "uri": "http://edamontology.org/topic_3375"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/drugtax/drugtax.biotools.json b/data/drugtax/drugtax.biotools.json
new file mode 100644
index 0000000000000..5e27114c61834
--- /dev/null
+++ b/data/drugtax/drugtax.biotools.json
@@ -0,0 +1,117 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T01:37:11.716358Z",
+    "biotoolsCURIE": "biotools:drugtax",
+    "biotoolsID": "drugtax",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "irina.moreira@cnc.uc.pt",
+            "name": "Irina S. Moreira",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "A. J. Preto"
+        },
+        {
+            "name": "Paulo C. Correia"
+        }
+    ],
+    "description": "Package for drug taxonomy identification and explainable feature extraction.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pypi.org/project/DrugTax/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T01:37:11.718952Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/MoreiraLAB/DrugTax"
+        }
+    ],
+    "name": "DrugTax",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13321-022-00649-W",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).DrugTax is an easy-to-use Python package for small molecule detailed characterization. It extends a previously explored chemical taxonomy making it ready-to-use in any Artificial Intelligence approach. DrugTax leverages small molecule representations as input in one of their most accessible and simple forms (SMILES) and allows the simultaneously extraction of taxonomy information and key features for big data algorithm deployment. In addition, it delivers a set of tools for bulk analysis and visualization that can also be used for chemical space representation and molecule similarity assessment. DrugTax is a valuable tool for chemoinformatic processing and can be easily integrated in drug discovery pipelines. DrugTax can be effortlessly installed via PyPI (https://pypi.org/project/DrugTax/) or GitHub (https://github.com/MoreiraLAB/DrugTax).",
+                "authors": [
+                    {
+                        "name": "Correia P.C."
+                    },
+                    {
+                        "name": "Moreira I.S."
+                    },
+                    {
+                        "name": "Preto A.J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Journal of Cheminformatics",
+                "title": "DrugTax: package for drug taxonomy identification and explainable feature extraction"
+            },
+            "pmcid": "PMC9609197",
+            "pmid": "36303244"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cheminformatics",
+            "uri": "http://edamontology.org/topic_2258"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Taxonomy",
+            "uri": "http://edamontology.org/topic_0637"
+        }
+    ]
+}
diff --git a/data/dsmzcelldive/dsmzcelldive.biotools.json b/data/dsmzcelldive/dsmzcelldive.biotools.json
new file mode 100644
index 0000000000000..67dfbfc4fc3ac
--- /dev/null
+++ b/data/dsmzcelldive/dsmzcelldive.biotools.json
@@ -0,0 +1,137 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-19T00:01:13.848337Z",
+    "biotoolsCURIE": "biotools:dsmzcelldive",
+    "biotoolsID": "dsmzcelldive",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Claudia Pommerenke",
+            "orcidid": "https://orcid.org/0000-0002-9448-416X"
+        },
+        {
+            "name": "Sonja Eberth",
+            "orcidid": "https://orcid.org/0000-0002-5796-2089"
+        },
+        {
+            "name": "Julia Koblitz",
+            "orcidid": "https://orcid.org/0000-0002-7260-2129",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Laura Steenpass",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Diving into high-throughput cell line data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "DNA barcoding",
+                    "uri": "http://edamontology.org/operation_3200"
+                },
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://celldive.dsmz.de",
+    "language": [
+        "JavaScript",
+        "PHP"
+    ],
+    "lastUpdate": "2023-01-19T00:01:13.851628Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/JKoblitz/DSMZCellDive"
+        }
+    ],
+    "name": "DSMZCellDive",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.12688/f1000research.111175.2",
+            "metadata": {
+                "abstract": "© 2022 Koblitz J et al.Human and animal cell lines serve as model systems in a wide range of life sciences such as cancer and infection research or drug screening. Reproducible data are highly dependent on authenticated, contaminant-free cell lines, no better delivered than by the official and certified biorepositories. Offering a web portal to high-throughput information on these model systems will facilitate working with and comparing to these references by data otherwise dispersed at different sources. We here provide DSMZCellDive to access a comprehensive data source on human and animal cell lines, freely available at celldive.dsmz.de. A wide variety of data sources are generated such as RNA-seq transcriptome data and STR (short tandem repeats) profiles. Several starting points ease entering the database via browsing, searching or visualising. This web tool is designed for further expansion on meta and high-throughput data to be generated in future. Explicated examples for the power of this novel tool include analysis of B-cell differentiation markers, homeo-oncogene expression, and measurement of genomic loss of heterozygosities by an enlarged STR panel of 17 loci. Sharing the data on cell lines by the biorepository itself will be of benefit to the scientific community since it (1) supports the selection of appropriate model cell lines, (2) ensures reliability, (3) avoids misleading data, (4) saves on additional experimentals, and (5) serves as reference for genomic and gene expression data.",
+                "authors": [
+                    {
+                        "name": "Dirks W.G."
+                    },
+                    {
+                        "name": "Eberth S."
+                    },
+                    {
+                        "name": "Koblitz J."
+                    },
+                    {
+                        "name": "Nagel S."
+                    },
+                    {
+                        "name": "Pommerenke C."
+                    },
+                    {
+                        "name": "Steenpass L."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "F1000Research",
+                "title": "DSMZCellDive: Diving into high-throughput cell line data"
+            },
+            "pmcid": "PMC9334839",
+            "pmid": "35949917"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/duet_sequencing/duet_sequencing.biotools.json b/data/duet_sequencing/duet_sequencing.biotools.json
new file mode 100644
index 0000000000000..f3142f448c6e3
--- /dev/null
+++ b/data/duet_sequencing/duet_sequencing.biotools.json
@@ -0,0 +1,110 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-28T00:01:10.642062Z",
+    "biotoolsCURIE": "biotools:duet_sequencing",
+    "biotoolsID": "duet_sequencing",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Amy Wing-Sze Leung"
+        },
+        {
+            "name": "Syed Shakeel Ahmed"
+        },
+        {
+            "name": "Yekai Zhou"
+        },
+        {
+            "name": "Ruibang Luo",
+            "orcidid": "http://orcid.org/0000-0001-9711-6533"
+        },
+        {
+            "name": "Tak-Wah Lam",
+            "orcidid": "http://orcid.org/0000-0003-4676-8587"
+        }
+    ],
+    "description": "SNP-Assisted Structural Variant Calling and Phasing Using Oxford Nanopore Sequencing.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/yekaizhou/duet",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-28T00:01:10.644636Z",
+    "license": "BSD-3-Clause",
+    "name": "Duet",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/s12859-022-05025-x",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Whole genome sequencing using the long-read Oxford Nanopore Technologies (ONT) MinION sequencer provides a cost-effective option for structural variant (SV) detection in clinical applications. Despite the advantage of using long reads, however, accurate SV calling and phasing are still challenging. Results: We introduce Duet, an SV detection tool optimized for SV calling and phasing using ONT data. The tool uses novel features integrated from both SV signatures and single-nucleotide polymorphism signatures, which can accurately distinguish SV haplotype from a false signal. Duet was benchmarked against state-of-the-art tools on multiple ONT sequencing datasets of sequencing coverage ranging from 8× to 40×. At low sequencing coverage of 8×, Duet performs better than all other tools in SV calling, SV genotyping and SV phasing. When the sequencing coverage is higher (20× to 40×), the F1-score for SV phasing is further improved in comparison to the performance of other tools, while its performance of SV genotyping and SV calling remains higher than other tools. Conclusion: Duet can perform accurate SV calling, SV genotyping and SV phasing using low-coverage ONT data, making it very useful for low-coverage genomes. It has great performance when scaled to high-coverage genomes, which is adaptable to various clinical applications. Duet is open source and is available at https://github.com/yekaizhou/duet.",
+                "authors": [
+                    {
+                        "name": "Ahmed S.S."
+                    },
+                    {
+                        "name": "Lam T.-W."
+                    },
+                    {
+                        "name": "Leung A.W.-S."
+                    },
+                    {
+                        "name": "Luo R."
+                    },
+                    {
+                        "name": "Zhou Y."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "Duet: SNP-assisted structural variant calling and phasing using Oxford nanopore sequencing"
+            },
+            "pmcid": "PMC9639287",
+            "pmid": "36344913"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/dxformer/dxformer.biotools.json b/data/dxformer/dxformer.biotools.json
new file mode 100644
index 0000000000000..12770b215aa26
--- /dev/null
+++ b/data/dxformer/dxformer.biotools.json
@@ -0,0 +1,56 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T11:12:41.839763Z",
+    "biotoolsCURIE": "biotools:dxformer",
+    "biotoolsID": "dxformer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jiajiepeng@nwpu.edu.cn",
+            "name": "Jiajie Peng",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zywei@fudan.edu.cn",
+            "name": "Zhongyu Wei",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A decoupled automatic diagnostic system based on decoder-encoder transformer with dense symptom representations.",
+    "editPermission": {
+        "type": "public"
+    },
+    "homepage": "https://github.com/lemuria-wchen/DxFormer",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T11:12:41.842434Z",
+    "license": "MIT",
+    "name": "DxFormer",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC744",
+            "pmcid": "PMC9825744",
+            "pmid": "36409016"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Respiratory medicine",
+            "uri": "http://edamontology.org/topic_3322"
+        }
+    ]
+}
diff --git a/data/dynamicviz/dynamicviz.biotools.json b/data/dynamicviz/dynamicviz.biotools.json
new file mode 100644
index 0000000000000..be7c61ed02537
--- /dev/null
+++ b/data/dynamicviz/dynamicviz.biotools.json
@@ -0,0 +1,79 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T15:26:13.237362Z",
+    "biotoolsCURIE": "biotools:dynamicviz",
+    "biotoolsID": "dynamicviz",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jamesz@stanford.edu",
+            "name": "James Zou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Rong Ma"
+        },
+        {
+            "name": "Eric D. Sun",
+            "orcidid": "http://orcid.org/0000-0001-8805-9864"
+        }
+    ],
+    "description": "Dynamic visualization of high-dimensional data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/sunericd/dynamicviz",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T15:26:13.239808Z",
+    "license": "MIT",
+    "name": "DynamicViz",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1101/2022.05.27.493785"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Data visualisation",
+            "uri": "http://edamontology.org/topic_0092"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        }
+    ]
+}
diff --git a/data/e-pix_web/e-pix_web.biotools.json b/data/e-pix_web/e-pix_web.biotools.json
new file mode 100644
index 0000000000000..82b780f20f939
--- /dev/null
+++ b/data/e-pix_web/e-pix_web.biotools.json
@@ -0,0 +1,79 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T23:31:37.298031Z",
+    "biotoolsCURIE": "biotools:e-pix_web",
+    "biotoolsID": "e-pix_web",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Lukas Arnecke"
+        },
+        {
+            "name": "Martin Bialke"
+        },
+        {
+            "name": "Wolfgang Hoffmann"
+        },
+        {
+            "name": "Christopher Hampf",
+            "orcidid": "https://orcid.org/0000-0002-4557-4783"
+        }
+    ],
+    "description": "Federated Trusted Third Party as an Approach for Privacy Preserving Record Linkage in a Large Network of University Medicines in Pandemic Research.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Anonymisation",
+                    "uri": "http://edamontology.org/operation_3283"
+                },
+                {
+                    "term": "Filtering",
+                    "uri": "http://edamontology.org/operation_3695"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://demo.ths-greifswald.de/epix-web/",
+    "lastUpdate": "2023-01-26T23:31:37.301468Z",
+    "name": "E-PIX",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.21203/RS.3.RS-1053445/V1"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Data security",
+            "uri": "http://edamontology.org/topic_3263"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        }
+    ]
+}
diff --git a/data/e-snps_and_go/e-snps_and_go.biotools.json b/data/e-snps_and_go/e-snps_and_go.biotools.json
new file mode 100644
index 0000000000000..7d3d1498082e0
--- /dev/null
+++ b/data/e-snps_and_go/e-snps_and_go.biotools.json
@@ -0,0 +1,103 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T01:23:56.169620Z",
+    "biotoolsCURIE": "biotools:e-snps_and_go",
+    "biotoolsID": "e-snps_and_go",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "pierluigi.martelli@unibo.it",
+            "name": "Pier Luigi Martelli",
+            "orcidid": "https://orcid.org/0000-0002-0274-5669",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Matteo Manfredi"
+        },
+        {
+            "name": "Castrense Savojardo",
+            "orcidid": "https://orcid.org/0000-0002-7359-0633"
+        },
+        {
+            "name": "Rita Casadio",
+            "orcidid": "https://orcid.org/0000-0002-7462-7039"
+        }
+    ],
+    "description": "Embedding of protein sequence and function improves the annotation of human pathogenic variants.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Gene functional annotation",
+                    "uri": "http://edamontology.org/operation_3672"
+                },
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                },
+                {
+                    "term": "Virulence prediction",
+                    "uri": "http://edamontology.org/operation_3461"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://esnpsandgo.biocomp.unibo.it",
+    "lastUpdate": "2023-01-09T01:23:56.172153Z",
+    "name": "E-SNPs and GO",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC678",
+            "pmid": "36227117"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        },
+        {
+            "term": "Protein variants",
+            "uri": "http://edamontology.org/topic_3120"
+        }
+    ]
+}
diff --git a/data/e-snpsgo/e-snpsgo.biotools.json b/data/e-snpsgo/e-snpsgo.biotools.json
new file mode 100644
index 0000000000000..82d65ec2b97e2
--- /dev/null
+++ b/data/e-snpsgo/e-snpsgo.biotools.json
@@ -0,0 +1,131 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-05T08:42:10.922859Z",
+    "biotoolsCURIE": "biotools:E-SNPsGO",
+    "biotoolsID": "E-SNPsGO",
+    "description": "E-SNPs&GO is a machine-learning method the pathogenicity of human variations. E-SNPs&GO classify input variations into pathogenic or benign.",
+    "editPermission": {
+        "authors": [
+            "ELIXIR-ITA-BOLOGNA"
+        ],
+        "type": "group"
+    },
+    "elixirCommunity": [
+        "Rare Diseases"
+    ],
+    "elixirNode": [
+        "Italy"
+    ],
+    "elixirPlatform": [
+        "Tools"
+    ],
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                },
+                {
+                    "data": {
+                        "term": "Sequence variations",
+                        "uri": "http://edamontology.org/data_3498"
+                    },
+                    "format": [
+                        {
+                            "term": "Textual format",
+                            "uri": "http://edamontology.org/format_2330"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Score",
+                        "uri": "http://edamontology.org/data_1772"
+                    },
+                    "format": [
+                        {
+                            "term": "HTML",
+                            "uri": "http://edamontology.org/format_2331"
+                        },
+                        {
+                            "term": "JSON",
+                            "uri": "http://edamontology.org/format_3464"
+                        },
+                        {
+                            "term": "TSV",
+                            "uri": "http://edamontology.org/format_3475"
+                        }
+                    ]
+                }
+            ]
+        }
+    ],
+    "homepage": "https://esnpsandgo.biocomp.unibo.it/",
+    "language": [
+        "Other"
+    ],
+    "lastUpdate": "2023-01-05T09:18:20.176934Z",
+    "name": "E-SNPs and GO",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "PierLuigiMartelli",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac678",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The advent of massive DNA sequencing technologies is producing a huge number of human single-nucleotide polymorphisms occurring in protein-coding regions and possibly changing their sequences. Discriminating harmful protein variations from neutral ones is one of the crucial challenges in precision medicine. Computational tools based on artificial intelligence provide models for protein sequence encoding, bypassing database searches for evolutionary information. We leverage the new encoding schemes for an efficient annotation of protein variants. RESULTS: E-SNPs&GO is a novel method that, given an input protein sequence and a single amino acid variation, can predict whether the variation is related to diseases or not. The proposed method adopts an input encoding completely based on protein language models and embedding techniques, specifically devised to encode protein sequences and GO functional annotations. We trained our model on a newly generated dataset of 101 146 human protein single amino acid variants in 13 661 proteins, derived from public resources. When tested on a blind set comprising 10 266 variants, our method well compares to recent approaches released in literature for the same task, reaching a Matthews Correlation Coefficient score of 0.72. We propose E-SNPs&GO as a suitable, efficient and accurate large-scale annotator of protein variant datasets. AVAILABILITY AND IMPLEMENTATION: The method is available as a webserver at https://esnpsandgo.biocomp.unibo.it. Datasets and predictions are available at https://esnpsandgo.biocomp.unibo.it/datasets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Casadio R."
+                    },
+                    {
+                        "name": "Manfredi M."
+                    },
+                    {
+                        "name": "Martelli P.L."
+                    },
+                    {
+                        "name": "Savojardo C."
+                    }
+                ],
+                "date": "2022-11-30T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants"
+            },
+            "pmcid": "PMC9710551",
+            "pmid": "36227117",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Protein variants",
+            "uri": "http://edamontology.org/topic_3120"
+        }
+    ]
+}
diff --git a/data/e-tsn/e-tsn.biotools.json b/data/e-tsn/e-tsn.biotools.json
new file mode 100644
index 0000000000000..797ff4b834231
--- /dev/null
+++ b/data/e-tsn/e-tsn.biotools.json
@@ -0,0 +1,101 @@
+{
+    "additionDate": "2023-01-28T11:21:03.788494Z",
+    "biotoolsCURIE": "biotools:e-tsn",
+    "biotoolsID": "e-tsn",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "shiliangli@ecust.edu.cn",
+            "name": "Shiliang Li",
+            "orcidid": "https://orcid.org/0000-0003-4414-237X",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An interactive visual exploration platform for target-disease knowledge mapping from literature.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Text mining",
+                    "uri": "http://edamontology.org/operation_0306"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.lilab-ecust.cn/etsn/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T11:21:03.791066Z",
+    "license": "Other",
+    "name": "e-TSN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC465",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.",
+                "authors": [
+                    {
+                        "name": "Feng Z."
+                    },
+                    {
+                        "name": "Li H."
+                    },
+                    {
+                        "name": "Li S."
+                    },
+                    {
+                        "name": "Shen Z."
+                    }
+                ],
+                "date": "2022-11-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "e-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature"
+            },
+            "pmcid": "PMC9677481",
+            "pmid": "36347537"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/e.page/e.page.biotools.json b/data/e.page/e.page.biotools.json
new file mode 100644
index 0000000000000..1e8cc53a09db5
--- /dev/null
+++ b/data/e.page/e.page.biotools.json
@@ -0,0 +1,129 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T10:28:44.717607Z",
+    "biotoolsCURIE": "biotools:e.page",
+    "biotoolsID": "e.page",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "a.mehdi@uq.edu.au",
+            "name": "Ahmed M. Mehdi",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Environmental pathways affecting gene expression (E.PAGE) as an R package to predict gene-environment associations.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/AhmedMehdiLab/E.PAGE",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-25T10:28:44.720956Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/AhmedMehdiLab/E.PATH"
+        }
+    ],
+    "name": "E.PAGE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41598-022-21988-6",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).The purpose of this study is to manually and semi-automatically curate a database and develop an R package that will act as a comprehensive resource to understand how biological processes are dysregulated due to interactions with environmental factors. The initial database search run on the Gene Expression Omnibus and the Molecular Signature Database retrieved a total of 90,018 articles. After title and abstract screening against pre-set criteria, a total of 237 datasets were selected and 522 gene modules were manually annotated. We then curated a database containing four environmental factors, cigarette smoking, diet, infections and toxic chemicals, along with a total of 25,789 genes that had an association with one or more of gene modules. The database and statistical analysis package was then tested with the differentially expressed genes obtained from the published literature related to type 1 diabetes, rheumatoid arthritis, small cell lung cancer, COVID-19, cobalt exposure and smoking. On testing, we uncovered statistically enriched biological processes, which revealed pathways associated with environmental factors and the genes. The curated database and enrichment tool are available as R packages at https://github.com/AhmedMehdiLab/E.PATH and https://github.com/AhmedMehdiLab/E.PAGE respectively.",
+                "authors": [
+                    {
+                        "name": "Ali R.A."
+                    },
+                    {
+                        "name": "Ali S."
+                    },
+                    {
+                        "name": "Badshah J."
+                    },
+                    {
+                        "name": "Chandra J."
+                    },
+                    {
+                        "name": "Frazer I.H."
+                    },
+                    {
+                        "name": "Mehdi A.M."
+                    },
+                    {
+                        "name": "Muralidharan S."
+                    },
+                    {
+                        "name": "Thomas R."
+                    },
+                    {
+                        "name": "Yang L."
+                    },
+                    {
+                        "name": "Zahir S.F."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "Environmental pathways affecting gene expression (E.PAGE) as an R package to predict gene–environment associations"
+            },
+            "pmcid": "PMC9636158",
+            "pmid": "36333579"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Immunology",
+            "uri": "http://edamontology.org/topic_0804"
+        },
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/easy353/easy353.biotools.json b/data/easy353/easy353.biotools.json
new file mode 100644
index 0000000000000..9fa2083ac5ced
--- /dev/null
+++ b/data/easy353/easy353.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T16:45:43.579757Z",
+    "biotoolsCURIE": "biotools:easy353",
+    "biotoolsID": "easy353",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yyu@scu.edu.cn",
+            "name": "Yan Yu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Easy353 is a tool for recovering Angiosperms353 gene set(AGS), which can filter reads from high throughput sequencing data such as RNASeq and genome skimming and capture AGS accurately and effectively with our optimized reference-guided assembler.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Filtering",
+                    "uri": "http://edamontology.org/operation_3695"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/plant720/Easy353",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-20T16:45:43.582333Z",
+    "license": "MIT",
+    "name": "Easy353",
+    "operatingSystem": [
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/MOLBEV/MSAC261",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.The Angiosperms353 gene set (AGS) consists of a set of 353 universal low-copy nuclear genes that were selected by examining more than 600 angiosperm species. These genes can be used for phylogenetic studies and population genetics at multiple taxonomic scales. However, current pipelines are not able to recover Angiosperms353 genes efficiently and accurately from high-throughput sequences. Here, we developed Easy353, a reference-guided assembly tool to recover the AGS from high-throughput sequencing (HTS) data (including genome skimming, RNA-seq, and target enrichment). Easy353 is an open-source user-friendly assembler for diverse types of high-throughput data. It has a graphical user interface and a command-line interface that is compatible with all widely-used computer systems. Evaluations, based on both simulated and empirical data, suggest that Easy353 yields low rates of assembly errors.",
+                "authors": [
+                    {
+                        "name": "Guo Y."
+                    },
+                    {
+                        "name": "Liu E."
+                    },
+                    {
+                        "name": "Xie P."
+                    },
+                    {
+                        "name": "Yu Y."
+                    },
+                    {
+                        "name": "Zhang Z."
+                    },
+                    {
+                        "name": "Zhou W."
+                    }
+                ],
+                "date": "2022-12-05T00:00:00Z",
+                "journal": "Molecular biology and evolution",
+                "title": "Easy353: A Tool to Get Angiosperms353 Genes for Phylogenomic Research"
+            },
+            "pmcid": "PMC9757696",
+            "pmid": "36458838"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Phylogenomics",
+            "uri": "http://edamontology.org/topic_0194"
+        },
+        {
+            "term": "Population genetics",
+            "uri": "http://edamontology.org/topic_3056"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/ecgxai/ecgxai.biotools.json b/data/ecgxai/ecgxai.biotools.json
new file mode 100644
index 0000000000000..24ddb17a6b285
--- /dev/null
+++ b/data/ecgxai/ecgxai.biotools.json
@@ -0,0 +1,72 @@
+{
+    "additionDate": "2023-01-27T17:04:09.252092Z",
+    "biotoolsCURIE": "biotools:ecgxai",
+    "biotoolsID": "ecgxai",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "p.wouters@umcutrecht.nl",
+            "name": "Philippe C Wouters",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://crt.ecgx.ai",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-27T17:04:09.254521Z",
+    "license": "AGPL-3.0",
+    "name": "ECGxAI",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/EURHEARTJ/EHAC617",
+            "pmid": "36342291"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cardiology",
+            "uri": "http://edamontology.org/topic_3335"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Surgery",
+            "uri": "http://edamontology.org/topic_3421"
+        }
+    ]
+}
diff --git a/data/echtvar/echtvar.biotools.json b/data/echtvar/echtvar.biotools.json
new file mode 100644
index 0000000000000..9b7008d9d94dd
--- /dev/null
+++ b/data/echtvar/echtvar.biotools.json
@@ -0,0 +1,68 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-19T10:20:35.375199Z",
+    "biotoolsCURIE": "biotools:echtvar",
+    "biotoolsID": "echtvar",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Brent S. Pedersen",
+            "orcidid": "http://orcid.org/0000-0003-1786-2216"
+        },
+        {
+            "name": "Jeroen de Ridder",
+            "orcidid": "http://orcid.org/0000-0002-0828-3477"
+        }
+    ],
+    "description": "Compressed variant representation for rapid annotation and filtering of SNPs and indels.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genetic variation analysis",
+                    "uri": "http://edamontology.org/operation_3197"
+                },
+                {
+                    "term": "SNP annotation",
+                    "uri": "http://edamontology.org/operation_3661"
+                },
+                {
+                    "term": "Variant filtering",
+                    "uri": "http://edamontology.org/operation_3675"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/brentp/echtvar",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-19T10:20:35.377823Z",
+    "license": "MIT",
+    "name": "echtvar",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/nar/gkac931",
+            "pmid": "36300617"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        }
+    ]
+}
diff --git a/data/ecotranslearn/ecotranslearn.biotools.json b/data/ecotranslearn/ecotranslearn.biotools.json
new file mode 100644
index 0000000000000..f1037e03e9b10
--- /dev/null
+++ b/data/ecotranslearn/ecotranslearn.biotools.json
@@ -0,0 +1,92 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T01:13:11.464340Z",
+    "biotoolsCURIE": "biotools:ecotranslearn",
+    "biotoolsID": "ecotranslearn",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Guillaume.Wacquet@ifremer.fr",
+            "name": "Guillaume Wacquet",
+            "orcidid": "https://orcid.org/0000-0002-3325-5136",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alain Lefebvre"
+        }
+    ],
+    "description": "An R-package to easily use Transfer Learning for Ecological Studies. A plankton case study.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Ecological modelling",
+                    "uri": "http://edamontology.org/operation_3946"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/IFREMER-LERBL/EcoTransLearn",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-01-09T01:13:11.466989Z",
+    "license": "GPL-3.0",
+    "name": "EcoTransLearn",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC703",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: In recent years, Deep Learning (DL) has been increasingly used in many fields, in particular in image recognition, due to its ability to solve problems where traditional machine learning algorithms fail. However, building an appropriate DL model from scratch, especially in the context of ecological studies, is a difficult task due to the dynamic nature and morphological variability of living organisms, as well as the high cost in terms of time, human resources and skills required to label a large number of training images. To overcome this problem, Transfer Learning (TL) can be used to improve a classifier by transferring information learnt from many domains thanks to a very large training set composed of various images, to another domain with a smaller amount of training data. To compensate the lack of 'easy-to-use' software optimized for ecological studies, we propose the EcoTransLearn R-package, which allows greater automation in the classification of images acquired with various devices (FlowCam, ZooScan, photographs, etc.), thanks to different TL methods pre-trained on the generic ImageNet dataset. AVAILABILITY AND IMPLEMENTATION: EcoTransLearn is an open-source package. It is implemented in R and calls Python scripts for image classification step (using reticulate and tensorflow libraries). The source code, instruction manual and examples can be found at https://github.com/IFREMER-LERBL/EcoTransLearn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Lefebvre A."
+                    },
+                    {
+                        "name": "Wacquet G."
+                    }
+                ],
+                "date": "2022-12-13T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "EcoTransLearn: an R-package to easily use transfer learning for ecological studies-a plankton case study"
+            },
+            "pmid": "36282847"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Ecology",
+            "uri": "http://edamontology.org/topic_0610"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/edir/edir.biotools.json b/data/edir/edir.biotools.json
new file mode 100644
index 0000000000000..b19f606417b9d
--- /dev/null
+++ b/data/edir/edir.biotools.json
@@ -0,0 +1,118 @@
+{
+    "additionDate": "2023-02-20T16:58:39.212211Z",
+    "biotoolsCURIE": "biotools:edir",
+    "biotoolsID": "edir",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "alexander.gheldof@uzbrussel.be",
+            "name": "Alexander Gheldof",
+            "orcidid": "https://orcid.org/0000-0002-8320-1961",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "The Exome Database of Interspersed Repeats (EDIR) was developed to provide an overview of the positions of repetitive structures within the human genome composed of interspersed repeats encompassing a coding sequence.",
+    "download": [
+        {
+            "type": "Software package",
+            "url": "http://193.70.34.71/EDIR.tar.gz"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                },
+                {
+                    "term": "Repeat sequence analysis",
+                    "uri": "http://edamontology.org/operation_0237"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://193.70.34.71:3857/edir/",
+    "language": [
+        "Python",
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-20T16:58:39.214895Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/lauravongoc/EDIR"
+        }
+    ],
+    "name": "EDIR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC771",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Intragenic exonic deletions are known to contribute to genetic diseases and are often flanked by regions of homology. RESULTS: In order to get a more clear view of these interspersed repeats encompassing a coding sequence, we have developed EDIR (Exome Database of Interspersed Repeats) which contains the positions of these structures within the human exome. EDIR has been calculated by an inductive strategy, rather than by a brute force approach and can be queried through an R/Bioconductor package or a web interface allowing the per-gene rapid extraction of homology-flanked sequences throughout the exome. AVAILABILITY AND IMPLEMENTATION: The code used to compile EDIR can be found at https://github.com/lauravongoc/EDIR. The full dataset of EDIR can be queried via an Rshiny application at http://193.70.34.71:3857/edir/. The R package for querying EDIR is called 'EDIRquery' and is available on Bioconductor. The full EDIR dataset can be downloaded from https://osf.io/m3gvx/ or http://193.70.34.71/EDIR.tar.gz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Dohr K."
+                    },
+                    {
+                        "name": "Gheldof A."
+                    },
+                    {
+                        "name": "Olsen C."
+                    },
+                    {
+                        "name": "Osei R."
+                    },
+                    {
+                        "name": "Seneca S."
+                    },
+                    {
+                        "name": "Vo Ngoc L.D.T."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "EDIR: exome database of interspersed repeats"
+            },
+            "pmcid": "PMC9805566",
+            "pmid": "36453866"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Library",
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        }
+    ]
+}
diff --git a/data/edomics/edomics.biotools.json b/data/edomics/edomics.biotools.json
new file mode 100644
index 0000000000000..47711918a7903
--- /dev/null
+++ b/data/edomics/edomics.biotools.json
@@ -0,0 +1,139 @@
+{
+    "additionDate": "2023-01-28T11:24:50.168141Z",
+    "biotoolsCURIE": "biotools:edomics",
+    "biotoolsID": "edomics",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "bodong@ouc.edu.cn",
+            "name": "Bo Dong",
+            "orcidid": "https://orcid.org/0000-0003-1616-5363",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "liyuli@ouc.edu.cn",
+            "name": "Yuli Li",
+            "orcidid": "https://orcid.org/0000-0002-8112-1730",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "swang@ouc.edu.cn",
+            "name": "Shi Wang",
+            "orcidid": "https://orcid.org/0000-0002-9571-9864",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A comprehensive and comparative multi-omics database for animal evo-devo.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Transcriptome assembly",
+                    "uri": "http://edamontology.org/operation_3258"
+                },
+                {
+                    "term": "Weighted correlation network analysis",
+                    "uri": "http://edamontology.org/operation_3766"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://edomics.qnlm.ac",
+    "lastUpdate": "2023-01-28T11:24:50.171203Z",
+    "license": "Other",
+    "name": "EDomics",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC944",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Evolutionary developmental biology (evo-devo) has been among the most fascinating interdisciplinary fields for decades, which aims to elucidate the origin and evolution of diverse developmental processes. The rapid accumulation of omics data provides unprecedented opportunities to answer many interesting but unresolved evo-devo questions. However, the access and utilization of these resources are hindered by challenges particularly in non-model animals. Here, we establish a comparative multi-omics database for animal evo-devo (EDomics, http://edomics.qnlm.ac) containing comprehensive genomes, bulk transcriptomes, and single-cell data across 40 representative species, many of which are generally used as model organisms for animal evo-devo study. EDomics provides a systematic view of genomic/transcriptomic information from various aspects, including genome assembly statistics, gene features and families, transcription factors, transposable elements, and gene expressional profiles/networks. It also exhibits spatiotemporal gene expression profiles at a single-cell level, such as cell atlas, cell markers, and spatial-map information. Moreover, EDomics provides highly valuable, customized datasets/resources for evo-devo research, including gene family expansion/contraction, inferred core gene repertoires, macrosynteny analysis for karyotype evolution, and cell type evolution analysis. EDomics presents a comprehensive and comparative multi-omics platform for animal evo-devo community to decipher the whole history of developmental evolution across the tree of life.",
+                "authors": [
+                    {
+                        "name": "Dong B."
+                    },
+                    {
+                        "name": "Jia D."
+                    },
+                    {
+                        "name": "Jiang A."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Liu F."
+                    },
+                    {
+                        "name": "Liu P."
+                    },
+                    {
+                        "name": "Pu Z."
+                    },
+                    {
+                        "name": "Qiao J."
+                    },
+                    {
+                        "name": "Wang B."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Wei J."
+                    },
+                    {
+                        "name": "Zhang J."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "EDomics: a comprehensive and comparative multi-omics database for animal evo-devo"
+            },
+            "pmcid": "PMC9825439",
+            "pmid": "36318263"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Developmental biology",
+            "uri": "http://edamontology.org/topic_3064"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/efmsdti/efmsdti.biotools.json b/data/efmsdti/efmsdti.biotools.json
new file mode 100644
index 0000000000000..deedc80b2dd95
--- /dev/null
+++ b/data/efmsdti/efmsdti.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T01:07:21.921693Z",
+    "biotoolsCURIE": "biotools:efmsdti",
+    "biotoolsID": "efmsdti",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yyzhang1217@163.com",
+            "name": "Yuanyuan Zhang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Mengjie Wu"
+        },
+        {
+            "name": "Shudong Wang"
+        },
+        {
+            "name": "Wei Chen"
+        }
+    ],
+    "description": "Drug-target interaction prediction based on an efficient fusion of multi-source data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Subcellular localisation prediction",
+                    "uri": "http://edamontology.org/operation_2489"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/meng-jie/EFMSDTI",
+    "language": [
+        "MATLAB",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T01:07:21.924162Z",
+    "license": "Not licensed",
+    "name": "EFMSDTI",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FPHAR.2022.1009996",
+            "metadata": {
+                "abstract": "Copyright © 2022 Zhang, Wu, Wang and Chen.Accurate identification of Drug Target Interactions (DTIs) is of great significance for understanding the mechanism of drug treatment and discovering new drugs for disease treatment. Currently, computational methods of DTIs prediction that combine drug and target multi-source data can effectively reduce the cost and time of drug development. However, in multi-source data processing, the contribution of different source data to DTIs is often not considered. Therefore, how to make full use of the contribution of different source data to predict DTIs for efficient fusion is the key to improving the prediction accuracy of DTIs. In this paper, considering the contribution of different source data to DTIs prediction, a DTIs prediction approach based on an effective fusion of drug and target multi-source data is proposed, named EFMSDTI. EFMSDTI first builds 15 similarity networks based on multi-source information networks classified as topological and semantic graphs of drugs and targets according to their biological characteristics. Then, the multi-networks are fused by selective and entropy weighting based on similarity network fusion (SNF) according to their contribution to DTIs prediction. The deep neural networks model learns the embedding of low-dimensional vectors of drugs and targets. Finally, the LightGBM algorithm based on Gradient Boosting Decision Tree (GBDT) is used to complete DTIs prediction. Experimental results show that EFMSDTI has better performance (AUROC and AUPR are 0.982) than several state-of-the-art algorithms. Also, it has a good effect on analyzing the top 1000 prediction results, while 990 of the first 1000DTIs were confirmed. Code and data are available at https://github.com/meng-jie/EFMSDTI.",
+                "authors": [
+                    {
+                        "name": "Chen W."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Wu M."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    }
+                ],
+                "date": "2022-09-23T00:00:00Z",
+                "journal": "Frontiers in Pharmacology",
+                "title": "EFMSDTI: Drug-target interaction prediction based on an efficient fusion of multi-source data"
+            },
+            "pmcid": "PMC9538487",
+            "pmid": "36210804"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Drug development",
+            "uri": "http://edamontology.org/topic_3373"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Pharmacology",
+            "uri": "http://edamontology.org/topic_0202"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/eggnog/eggnog.biotools.json b/data/eggnog/eggnog.biotools.json
index de0bfe8dc2bbf..f280afbce62da 100644
--- a/data/eggnog/eggnog.biotools.json
+++ b/data/eggnog/eggnog.biotools.json
@@ -135,7 +135,7 @@
     "language": [
         "Python"
     ],
-    "lastUpdate": "2020-10-07T22:54:52Z",
+    "lastUpdate": "2023-01-28T11:27:56.183348Z",
     "license": "GPL-3.0",
     "maturity": "Mature",
     "name": "eggNOG",
@@ -191,7 +191,7 @@
                         "name": "Walter M.C."
                     }
                 ],
-                "citationCount": 992,
+                "citationCount": 1251,
                 "date": "2016-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "EGGNOG 4.5: A hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences"
@@ -199,6 +199,11 @@
             "pmcid": "PMC4702882",
             "pmid": "26582926"
         },
+        {
+            "doi": "10.1093/NAR/GKAC1022",
+            "pmcid": "PMC9825578",
+            "pmid": "36399505"
+        },
         {
             "metadata": {
                 "abstract": "Orthologous relationships form the basis of most comparative genomic and metagenomic studies and are essential for proper phylogenetic and functional analyses. The third version of the eggNOG database (http://eggnog.embl.de) contains nonsupervised orthologous groups constructed from 1133 organisms, doubling the number of genes with orthology assignment compared to eggNOG v2. The new release is the result of a number of improvements and expansions: (i) the underlying homology searches are now based on the SIMAP database; (ii) the orthologous groups have been extended to 41 levels of selected taxonomic ranges enabling much more fine-grained orthology assignments; and (iii) the newly designed web page is considerably faster with more functionality. In total, eggNOG v3 contains 721 801 orthologous groups, encompassing a total of 4 396 591 genes. Additionally, we updated 4873 and 4850 original COGs and KOGs, respectively, to include all 1133 organisms. At the universal level, covering all three domains of life, 101 208 orthologous groups are available, while the others are applicable at 40 more limited taxonomic ranges. Each group is amended by multiple sequence alignments and maximum-likelihood trees and broad functional descriptions are provided for 450 904 orthologous groups (62.5%). © The Author(s) 2011. Published by Oxford University Press.",
@@ -243,7 +248,7 @@
                         "name": "Von Mering C."
                     }
                 ],
-                "citationCount": 347,
+                "citationCount": 382,
                 "date": "2012-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "eggNOG v3.0: Orthologous groups covering 1133 organisms at 41 different taxonomic ranges"
@@ -276,7 +281,7 @@
                         "name": "von Mering C."
                     }
                 ],
-                "citationCount": 281,
+                "citationCount": 347,
                 "date": "2008-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "eggNOG: Automated construction and annotation of orthologous groups of genes"
@@ -321,7 +326,7 @@
                         "name": "Von Mering C."
                     }
                 ],
-                "citationCount": 167,
+                "citationCount": 175,
                 "date": "2009-11-07T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "eggNOG v2.0: Extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations"
@@ -372,7 +377,7 @@
                         "name": "Von Mering C."
                     }
                 ],
-                "citationCount": 343,
+                "citationCount": 394,
                 "date": "2014-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "EggNOG v4.0: Nested orthology inference across 3686 organisms"
@@ -420,7 +425,7 @@
                         "name": "Von Mering C."
                     }
                 ],
-                "citationCount": 373,
+                "citationCount": 1145,
                 "date": "2019-01-08T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "EggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses"
@@ -446,6 +451,6 @@
     ],
     "validated": 1,
     "version": [
-        "5.0"
+        "6.0"
     ]
 }
diff --git a/data/em-hiv/em-hiv.biotools.json b/data/em-hiv/em-hiv.biotools.json
new file mode 100644
index 0000000000000..334d1f0f5fdec
--- /dev/null
+++ b/data/em-hiv/em-hiv.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T01:02:05.663512Z",
+    "biotoolsCURIE": "biotools:em-hiv",
+    "biotoolsID": "em-hiv",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hupengwei@hotmail.com",
+            "name": "Pengwei Hu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhouxi@ms.xjb.ac.cn",
+            "name": "Xi Zhou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Lun Hu"
+        },
+        {
+            "name": "Zhenfeng Li"
+        }
+    ],
+    "description": "Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Protein cleavage site prediction",
+                    "uri": "http://edamontology.org/operation_0422"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/AllenV5/EM-HIV",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T01:02:05.666060Z",
+    "license": "Not licensed",
+    "name": "EM-HIV",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-04999-Y",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: The site information of substrates that can be cleaved by human immunodeficiency virus 1 proteases (HIV-1 PRs) is of great significance for designing effective inhibitors against HIV-1 viruses. A variety of machine learning-based algorithms have been developed to predict HIV-1 PR cleavage sites by extracting relevant features from substrate sequences. However, only relying on the sequence information is not sufficient to ensure a promising performance due to the uncertainty in the way of separating the datasets used for training and testing. Moreover, the existence of noisy data, i.e., false positive and false negative cleavage sites, could negatively influence the accuracy performance. Results: In this work, an ensemble learning algorithm for predicting HIV-1 PR cleavage sites, namely EM-HIV, is proposed by training a set of weak learners, i.e., biased support vector machine classifiers, with the asymmetric bagging strategy. By doing so, the impact of data imbalance and noisy data can thus be alleviated. Besides, in order to make full use of substrate sequences, the features used by EM-HIV are collected from three different coding schemes, including amino acid identities, chemical properties and variable-length coevolutionary patterns, for the purpose of constructing more relevant feature vectors of octamers. Experiment results on three independent benchmark datasets demonstrate that EM-HIV outperforms state-of-the-art prediction algorithm in terms of several evaluation metrics. Hence, EM-HIV can be regarded as a useful tool to accurately predict HIV-1 PR cleavage sites.",
+                "authors": [
+                    {
+                        "name": "Hu L."
+                    },
+                    {
+                        "name": "Hu P."
+                    },
+                    {
+                        "name": "Li Z."
+                    },
+                    {
+                        "name": "Tang Z."
+                    },
+                    {
+                        "name": "Zhao C."
+                    },
+                    {
+                        "name": "Zhou X."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach"
+            },
+            "pmcid": "PMC9608884",
+            "pmid": "36303135"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Laboratory techniques",
+            "uri": "http://edamontology.org/topic_3361"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/emati/emati.biotools.json b/data/emati/emati.biotools.json
new file mode 100644
index 0000000000000..a4365f6edbf4c
--- /dev/null
+++ b/data/emati/emati.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T17:03:25.001529Z",
+    "biotoolsCURIE": "biotools:emati",
+    "biotoolsID": "emati",
+    "confidence_flag": "tool",
+    "cost": "Free of charge (with restrictions)",
+    "credit": [
+        {
+            "email": "michael.schroeder@tu-dresden.de",
+            "name": "Michael Schroeder",
+            "orcidid": "https://orcid.org/0000-0003-2848-6949",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A recommender system for biomedical literature based on supervised learning.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Sorting",
+                    "uri": "http://edamontology.org/operation_3802"
+                },
+                {
+                    "term": "Text annotation",
+                    "uri": "http://edamontology.org/operation_3778"
+                },
+                {
+                    "term": "Text mining",
+                    "uri": "http://edamontology.org/operation_0306"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://emati.biotec.tu-dresden.de",
+    "language": [
+        "JavaScript",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-20T17:03:25.004261Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/bioinfcollab/emati"
+        }
+    ],
+    "name": "Emati",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/DATABASE/BAAC104",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press.The scientific literature continues to grow at an ever-increasing rate. Considering that thousands of new articles are published every week, it is obvious how challenging it is to keep up with newly published literature on a regular basis. Using a recommender system that improves the user experience in the online environment can be a solution to this problem. In the present study, we aimed to develop a web-based article recommender service, called Emati. Since the data are text-based by nature and we wanted our system to be independent of the number of users, a content-based approach has been adopted in this study. A supervised machine learning model has been proposed to generate article recommendations. Two different supervised learning approaches, namely the naïve Bayes model with Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer and the state-of-The-Art language model bidirectional encoder representations from transformers (BERT), have been implemented. In the first one, a list of documents is converted into TF-IDF-weighted features and fed into a classifier to distinguish relevant articles from irrelevant ones. Multinomial naïve Bayes algorithm is used as a classifier since, along with the class label, it also gives the probability that the input belongs to this class. The second approach is based on fine-Tuning the pretrained state-of-The-Art language model BERT for the text classification task. Emati provides a weekly updated list of article recommendations and presents it to the user, sorted by probability scores. New article recommendations are also sent to users' email addresses on a weekly basis. Additionally, Emati has a personalized search feature to search online services' (such as PubMed and arXiv) content and have the results sorted by the user's classifier. Database URL: https://emati.biotec.tu-dresden.de",
+                "authors": [
+                    {
+                        "name": "Kart O."
+                    },
+                    {
+                        "name": "Kwasnicki R."
+                    },
+                    {
+                        "name": "Lachmann K."
+                    },
+                    {
+                        "name": "Mestiashvili A."
+                    },
+                    {
+                        "name": "Schroeder M."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Database",
+                "title": "Emati: A recommender system for biomedical literature based on supervised learning"
+            },
+            "pmcid": "PMC9732843",
+            "pmid": "36484479"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biological databases",
+            "uri": "http://edamontology.org/topic_3071"
+        },
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/ematlas/ematlas.biotools.json b/data/ematlas/ematlas.biotools.json
new file mode 100644
index 0000000000000..be0ffec48664c
--- /dev/null
+++ b/data/ematlas/ematlas.biotools.json
@@ -0,0 +1,82 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T00:56:36.124520Z",
+    "biotoolsCURIE": "biotools:ematlas",
+    "biotoolsID": "ematlas",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yczuo@imu.edu.cn",
+            "name": "Yongchun Zuo",
+            "orcidid": "https://orcid.org/0000-0002-6065-7835",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xingyongqiang1984@163.com",
+            "name": "Yongqiang Xing",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Pengfei Liang"
+        },
+        {
+            "name": "Lei Zheng",
+            "orcidid": "https://orcid.org/0000-0002-8531-6949"
+        }
+    ],
+    "description": "A comprehensive atlas for exploring spatiotemporal activation in mammalian embryogenesis.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bioinfor.imu.edu.cn/ematlas",
+    "lastUpdate": "2023-01-09T00:56:36.126977Z",
+    "name": "EmAtlas",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC848",
+            "pmid": "36189903"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Regenerative medicine",
+            "uri": "http://edamontology.org/topic_3395"
+        }
+    ]
+}
diff --git a/data/emli-icc/emli-icc.biotools.json b/data/emli-icc/emli-icc.biotools.json
new file mode 100644
index 0000000000000..fe4252172e6ce
--- /dev/null
+++ b/data/emli-icc/emli-icc.biotools.json
@@ -0,0 +1,98 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T00:51:57.001474Z",
+    "biotoolsCURIE": "biotools:emli-icc",
+    "biotoolsID": "emli-icc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "fanlj@zju.edu.cn",
+            "name": "Longjiang Fan",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "yifeishen@zju.edu.cn",
+            "name": "Peng Zhao",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhaop@zju.edu.cn",
+            "name": "Yifei Shen",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jian Ruan"
+        }
+    ],
+    "description": "An ensemble machine learning-based integration algorithm for metastasis prediction and risk stratification in intrahepatic cholangiocarcinoma.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene symbol",
+                        "uri": "http://edamontology.org/data_1026"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Gene prediction",
+                    "uri": "http://edamontology.org/operation_2454"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://ibi.zju.edu.cn/EMLI/",
+    "lastUpdate": "2023-01-09T00:51:57.003869Z",
+    "name": "EMLI-ICC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC450",
+            "pmid": "36259363"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/endecon/endecon.biotools.json b/data/endecon/endecon.biotools.json
new file mode 100644
index 0000000000000..245d5ef73e282
--- /dev/null
+++ b/data/endecon/endecon.biotools.json
@@ -0,0 +1,141 @@
+{
+    "additionDate": "2023-02-20T17:11:29.353190Z",
+    "biotoolsCURIE": "biotools:endecon",
+    "biotoolsID": "endecon",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "zhangxf@ccnu.edu.cn",
+            "name": "Xiao-Fei Zhang",
+            "orcidid": "https://orcid.org/0000-0002-5052-9725",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Cell type deconvolution of spatially resolved transcriptomics data via ensemble learning.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deisotoping",
+                    "uri": "http://edamontology.org/operation_3629"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Zhangxf-ccnu/EnDecon",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-02-20T17:11:29.355834Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/SunXQlab/EnDecon"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/SunXQlab/ST-deconvoulution"
+        }
+    ],
+    "name": "EnDecon",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC805",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The rapid development of spatial transcriptomics (ST) approaches has provided new insights into understanding tissue architecture and function. However, the gene expressions measured at a spot may contain contributions from multiple cells due to the low-resolution of current ST technologies. Although many computational methods have been developed to disentangle discrete cell types from spatial mixtures, the community lacks a thorough evaluation of the performance of those deconvolution methods. RESULTS: Here, we present a comprehensive benchmarking of 14 deconvolution methods on four datasets. Furthermore, we investigate the robustness of different methods to sequencing depth, spot size and the choice of normalization. Moreover, we propose a new ensemble learning-based deconvolution method (EnDecon) by integrating multiple individual methods for more accurate deconvolution. The major new findings include: (i) cell2loction, RCTD and spatialDWLS are more accurate than other ST deconvolution methods, based on the evaluation of three metrics: RMSE, PCC and JSD; (ii) cell2location and spatialDWLS are more robust to the variation of sequencing depth than RCTD; (iii) the accuracy of the existing methods tends to decrease as the spot size becomes smaller; (iv) most deconvolution methods perform best when they normalize ST data using the method described in their original papers; and (v) the integrative method, EnDecon, could achieve more accurate ST deconvolution. Our study provides valuable information and guideline for practically applying ST deconvolution tools and developing new and more effective methods. AVAILABILITY AND IMPLEMENTATION: The benchmarking pipeline is available at https://github.com/SunXQlab/ST-deconvoulution. An R package for EnDecon is available at https://github.com/SunXQlab/EnDecon. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Sun X."
+                    },
+                    {
+                        "name": "Yan L."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Benchmarking and integration of methods for deconvoluting spatial transcriptomic data"
+            },
+            "pmcid": "PMC9825747",
+            "pmid": "36515467",
+            "type": [
+                "Benchmarking study"
+            ]
+        },
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC825",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Spatially resolved gene expression profiles are the key to exploring the cell type spatial distributions and understanding the architecture of tissues. Many spatially resolved transcriptomics (SRT) techniques do not provide single-cell resolutions, but they measure gene expression profiles on captured locations (spots) instead, which are mixtures of potentially heterogeneous cell types. Currently, several cell-type deconvolution methods have been proposed to deconvolute SRT data. Due to the different model strategies of these methods, their deconvolution results also vary. RESULTS: Leveraging the strengths of multiple deconvolution methods, we introduce a new weighted ensemble learning deconvolution method, EnDecon, to predict cell-type compositions on SRT data in this work. EnDecon integrates multiple base deconvolution results using a weighted optimization model to generate a more accurate result. Simulation studies demonstrate that EnDecon outperforms the competing methods and the learned weights assigned to base deconvolution methods have high positive correlations with the performances of these base methods. Applied to real datasets from different spatial techniques, EnDecon identifies multiple cell types on spots, localizes these cell types to specific spatial regions and distinguishes distinct spatial colocalization and enrichment patterns, providing valuable insights into spatial heterogeneity and regionalization of tissues. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/Zhangxf-ccnu/EnDecon. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Li H.-S."
+                    },
+                    {
+                        "name": "Tu J.-J."
+                    },
+                    {
+                        "name": "Yan H."
+                    },
+                    {
+                        "name": "Zhang X.-F."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "EnDecon: cell type deconvolution of spatially resolved transcriptomics data via ensemble learning"
+            },
+            "pmcid": "PMC9825263",
+            "pmid": "36548388",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/endhic/endhic.biotools.json b/data/endhic/endhic.biotools.json
new file mode 100644
index 0000000000000..0d9861be5a916
--- /dev/null
+++ b/data/endhic/endhic.biotools.json
@@ -0,0 +1,127 @@
+{
+    "additionDate": "2023-02-20T17:17:04.822580Z",
+    "biotoolsCURIE": "biotools:endhic",
+    "biotoolsID": "endhic",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "fanwei@caas.cn",
+            "name": "Wei Fan",
+            "orcidid": "https://orcid.org/0000-0001-5036-8733",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "milrazhang@163.com",
+            "name": "Yan Zhang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "EndHic is a fast and easy-to-use Hi-C scaffolding tool, using the Hi-C links from contig end regions instead of whole contig regions to assemble large contigs into chromosomal-level scaffolds.",
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://github.com/fanagislab/EndHiC/tree/master/z.testing_data"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome assembly",
+                    "uri": "http://edamontology.org/operation_0525"
+                },
+                {
+                    "term": "Mapping assembly",
+                    "uri": "http://edamontology.org/operation_0523"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/fanagislab/EndHiC",
+    "language": [
+        "Perl",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-20T17:17:04.825146Z",
+    "license": "GPL-1.0",
+    "name": "EndHiC",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05087-X",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: The application of PacBio HiFi and ultra-long ONT reads have enabled huge progress in the contig-level assembly, but it is still challenging to assemble large contigs into chromosomes with available Hi-C scaffolding tools, which count Hi-C links between contigs using the whole or a large part of contig regions. As the Hi-C links of two adjacent contigs concentrate only at the neighbor ends of the contigs, larger contig size will reduce the power to differentiate adjacent (signal) and non-adjacent (noise) contig linkages, leading to a higher rate of mis-assembly. Results: We design and develop a novel Hi-C based scaffolding tool EndHiC, which is suitable to assemble large contigs into chromosomal-level scaffolds. The core idea behind EndHiC, which distinguishes it from other Hi-C scaffolding tools, is using Hi-C links only from the most effective regions of contig ends. By this way, the signal neighbor contig linkages and noise non-neighbor contig linkages are separated more clearly. Benefiting from the increased signal to noise ratio, the reciprocal best requirement, as well as the robustness evaluation, EndHiC achieves higher accuracy for scaffolding large contigs compared to existing tools. EndHiC has been successfully applied in the Hi-C scaffolding of simulated data from human, rice and Arabidopsis, and real data from human, great burdock, water spinach, chicory, endive, yacon, and Ipomoea cairica, suggesting that EndHiC can be applied to a broad range of plant and animal genomes. Conclusions: EndHiC is a novel Hi-C scaffolding tool, which is suitable for scaffolding of contig assemblies with contig N50 size near or over 10 Mb and N90 size near or over 1 Mb. EndHiC is efficient both in time and memory, and it is interface-friendly to the users. As more genome projects have been launched and the contig continuity constantly improved, we believe EndHiC has the potential to make a great contribution to the genomics field and liberate the scientists from labor-intensive manual curation works.",
+                "authors": [
+                    {
+                        "name": "Fan W."
+                    },
+                    {
+                        "name": "Jiang F."
+                    },
+                    {
+                        "name": "Liu H."
+                    },
+                    {
+                        "name": "Wang A."
+                    },
+                    {
+                        "name": "Wang H."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Xu D."
+                    },
+                    {
+                        "name": "Yang B."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zhao H."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "EndHiC: assemble large contigs into chromosome-level scaffolds using the Hi-C links from contig ends"
+            },
+            "pmcid": "PMC9730666",
+            "pmid": "36482318"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/ensemblesplice/ensemblesplice.biotools.json b/data/ensemblesplice/ensemblesplice.biotools.json
new file mode 100644
index 0000000000000..adb3521eeb64b
--- /dev/null
+++ b/data/ensemblesplice/ensemblesplice.biotools.json
@@ -0,0 +1,101 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T00:47:43.598302Z",
+    "biotoolsCURIE": "biotools:ensemblesplice",
+    "biotoolsID": "ensemblesplice",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ooluwada@uccs.edu",
+            "name": "Oluwatosin Oluwadare",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Trevor Martin"
+        },
+        {
+            "name": "Victor Akpokiro"
+        }
+    ],
+    "description": "Ensemble deep learning model for splice site prediction.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Sequence merging",
+                    "uri": "http://edamontology.org/operation_0232"
+                },
+                {
+                    "term": "Splice site prediction",
+                    "uri": "http://edamontology.org/operation_0433"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/OluwadareLab/EnsembleSplice",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T00:47:43.600825Z",
+    "license": "Not licensed",
+    "name": "EnsembleSplice",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-04971-W",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Identifying splice site regions is an important step in the genomic DNA sequencing pipelines of biomedical and pharmaceutical research. Within this research purview, efficient and accurate splice site detection is highly desirable, and a variety of computational models have been developed toward this end. Neural network architectures have recently been shown to outperform classical machine learning approaches for the task of splice site prediction. Despite these advances, there is still considerable potential for improvement, especially regarding model prediction accuracy, and error rate. Results: Given these deficits, we propose EnsembleSplice, an ensemble learning architecture made up of four (4) distinct convolutional neural networks (CNN) model architecture combination that outperform existing splice site detection methods in the experimental evaluation metrics considered including the accuracies and error rates. We trained and tested a variety of ensembles made up of CNNs and DNNs using the five-fold cross-validation method to identify the model that performed the best across the evaluation and diversity metrics. As a result, we developed our diverse and highly effective splice site (SS) detection model, which we evaluated using two (2) genomic Homo sapiens datasets and the Arabidopsis thaliana dataset. The results showed that for of the Homo sapiens EnsembleSplice achieved accuracies of 94.16% for one of the acceptor splice sites and 95.97% for donor splice sites, with an error rate for the same Homo sapiens dataset, 4.03% for the donor splice sites and 5.84% for the acceptor splice sites datasets. Conclusions: Our five-fold cross validation ensured the prediction accuracy of our models are consistent. For reproducibility, all the datasets used, models generated, and results in our work are publicly available in our GitHub repository here: https://github.com/OluwadareLab/EnsembleSplice",
+                "authors": [
+                    {
+                        "name": "Akpokiro V."
+                    },
+                    {
+                        "name": "Martin T."
+                    },
+                    {
+                        "name": "Oluwadare O."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "EnsembleSplice: ensemble deep learning model for splice site prediction"
+            },
+            "pmcid": "PMC9535948",
+            "pmid": "36203144"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/entail/entail.biotools.json b/data/entail/entail.biotools.json
new file mode 100644
index 0000000000000..b4d6af264dc7e
--- /dev/null
+++ b/data/entail/entail.biotools.json
@@ -0,0 +1,96 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T19:27:57.834423Z",
+    "biotoolsCURIE": "biotools:entail",
+    "biotoolsID": "entail",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "aauriemmacitarella@unisa.it",
+            "name": "Alessia Auriemma Citarella",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "ENTAIL, for the prediction of fibril deposits involved in the amyloidoses. It was developed using over than 4000 molecular descriptors.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/luigidibiasi/ENTAIL",
+    "language": [
+        "Perl"
+    ],
+    "lastUpdate": "2023-02-20T19:27:57.837052Z",
+    "license": "Not licensed",
+    "name": "ENTAIL",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05070-6",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: This research aims to increase our knowledge of amyloidoses. These disorders cause incorrect protein folding, affecting protein functionality (on structure). Fibrillar deposits are the basis of some wellknown diseases, such as Alzheimer, Creutzfeldt–Jakob diseases and type II diabetes. For many of these amyloid proteins, the relative precursors are known. Discovering new protein precursors involved in forming amyloid fibril deposits would improve understanding the pathological processes of amyloidoses. Results: A new classifier, called ENTAIL, was developed using over than 4000 molecular descriptors. ENTAIL was based on the Naive Bayes Classifier with Unbounded Support and Gaussian Kernel Type, with an accuracy on the test set of 81.80%, SN of 100%, SP of 63.63% and an MCC of 0.683 on a balanced dataset. Conclusions: The analysis carried out has demonstrated how, despite the various configurations of the tests, performances are superior in terms of performance on a balanced dataset.",
+                "authors": [
+                    {
+                        "name": "Auriemma Citarella A."
+                    },
+                    {
+                        "name": "De Marco F."
+                    },
+                    {
+                        "name": "Di Biasi L."
+                    },
+                    {
+                        "name": "Tortora G."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "ENTAIL: yEt aNoTher amyloid fIbrils cLassifier"
+            },
+            "pmcid": "PMC9714056",
+            "pmid": "36456900"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Electron microscopy",
+            "uri": "http://edamontology.org/topic_0611"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Protein folding, stability and design",
+            "uri": "http://edamontology.org/topic_0130"
+        },
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/envemind/envemind.biotools.json b/data/envemind/envemind.biotools.json
new file mode 100644
index 0000000000000..a28b7ce66bd09
--- /dev/null
+++ b/data/envemind/envemind.biotools.json
@@ -0,0 +1,113 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T00:37:28.128249Z",
+    "biotoolsCURIE": "biotools:envemind",
+    "biotoolsID": "envemind",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "pmradzinski@mimuw.edu.pl",
+            "name": "Piotr Radziński",
+            "orcidid": "https://orcid.org/0000-0001-5107-7487",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Anna Gambin"
+        },
+        {
+            "name": "Dirk Valkenborg"
+        },
+        {
+            "name": "Michał Piotr Startek",
+            "orcidid": "https://orcid.org/0000-0001-5227-3447"
+        }
+    ],
+    "description": "Accurate Monoisotopic Mass Determination Based On Isotopic Envelope.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deisotoping",
+                    "uri": "http://edamontology.org/operation_3629"
+                },
+                {
+                    "term": "Protein identification",
+                    "uri": "http://edamontology.org/operation_3767"
+                },
+                {
+                    "term": "Spectrum calculation",
+                    "uri": "http://edamontology.org/operation_3860"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/PiotrRadzinski/envemind",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-09T00:37:28.131465Z",
+    "license": "MIT",
+    "name": "envemind",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/JASMS.2C00176",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society.Nowadays, monoisotopic mass is used as an important feature in top-down proteomics. Knowing the exact monoisotopic mass is helpful for precise and quick protein identification in large protein databases. However, only in spectra of small molecules the monoisotopic peak is visible. For bigger molecules like proteins, it is hidden in noise or undetected at all, and therefore its position has to be predicted. By improving the prediction of the peak, we contribute to a more accurate identification of molecules, which is crucial in fields such as chemistry and medicine. In this work, we present the envemind algorithm, which is a two-step procedure to predict monoisotopic masses of proteins. The prediction is based on an isotopic envelope. Therefore, envemind is dedicated to spectra where we are able to resolve the one dalton separated isotopic variants. Furthermore, only single-molecule spectra are allowed, that is, spectra that do not require prior deconvolution. The algorithm deals with the problem of off-by-one dalton errors, which are common in monoisotopic mass prediction. A novel aspect of this work is a mathematical exploration of the space of molecules, where we equate chemical formulas and their theoretical spectrum. Since the space of molecules consists of all possible chemical formulas, this approach is not limited to known substances only. This makes optimization processes faster and enables to approximate theoretical spectrum for a given experimental one. The algorithm is available as a Python package envemind on our GitHub page https://github.com/PiotrRadzinski/envemind.",
+                "authors": [
+                    {
+                        "name": "Gambin A."
+                    },
+                    {
+                        "name": "Radzinski P."
+                    },
+                    {
+                        "name": "Startek M.P."
+                    },
+                    {
+                        "name": "Valkenborg D."
+                    }
+                ],
+                "date": "2022-11-02T00:00:00Z",
+                "journal": "Journal of the American Society for Mass Spectrometry",
+                "title": "Envemind: Accurate Monoisotopic Mass Determination Based on Isotopic Envelope"
+            },
+            "pmcid": "PMC9634886",
+            "pmid": "36223196"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Chemistry",
+            "uri": "http://edamontology.org/topic_3314"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
+}
diff --git a/data/epimutacions/epimutacions.biotools.json b/data/epimutacions/epimutacions.biotools.json
new file mode 100644
index 0000000000000..61c321bfbc4b5
--- /dev/null
+++ b/data/epimutacions/epimutacions.biotools.json
@@ -0,0 +1,102 @@
+{
+    "additionDate": "2023-02-07T14:25:10.924789Z",
+    "biotoolsCURIE": "biotools:epimutacions",
+    "biotoolsID": "epimutacions",
+    "credit": [
+        {
+            "email": "carles.hernandez@isglobal.org",
+            "name": "Carles Hernandez-Ferrer",
+            "orcidid": "https://orcid.org/0000-0002-8029-7160",
+            "url": "http://www.carleshf.com"
+        },
+        {
+            "email": "carlos.ruiz@isglobal.org",
+            "name": "Carlos Ruiz-Arenas",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Developer"
+            ]
+        },
+        {
+            "email": "dolors.pelegri@isglobal.org",
+            "name": "Dolors Pelegri-Siso",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Maintainer"
+            ]
+        },
+        {
+            "email": "leire.abarrategui@isglobal.org",
+            "name": "Leire Abarrategui",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Developer"
+            ]
+        },
+        {
+            "email": "juanr.gonzalez@isglobal.org",
+            "name": "Juan R. Gonzalez",
+            "url": "https://brge.isglobal.org/"
+        }
+    ],
+    "description": "The package includes some statistical outlier detection methods for epimutations detection in DNA methylation data. The methods included in the package are MANOVA, Multivariate linear models, isolation forest, robust mahalanobis distance, quantile and beta. The methods compare a case sample with a suspected disease against a reference panel (composed of healthy individuals) to identify epimutations in the given case sample. It also contains functions to annotate and visualize the identified epimutations.",
+    "documentation": [
+        {
+            "type": [
+                "API documentation"
+            ],
+            "url": "https://www.bioconductor.org/packages/release/bioc/manuals/epimutacions/man/epimutacions.pdf"
+        },
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://www.bioconductor.org/packages/release/bioc/vignettes/epimutacions/inst/doc/epimutacions.html"
+        }
+    ],
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://www.bioconductor.org/packages/release/bioc/src/contrib/epimutacions_1.2.0.tar.gz",
+            "version": "1.2.0"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Analysis",
+                    "uri": "http://edamontology.org/operation_2945"
+                },
+                {
+                    "term": "Annotation",
+                    "uri": "http://edamontology.org/operation_0226"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.bioconductor.org/packages/release/bioc/html/epimutacions.html",
+    "lastUpdate": "2023-02-07T14:25:10.927150Z",
+    "link": [
+        {
+            "type": [
+                "Mirror"
+            ],
+            "url": "https://www.bioconductor.org/packages/epimutacions"
+        }
+    ],
+    "name": "epimutacions",
+    "owner": "chernan3",
+    "topic": [
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        }
+    ],
+    "version": [
+        "1.2.0"
+    ]
+}
diff --git a/data/epiphany/epiphany.biotools.json b/data/epiphany/epiphany.biotools.json
new file mode 100644
index 0000000000000..1316e712e834c
--- /dev/null
+++ b/data/epiphany/epiphany.biotools.json
@@ -0,0 +1,95 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-09T00:29:02.574112Z",
+    "biotoolsCURIE": "biotools:epiphany",
+    "biotoolsID": "epiphany",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "scott.napper@usask.ca",
+            "name": "Scott Napper",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Anthony J Kusalik"
+        },
+        {
+            "name": "Antonio Facciuolo"
+        },
+        {
+            "name": "Zoe Parker Cates"
+        }
+    ],
+    "description": "Platform for Analysis and Visualization of Peptide Immunoarray Data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dendrogram visualisation",
+                    "uri": "http://edamontology.org/operation_2938"
+                },
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://epiphany.usask.ca/epiphany/",
+    "lastUpdate": "2023-01-09T00:29:02.577571Z",
+    "name": "EPIphany",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FBINF.2021.694324",
+            "pmcid": "PMC9581008",
+            "pmid": "36303765"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Allergy, clinical immunology and immunotherapeutics",
+            "uri": "http://edamontology.org/topic_3400"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/epitope-evaluator/epitope-evaluator.biotools.json b/data/epitope-evaluator/epitope-evaluator.biotools.json
new file mode 100644
index 0000000000000..85d26a74748be
--- /dev/null
+++ b/data/epitope-evaluator/epitope-evaluator.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T15:21:47.360474Z",
+    "biotoolsCURIE": "biotools:epitope-evaluator",
+    "biotoolsID": "epitope-evaluator",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "drequena@rockefeller.edu",
+            "name": "David Requena",
+            "orcidid": "http://orcid.org/0000-0002-5968-1133",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "fuxman@bu.edu",
+            "name": "Juan Ignacio Fuxman Bass",
+            "orcidid": "http://orcid.org/0000-0001-9457-1207",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "luis.soto8@unmsm.edu.pe",
+            "name": "Luis Fernando Soto",
+            "orcidid": "http://orcid.org/0000-0002-6348-6437",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An interactive web application to study predicted T-cell epitopes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                },
+                {
+                    "term": "Side chain modelling",
+                    "uri": "http://edamontology.org/operation_0480"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://fuxmanlab.shinyapps.io/Epitope-Evaluator/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-26T15:21:47.363079Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/SotoLF/Epitope-Evaluator"
+        }
+    ],
+    "name": "Epitope-Evaluator",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/journal.pone.0273577",
+            "metadata": {
+                "abstract": "© 2022 Soto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Multiple immunoinformatic tools have been developed to predict T-cell epitopes from protein amino acid sequences for different major histocompatibility complex (MHC) alleles. These prediction tools output hundreds of potential peptide candidates which require further processing; however, these tools are either not graphical or not friendly for non-programming users. We present Epitope-Evaluator, a web tool developed in the Shiny/R framework to interactively analyze predicted T-cell epitopes. Epitope-Evaluator contains six tools providing the distribution of epitopes across a selected set of MHC alleles, the promiscuity and conservation of epitopes, and their density and location within antigens. Epitope-Evaluator requires as input the fasta file of protein sequences and the output prediction file coming out from any predictor. By choosing different cutoffs and parameters, users can produce several interactive plots and tables that can be downloaded as JPG and text files, respectively. Using Epitope-Evaluator, we found the HLA-B*40, HLA-B*27:05 and HLA-B*07:02 recognized fewer epitopes from the SARS-CoV-2 proteome than other MHC Class I alleles. We also identified shared epitopes between Delta, Omicron, and Wuhan Spike variants as well as variant-specific epitopes. In summary, Epitope-Evaluator removes the programming barrier and provides intuitive tools, allowing a straightforward interpretation and graphical representations that facilitate the selection of candidate epitopes for experimental evaluation. The web server Epitope-Evaluator is available at https://fuxmanlab.shinyapps.io/EpitopeEvaluator/",
+                "authors": [
+                    {
+                        "name": "Bass J.I.F."
+                    },
+                    {
+                        "name": "Requena D."
+                    },
+                    {
+                        "name": "Soto L.F."
+                    }
+                ],
+                "citationCount": 2,
+                "date": "2022-08-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "Epitope-Evaluator: An interactive web application to study predicted T-cell epitopes"
+            },
+            "pmcid": "PMC9417011",
+            "pmid": "36018887"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Immunogenetics",
+            "uri": "http://edamontology.org/topic_3930"
+        },
+        {
+            "term": "Immunoinformatics",
+            "uri": "http://edamontology.org/topic_3948"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Vaccinology",
+            "uri": "http://edamontology.org/topic_3966"
+        }
+    ]
+}
diff --git a/data/esmc/esmc.biotools.json b/data/esmc/esmc.biotools.json
new file mode 100644
index 0000000000000..8eba4ae110b62
--- /dev/null
+++ b/data/esmc/esmc.biotools.json
@@ -0,0 +1,122 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T19:30:46.789518Z",
+    "biotoolsCURIE": "biotools:esmc",
+    "biotoolsID": "esmc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "wangcf1967@163.com",
+            "name": "Changfa Wang",
+            "orcidid": "https://orcid.org/0000-0002-5186-1703",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "shuaicli@cityu.edu.hk",
+            "name": "Shuaicheng Li",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A statistical model to infer admixture events from individual genomics data.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Statistical inference",
+                    "uri": "http://edamontology.org/operation_3658"
+                },
+                {
+                    "term": "Statistical modelling",
+                    "uri": "http://edamontology.org/operation_3664"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/zachary-zzc/eSMC",
+    "language": [
+        "C",
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-20T19:30:46.792151Z",
+    "license": "MIT",
+    "name": "eSMC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12864-022-09033-2",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Inferring historical population admixture events yield essential insights in understanding a species demographic history. Methods are available to infer admixture events in demographic history with extant genetic data from multiple sources. Due to the deficiency in ancient population genetic data, there lacks a method for admixture inference from a single source. Pairwise Sequentially Markovian Coalescent (PSMC) estimates the historical effective population size from lineage genomes of a single individual, based on the distribution of the most recent common ancestor between the diploid’s alleles. However, PSMC does not infer the admixture event. Results: Here, we proposed eSMC, an extended PSMC model for admixture inference from a single source. We evaluated our model’s performance on both in silico data and real data. We simulated population admixture events at an admixture time range from 5 kya to 100 kya (5 years/generation) with population admix ratio at 1:1, 2:1, 3:1, and 4:1, respectively. The root means the square error is ± 7.61 kya for all experiments. Then we implemented our method to infer the historical admixture events in human, donkey and goat populations. The estimated admixture time for both Han and Tibetan individuals range from 60 kya to 80 kya (25 years/generation), while the estimated admixture time for the domesticated donkeys and the goats ranged from 40 kya to 60 kya (8 years/generation) and 40 kya to 100 kya (6 years/generation), respectively. The estimated admixture times were concordance to the time that domestication occurred in human history. Conclusion: Our eSMC effectively infers the time of the most recent admixture event in history from a single individual’s genomics data. The source code of eSMC is hosted at https://github.com/zachary-zzc/eSMC.",
+                "authors": [
+                    {
+                        "name": "Chen L."
+                    },
+                    {
+                        "name": "Li S."
+                    },
+                    {
+                        "name": "Miao X."
+                    },
+                    {
+                        "name": "Qian X."
+                    },
+                    {
+                        "name": "Wang C."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Zhao Z."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Genomics",
+                "title": "eSMC: a statistical model to infer admixture events from individual genomics data"
+            },
+            "pmcid": "PMC9748406",
+            "pmid": "36517735"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Population genetics",
+            "uri": "http://edamontology.org/topic_3056"
+        },
+        {
+            "term": "Population genomics",
+            "uri": "http://edamontology.org/topic_3796"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/espaloma/espaloma.biotools.json b/data/espaloma/espaloma.biotools.json
new file mode 100644
index 0000000000000..939010beb532c
--- /dev/null
+++ b/data/espaloma/espaloma.biotools.json
@@ -0,0 +1,129 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T11:33:39.723315Z",
+    "biotoolsCURIE": "biotools:espaloma",
+    "biotoolsID": "espaloma",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "John D. Chodera",
+            "orcidid": "https://orcid.org/0000-0003-0542-119X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Yuanqing Wang",
+            "orcidid": "https://orcid.org/0000-0003-4403-2015",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "End-to-end differentiable construction of molecular mechanics force fields.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Forcefield parameterisation",
+                    "uri": "http://edamontology.org/operation_3893"
+                },
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/choderalab/espaloma",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T11:33:39.726083Z",
+    "license": "MIT",
+    "name": "espaloma",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1039/D2SC02739A",
+            "metadata": {
+                "abstract": "© 2022 The Royal Society of Chemistry.Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process—spanning chemical perception to parameter assignment—is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with low inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field (“Parsley”) openff-1.2.0 small molecule force field augmented with a peptide dataset, the resulting espaloma model shows superior accuracy vis-á-vis experiments in computing relative alchemical free energy calculations for a popular benchmark. This approach is implemented in the free and open source package espaloma, available at https://github.com/choderalab/espaloma.",
+                "authors": [
+                    {
+                        "name": "Bruce Macdonald H.E."
+                    },
+                    {
+                        "name": "Chodera J.D."
+                    },
+                    {
+                        "name": "Fass J."
+                    },
+                    {
+                        "name": "Henry M."
+                    },
+                    {
+                        "name": "Herr J.E."
+                    },
+                    {
+                        "name": "Kaminow B."
+                    },
+                    {
+                        "name": "Pulido I."
+                    },
+                    {
+                        "name": "Rufa D."
+                    },
+                    {
+                        "name": "Takaba K."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Zhang I."
+                    }
+                ],
+                "citationCount": 3,
+                "date": "2022-09-08T00:00:00Z",
+                "journal": "Chemical Science",
+                "title": "End-to-end differentiable construction of molecular mechanics force fields"
+            },
+            "pmcid": "PMC9600499",
+            "pmid": "36349096"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Chemistry",
+            "uri": "http://edamontology.org/topic_3314"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Physics",
+            "uri": "http://edamontology.org/topic_3318"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/euphausiidb/euphausiidb.biotools.json b/data/euphausiidb/euphausiidb.biotools.json
new file mode 100644
index 0000000000000..1464c0a91cd39
--- /dev/null
+++ b/data/euphausiidb/euphausiidb.biotools.json
@@ -0,0 +1,63 @@
+{
+    "additionDate": "2023-03-01T10:47:21.937523Z",
+    "biotoolsCURIE": "biotools:euphausiidb",
+    "biotoolsID": "euphausiidb",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "toullec@sb-roscoff.fr",
+            "name": "Jean Yves TOULLEC"
+        },
+        {
+            "email": "corre@sb-roscoff.fr",
+            "name": "CORRE Erwan",
+            "orcidid": "https://orcid.org/0000-0001-6354-2278"
+        },
+        {
+            "name": "Fatoumata BINTA BARRY"
+        },
+        {
+            "name": "Loraine GUEGUEN",
+            "orcidid": "https://orcid.org/0000-0002-8640-4190"
+        },
+        {
+            "name": "Mark HOEBEKE",
+            "orcidid": "https://orcid.org/0000-0001-6311-9752"
+        }
+    ],
+    "description": "The EuphausiiDB portal offers the possibility for users to explore an Euphausiidae transcriptom database by using “simple” and “advanced” search functions for a specific taxonomic level, a specific geographic location or project origin, and a specific annotation. Statistical interactive charts, readsets location maps and tables, and the resulting list of datasets are associated with the search functions. For each selected dataset, the user can access readset and assembly short summary pages with cross-references to external databases (EBI SRA, NCBI taxID, WORMS), which allow better traceability and homogeneity across databases, as well as the possibility of downloading all resulting files...The intention is to develop the database continually as the transcriptomes of new species are sequenced and thus provide an up-to-date reference dedicated to the diversity of euphausiids.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "http://euphausiidb.sb-roscoff.fr/euphausiidb/documentation/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "http://euphausiidb.sb-roscoff.fr",
+    "lastUpdate": "2023-03-01T11:40:02.870378Z",
+    "license": "Not licensed",
+    "maturity": "Emerging",
+    "name": "EuphausiiDB",
+    "owner": "corre_erwan",
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Biodiversity",
+            "uri": "http://edamontology.org/topic_3050"
+        },
+        {
+            "term": "Marine biology",
+            "uri": "http://edamontology.org/topic_3387"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/ev-dna/ev-dna.biotools.json b/data/ev-dna/ev-dna.biotools.json
new file mode 100644
index 0000000000000..ef0af2d4ad0b5
--- /dev/null
+++ b/data/ev-dna/ev-dna.biotools.json
@@ -0,0 +1,119 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-08T01:33:16.023808Z",
+    "biotoolsCURIE": "biotools:ev-dna",
+    "biotoolsID": "ev-dna",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "julia.burnier@mcgill.ca",
+            "name": "Julia V. Burnier",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Mingyang Li"
+        },
+        {
+            "name": "Thupten Tsering"
+        },
+        {
+            "name": "Yunxi Chen"
+        }
+    ],
+    "description": "Database for EV-associated DNA in human liquid biopsy samples.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.evdnadatabase.com",
+    "lastUpdate": "2023-01-08T01:33:28.189992Z",
+    "name": "EV-DNA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/JEV2.12270",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Journal of Extracellular Vesicles published by Wiley Periodicals, LLC on behalf of the International Society for Extracellular Vesicles.Extracellular vesicles (EVs) play a key role in cellular communication both in physiological conditions and in pathologies such as cancer. Emerging evidence has shown that EVs are active carriers of molecular cargo (e.g. protein and nucleic acids) and a powerful source of biomarkers and targets. While recent studies on EV-associated DNA (EV-DNA) in human biofluids have generated a large amount of data, there is currently no database that catalogues information on EV-DNA. To fill this gap, we have manually curated a database of EV-DNA data derived from human biofluids (liquid biopsy) and in-vitro studies, called the Extracellular Vesicle-Associated DNA Database (EV-ADD). This database contains validated experimental details and data extracted from peer-reviewed published literature. It can be easily queried to search for EV isolation methods and characterization, EV-DNA isolation techniques, quality validation, DNA fragment size, volume of starting material, gene names and disease context. Currently, our database contains samples representing 23 diseases, with 13 different types of EV isolation techniques applied on eight different human biofluids (e.g. blood, saliva). In addition, EV-ADD encompasses EV-DNA data both representing the whole genome and specifically including oncogenes, such as KRAS, EGFR, BRAF, MYC, and mitochondrial DNA (mtDNA). An EV-ADD data metric system was also integrated to assign a compliancy score to the MISEV guidelines based on experimental parameters reported in each study. While currently available databases document the presence of proteins, lipids, RNA and metabolites in EVs (e.g. Vesiclepedia, ExoCarta, ExoBCD, EVpedia, and EV-TRACK), to the best of our knowledge, EV-ADD is the first of its kind to compile all available EV-DNA datasets derived from human biofluid samples. We believe that this database provides an important reference resource on EV-DNA-based liquid biopsy research, serving as a learning tool and to showcase the latest developments in the EV-DNA field. EV-ADD will be updated yearly as newly published EV-DNA data becomes available and it is freely available at www.evdnadatabase.com.",
+                "authors": [
+                    {
+                        "name": "Abdouh M."
+                    },
+                    {
+                        "name": "Burnier J.V."
+                    },
+                    {
+                        "name": "Bustamante P."
+                    },
+                    {
+                        "name": "Chen Y."
+                    },
+                    {
+                        "name": "Laskaris A."
+                    },
+                    {
+                        "name": "Li M."
+                    },
+                    {
+                        "name": "Nadeau A."
+                    },
+                    {
+                        "name": "Tsering T."
+                    }
+                ],
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "Journal of Extracellular Vesicles",
+                "title": "EV-ADD, a database for EV-associated DNA in human liquid biopsy samples"
+            },
+            "pmcid": "PMC9587709",
+            "pmid": "36271888"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Lipids",
+            "uri": "http://edamontology.org/topic_0153"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/evalfq/evalfq.biotools.json b/data/evalfq/evalfq.biotools.json
new file mode 100644
index 0000000000000..3d771e45bd5bc
--- /dev/null
+++ b/data/evalfq/evalfq.biotools.json
@@ -0,0 +1,88 @@
+{
+    "additionDate": "2023-01-28T12:45:49.778730Z",
+    "biotoolsCURIE": "biotools:evalfq",
+    "biotoolsID": "evalfq",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhufeng@zju.edu.cn",
+            "name": "Feng Zhu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "R Package for Evaluating Label-Free Proteome Quantification",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                },
+                {
+                    "term": "Label-free quantification",
+                    "uri": "http://edamontology.org/operation_3634"
+                },
+                {
+                    "term": "Labeled quantification",
+                    "uri": "http://edamontology.org/operation_3635"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/idrblab/EVALFQ",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-28T12:45:49.781353Z",
+    "license": "GPL-3.0",
+    "name": "EVALFQ",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC477",
+            "pmid": "36403090"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Proteogenomics",
+            "uri": "http://edamontology.org/topic_3922"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
+}
diff --git a/data/evam-tools/evam-tools.biotools.json b/data/evam-tools/evam-tools.biotools.json
new file mode 100644
index 0000000000000..77c06b1ef5447
--- /dev/null
+++ b/data/evam-tools/evam-tools.biotools.json
@@ -0,0 +1,113 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T23:51:28.240343Z",
+    "biotoolsCURIE": "biotools:evam-tools",
+    "biotoolsID": "evam-tools",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "r.diaz@uam.es",
+            "name": "Ramon Diaz-Uriarte",
+            "orcidid": "http://orcid.org/0000-0002-6637-9039",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Pablo Herrera-Nieto",
+            "orcidid": "http://orcid.org/0000-0002-3954-1723"
+        }
+    ],
+    "description": "Tools for evolutionary accumulation and cancer progression models.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://rdiaz02.github.io/EvAM-Tools/pdfs/evamtools-manual.pdf"
+        }
+    ],
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://hub.docker.com/u/rdiaz02"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://iib.uam.es/evamtools",
+    "language": [
+        "C",
+        "R"
+    ],
+    "lastUpdate": "2023-02-27T23:51:28.242980Z",
+    "license": "AGPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/rdiaz02/EvAM-Tools"
+        }
+    ],
+    "name": "EvAM-Tools",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac710",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: EvAM-Tools is an R package and web application that provides a unified interface to state-of-the-art cancer progression models and, more generally, evolutionary models of event accumulation. The output includes, in addition to the fitted models, the transition (and transition rate) matrices between genotypes and the probabilities of evolutionary paths. Generation of random cancer progression models is also available. Using the GUI in the web application, users can easily construct models (modifying directed acyclic graphs of restrictions, matrices of mutual hazards or specifying genotype composition), generate data from them (with user-specified observational/genotyping error) and analyze the data. AVAILABILITY AND IMPLEMENTATION: Implemented in R and C; open source code available under the GNU Affero General Public License v3.0 at https://github.com/rdiaz02/EvAM-Tools. Docker images freely available from https://hub.docker.com/u/rdiaz02. Web app freely accessible at https://iib.uam.es/evamtools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Diaz-Uriarte R."
+                    },
+                    {
+                        "name": "Herrera-Nieto P."
+                    }
+                ],
+                "date": "2022-12-13T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "EvAM-Tools: tools for evolutionary accumulation and cancer progression models"
+            },
+            "pmcid": "PMC9750106",
+            "pmid": "36287062"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/evanalyzer/evanalyzer.biotools.json b/data/evanalyzer/evanalyzer.biotools.json
new file mode 100644
index 0000000000000..21d2ebcee386a
--- /dev/null
+++ b/data/evanalyzer/evanalyzer.biotools.json
@@ -0,0 +1,167 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T12:51:28.723041Z",
+    "biotoolsCURIE": "biotools:evanalyzer",
+    "biotoolsID": "evanalyzer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "nicole.meisner-kober@plus.ac.at",
+            "name": "Nicole Meisner‐Kober",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "EVAnalyzer is a new open‐source plugin for Fiji, developed for automated single vesicle quantification from fluorescence microscopy images",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Single particle alignment and classification",
+                    "uri": "http://edamontology.org/operation_3458"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/joda01/evanalyzer/releases",
+    "language": [
+        "Java"
+    ],
+    "lastUpdate": "2023-01-28T12:51:28.726480Z",
+    "link": [
+        {
+            "type": [
+                "Issue tracker"
+            ],
+            "url": "https://github.com/joda01/evanalyzer/issues"
+        }
+    ],
+    "name": "EVAnalyzer",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1002/JEV2.12282",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Journal of Extracellular Vesicles published by Wiley Periodicals, LLC on behalf of the International Society for Extracellular Vesicles.Extracellular vesicle (EV) research increasingly demands for quantitative characterisation at the single vesicle level to address heterogeneity and complexity of EV subpopulations. Emerging, commercialised technologies for single EV analysis based on, for example, imaging flow cytometry or imaging after capture on chips generally require dedicated instrumentation and proprietary software not readily accessible to every lab. This limits their implementation for routine EV characterisation in the rapidly growing EV field. We and others have shown that single vesicles can be detected as light diffraction limited fluorescent spots using standard confocal and widefield fluorescence microscopes. Advancing this simple strategy into a process for routine EV quantitation, we developed ‘EVAnalyzer’, an ImageJ/Fiji (Fiji is just ImageJ) plugin for automated, quantitative single vesicle analysis from imaging data. Using EVAnalyzer, we established a robust protocol for capture, (immuno-)labelling and fluorescent imaging of EVs. To exemplify the application scope, the process was optimised and systematically tested for (i) quantification of EV subpopulations, (ii) validation of EV labelling reagents, (iii) in situ determination of antibody specificity, sensitivity and species cross-reactivity for EV markers and (iv) optimisation of genetic EV engineering. Additionally, we show that the process can be applied to synthetic nanoparticles, allowing to determine siRNA encapsulation efficiencies of lipid-based nanoparticles (LNPs) and protein loading of SiO2 nanoparticles. EVAnalyzer further provides a pipeline for automated quantification of cell uptake at the single cell–single vesicle level, thereby enabling high content EV cell uptake assays and plate-based screens. Notably, the entire procedure from sample preparation to the final data output is entirely based on standard reagents, materials, laboratory equipment and open access software. In summary, we show that EVAnalyzer enables rigorous characterisation of EVs with generally accessible tools. Since we further provide the plugin as open-source code, we expect EVAnalyzer to not only be a resource of immediate impact, but an open innovation platform for the EV and nanoparticle research communities.",
+                "authors": [
+                    {
+                        "name": "Benirschke H.M."
+                    },
+                    {
+                        "name": "Blochl C."
+                    },
+                    {
+                        "name": "Danmayr J."
+                    },
+                    {
+                        "name": "Gomes F.G."
+                    },
+                    {
+                        "name": "Heger Z."
+                    },
+                    {
+                        "name": "Heuser T."
+                    },
+                    {
+                        "name": "Himly M."
+                    },
+                    {
+                        "name": "Hintersteiner M."
+                    },
+                    {
+                        "name": "Huber C.G."
+                    },
+                    {
+                        "name": "Jaritsch M."
+                    },
+                    {
+                        "name": "Johnson L."
+                    },
+                    {
+                        "name": "Kiefer J."
+                    },
+                    {
+                        "name": "Klinglmayr E."
+                    },
+                    {
+                        "name": "Kratochvil Z."
+                    },
+                    {
+                        "name": "Matea C.-T."
+                    },
+                    {
+                        "name": "Meisner-Kober N."
+                    },
+                    {
+                        "name": "Miller A."
+                    },
+                    {
+                        "name": "Plank T."
+                    },
+                    {
+                        "name": "Rauter J."
+                    },
+                    {
+                        "name": "Schurz M."
+                    },
+                    {
+                        "name": "Stanojlovic V."
+                    },
+                    {
+                        "name": "Wolf M."
+                    },
+                    {
+                        "name": "Zimmerebner P."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Journal of Extracellular Vesicles",
+                "title": "EVAnalyzer: High content imaging for rigorous characterisation of single extracellular vesicles using standard laboratory equipment and a new open-source ImageJ/Fiji plugin"
+            },
+            "pmcid": "PMC9702573",
+            "pmid": "36437554"
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "fiji",
+            "type": "uses"
+        }
+    ],
+    "toolType": [
+        "Plug-in"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Cytometry",
+            "uri": "http://edamontology.org/topic_3934"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Lipids",
+            "uri": "http://edamontology.org/topic_0153"
+        }
+    ]
+}
diff --git a/data/evlncrna-dpred/evlncrna-dpred.biotools.json b/data/evlncrna-dpred/evlncrna-dpred.biotools.json
new file mode 100644
index 0000000000000..fd29feea10ca8
--- /dev/null
+++ b/data/evlncrna-dpred/evlncrna-dpred.biotools.json
@@ -0,0 +1,128 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T19:34:34.855278Z",
+    "biotoolsCURIE": "biotools:evlncrna-dpred",
+    "biotoolsID": "evlncrna-dpred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhouyq@szbl.ac.cn",
+            "name": "Yaoqi Zhou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jihua Wang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A web tool for prediction of experimentally validated lncRNAs by deep learning.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Formatting",
+                    "uri": "http://edamontology.org/operation_0335"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.sdklab-biophysics-dzu.net/EVlncRNA-Dpred/index.html",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-20T19:34:34.857879Z",
+    "license": "Not licensed",
+    "name": "EVlncRNA-Dpred",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC583",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.Long non-coding RNAs (lncRNAs) played essential roles in nearly every biological process and disease. Many algorithms were developed to distinguish lncRNAs from mRNAs in transcriptomic data and facilitated discoveries of more than 600 000 of lncRNAs. However, only a tiny fraction (<1%) of lncRNA transcripts (~4000) were further validated by low-throughput experiments (EVlncRNAs). Given the cost and labor-intensive nature of experimental validations, it is necessary to develop computational tools to prioritize those potentially functional lncRNAs because many lncRNAs from high-throughput sequencing (HTlncRNAs) could be resulted from transcriptional noises. Here, we employed deep learning algorithms to separate EVlncRNAs from HTlncRNAs and mRNAs. For overcoming the challenge of small datasets, we employed a three-layer deep-learning neural network (DNN) with a K-mer feature as the input and a small convolutional neural network (CNN) with one-hot encoding as the input. Three separate models were trained for human (h), mouse (m) and plant (p), respectively. The final concatenated models (EVlncRNA-Dpred (h), EVlncRNA-Dpred (m) and EVlncRNA-Dpred (p)) provided substantial improvement over a previous model based on support-vector-machines (EVlncRNA-pred). For example, EVlncRNA-Dpred (h) achieved 0.896 for the area under receiver-operating characteristic curve, compared with 0.582 given by sequence-based EVlncRNA-pred model. The models developed here should be useful for screening lncRNA transcripts for experimental validations. EVlncRNA-Dpred is available as a web server at https://www.sdklab-biophysics-dzu.net/EVlncRNA-Dpred/index.html, and the data and source code can be freely available along with the web server.",
+                "authors": [
+                    {
+                        "name": "Cao Z."
+                    },
+                    {
+                        "name": "Ding M."
+                    },
+                    {
+                        "name": "Feng J."
+                    },
+                    {
+                        "name": "Huang P."
+                    },
+                    {
+                        "name": "Ji B."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Yang Y."
+                    },
+                    {
+                        "name": "Yu X."
+                    },
+                    {
+                        "name": "Zhang J."
+                    },
+                    {
+                        "name": "Zhou B."
+                    },
+                    {
+                        "name": "Zhou Y."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "EVlncRNA-Dpred: improved prediction of experimentally validated lncRNAs by deep learning"
+            },
+            "pmcid": "PMC9851331",
+            "pmid": "36573492"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        }
+    ]
+}
diff --git a/data/evolclust/evolclust.biotools.json b/data/evolclust/evolclust.biotools.json
index e27d8ed10628a..904e69a06dd41 100644
--- a/data/evolclust/evolclust.biotools.json
+++ b/data/evolclust/evolclust.biotools.json
@@ -28,14 +28,14 @@
     "language": [
         "Python"
     ],
-    "lastUpdate": "2022-02-17T15:27:20.835714Z",
+    "lastUpdate": "2023-03-09T10:41:31.966646Z",
     "name": "EvolClust",
     "owner": "Gabaldonlab",
     "publication": [
         {
             "doi": "10.1093/BIOINFORMATICS/BTZ706",
             "metadata": {
-                "abstract": "© 2020 Oxford University Press. All rights reserved.Motivation: The evolution and role of gene clusters in eukaryotes is poorly understood. Currently, most studies and computational prediction programs limit their focus to specific types of clusters, such as those involved in secondary metabolism. Results: We present EvolClust, a python-based tool for the inference of evolutionary conserved gene clusters from genome comparisons, independently of the function or gene composition of the cluster. EvolClust predicts conserved gene clusters from pairwise genome comparisons and infers families of related clusters from multiple (all versus all) genome comparisons.",
+                "abstract": "Motivation: The evolution and role of gene clusters in eukaryotes is poorly understood. Currently, most studies and computational prediction programs limit their focus to specific types of clusters, such as those involved in secondary metabolism. Results: We present EvolClust, a python-based tool for the inference of evolutionary conserved gene clusters from genome comparisons, independently of the function or gene composition of the cluster. EvolClust predicts conserved gene clusters from pairwise genome comparisons and infers families of related clusters from multiple (all versus all) genome comparisons.",
                 "authors": [
                     {
                         "name": "Gabaldon T."
@@ -44,12 +44,22 @@
                         "name": "Marcet-Houben M."
                     }
                 ],
-                "citationCount": 5,
+                "citationCount": 6,
                 "date": "2020-02-15T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "EvolClust: Automated inference of evolutionary conserved gene clusters in eukaryotes"
             },
-            "pmid": "31560365"
+            "pmid": "31560365",
+            "type": [
+                "Method"
+            ]
+        },
+        {
+            "doi": "10.1016/j.jmb.2023.168013",
+            "pmid": "36806474",
+            "type": [
+                "Primary"
+            ]
         }
     ],
     "toolType": [
diff --git a/data/explorepipolin/explorepipolin.biotools.json b/data/explorepipolin/explorepipolin.biotools.json
new file mode 100644
index 0000000000000..05a4fa78a541f
--- /dev/null
+++ b/data/explorepipolin/explorepipolin.biotools.json
@@ -0,0 +1,94 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T15:16:26.540007Z",
+    "biotoolsCURIE": "biotools:explorepipolin",
+    "biotoolsID": "explorepipolin",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "modesto.redrejo@uam.es",
+            "name": "M. Redrejo-Rodríguez",
+            "orcidid": "http://orcid.org/0000-0003-0014-4162",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "V. Mateo-Cáceres"
+        },
+        {
+            "name": "L. Chuprikova",
+            "orcidid": "https://orcid.org/0000-0001-5135-9441"
+        },
+        {
+            "name": "M. de Toro",
+            "orcidid": "http://orcid.org/0000-0003-3329-0203"
+        }
+    ],
+    "description": "Reconstruction and annotation of bacterial mobile elements from draft genomes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Genome assembly",
+                    "uri": "http://edamontology.org/operation_0525"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/pipolinlab/ExplorePipolin",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T15:16:26.542558Z",
+    "license": "GPL-3.0",
+    "name": "ExplorePipolin",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioadv/vbac056",
+            "pmcid": "PMC9710591",
+            "pmid": "36699382"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Mobile genetic elements",
+            "uri": "http://edamontology.org/topic_0798"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ]
+}
diff --git a/data/ezcancertarget/ezcancertarget.biotools.json b/data/ezcancertarget/ezcancertarget.biotools.json
new file mode 100644
index 0000000000000..10aefa59a730a
--- /dev/null
+++ b/data/ezcancertarget/ezcancertarget.biotools.json
@@ -0,0 +1,146 @@
+{
+    "accessibility": "Open access (with restrictions)",
+    "additionDate": "2023-01-08T01:15:15.041763Z",
+    "biotoolsCURIE": "biotools:ezcancertarget",
+    "biotoolsID": "ezcancertarget",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dora.david@med.semmelweis-univ.hu",
+            "name": "David Dora",
+            "orcidid": "https://orcid.org/0000-0002-3138-8816",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zoltan.lohinai@koranyi.hu",
+            "name": "Zoltan Lohinai",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Csongor Gerdán"
+        },
+        {
+            "name": "Gabor Szegvari"
+        },
+        {
+            "name": "Timea Dora"
+        }
+    ],
+    "description": "An open-access drug repurposing and data-collection tool to enhance target validation and optimize international research efforts against highly progressive cancers.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://cycle20.github.io/EZCancerTarget/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/cycle20/EZCancerTarget",
+    "language": [
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-08T01:17:51.825122Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://cycle20.github.io/EZCancerTarget/"
+        }
+    ],
+    "name": "EZCancerTarget",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13040-022-00307-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).The expanding body of potential therapeutic targets requires easily accessible, structured, and transparent real-time interpretation of molecular data. Open-access genomic, proteomic and drug-repurposing databases transformed the landscape of cancer research, but most of them are difficult and time-consuming for casual users. Furthermore, to conduct systematic searches and data retrieval on multiple targets, researchers need the help of an expert bioinformatician, who is not always readily available for smaller research teams. We invite research teams to join and aim to enhance the cooperative work of more experienced groups to harmonize international efforts to overcome devastating malignancies. Here, we integrate available fundamental data and present a novel, open access, data-aggregating, drug repurposing platform, deriving our searches from the entries of Clue.io. We show how we integrated our previous expertise in small-cell lung cancer (SCLC) to initiate a new platform to overcome highly progressive cancers such as triple-negative breast and pancreatic cancer with data-aggregating approaches. Through the front end, the current content of the platform can be further expanded or replaced and users can create their drug-target list to select the clinically most relevant targets for further functional validation assays or drug trials. EZCancerTarget integrates searches from publicly available databases, such as PubChem, DrugBank, PubMed, and EMA, citing up-to-date and relevant literature of every target. Moreover, information on compounds is complemented with biological background information on eligible targets using entities like UniProt, String, and GeneCards, presenting relevant pathways, molecular- and biological function and subcellular localizations of these molecules. Cancer drug discovery requires a convergence of complex, often disparate fields. We present a simple, transparent, and user-friendly drug repurposing software to facilitate the efforts of research groups in the field of cancer research.",
+                "authors": [
+                    {
+                        "name": "Dora D."
+                    },
+                    {
+                        "name": "Dora T."
+                    },
+                    {
+                        "name": "Gerdan C."
+                    },
+                    {
+                        "name": "Lohinai Z."
+                    },
+                    {
+                        "name": "Szegvari G."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BioData Mining",
+                "title": "EZCancerTarget: an open-access drug repurposing and data-collection tool to enhance target validation and optimize international research efforts against highly progressive cancers"
+            },
+            "pmcid": "PMC9526900",
+            "pmid": "36183137"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/ezqtl/ezqtl.biotools.json b/data/ezqtl/ezqtl.biotools.json
new file mode 100644
index 0000000000000..5216dd474c9e3
--- /dev/null
+++ b/data/ezqtl/ezqtl.biotools.json
@@ -0,0 +1,137 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T01:51:43.322139Z",
+    "biotoolsCURIE": "biotools:ezqtl",
+    "biotoolsID": "ezqtl",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jiyeon.choi2@nih.gov",
+            "name": "Jiyeon Choi",
+            "orcidid": "http://orcid.org/0000-0002-0955-2384",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "kevin.brown3@nih.gov",
+            "name": "Kevin M Brown",
+            "orcidid": "http://orcid.org/0000-0002-8558-6711",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alyssa Klein",
+            "orcidid": "http://orcid.org/0000-0003-3763-5731"
+        },
+        {
+            "name": "Jian Sang",
+            "orcidid": "http://orcid.org/0000-0003-4953-3417"
+        },
+        {
+            "name": "Tongwu Zhang",
+            "orcidid": "http://orcid.org/0000-0003-2124-2706"
+        }
+    ],
+    "description": "A Web Platform for Interactive Visualization and Colocalization of Quantitative Trait Loci and GWAS.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression QTL analysis",
+                    "uri": "http://edamontology.org/operation_3232"
+                },
+                {
+                    "term": "Genetic mapping",
+                    "uri": "http://edamontology.org/operation_0282"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://analysistools.cancer.gov/ezqtl",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-01-17T01:51:43.324635Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/CBIIT/nci-webtools-dceg-ezQTL"
+        }
+    ],
+    "name": "ezQTL",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.gpb.2022.05.004",
+            "metadata": {
+                "abstract": "© 2022Genome-wide association studies (GWAS) have identified thousands of genomic loci associated with complex diseases and traits, including cancer. The vast majority of common trait-associated variants identified via GWAS fall in non-coding regions of the genome, posing a challenge in elucidating the causal variants, genes, and mechanisms involved. Expression quantitative trait locus (eQTL) and other molecular QTL studies have been valuable resources in identifying candidate causal genes from GWAS loci through statistical colocalization methods. While QTL colocalization is becoming a standard analysis in post-GWAS investigation, an easy web tool for users to perform formal colocalization analyses with either user-provided or public GWAS and eQTL datasets has been lacking. Here, we present ezQTL, a web-based bioinformatic application to interactively visualize and analyze genetic association data such as GWAS loci and molecular QTLs under different linkage disequilibrium (LD) patterns (1000 Genomes Project, UK Biobank, or user-provided data). This application allows users to perform data quality control for variants matched between different datasets, LD visualization, and two-trait colocalization analyses using two state-of-the-art methodologies (eCAVIAR and HyPrColoc), including batch processing. ezQTL is a free and publicly available cross-platform web tool, which can be accessed online at https://analysistools.cancer.gov/ezqtl.",
+                "authors": [
+                    {
+                        "name": "Brown K.M."
+                    },
+                    {
+                        "name": "Choi J."
+                    },
+                    {
+                        "name": "Klein A."
+                    },
+                    {
+                        "name": "Sang J."
+                    },
+                    {
+                        "name": "Zhang T."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-06-01T00:00:00Z",
+                "journal": "Genomics, Proteomics and Bioinformatics",
+                "title": "ezQTL: A Web Platform for Interactive Visualization and Colocalization of QTLs and GWAS Loci"
+            },
+            "pmcid": "PMC9801033",
+            "pmid": "35643189"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/factorizer/factorizer.biotools.json b/data/factorizer/factorizer.biotools.json
new file mode 100644
index 0000000000000..39747175d6889
--- /dev/null
+++ b/data/factorizer/factorizer.biotools.json
@@ -0,0 +1,88 @@
+{
+    "additionDate": "2023-02-20T19:38:40.283610Z",
+    "biotoolsCURIE": "biotools:factorizer",
+    "biotoolsID": "factorizer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Pooya Ashtari"
+        }
+    ],
+    "description": "A scalable interpretable approach to context modeling for medical image segmentation.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/pashtari/factorizer",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-20T19:38:40.286190Z",
+    "license": "Apache-2.0",
+    "name": "Factorizer",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.MEDIA.2022.102706",
+            "metadata": {
+                "abstract": "© 2022 The Author(s)Convolutional Neural Networks (CNNs) with U-shaped architectures have dominated medical image segmentation, which is crucial for various clinical purposes. However, the inherent locality of convolution makes CNNs fail to fully exploit global context, essential for better recognition of some structures, e.g., brain lesions. Transformers have recently proven promising performance on vision tasks, including semantic segmentation, mainly due to their capability of modeling long-range dependencies. Nevertheless, the quadratic complexity of attention makes existing Transformer-based models use self-attention layers only after somehow reducing the image resolution, which limits the ability to capture global contexts present at higher resolutions. Therefore, this work introduces a family of models, dubbed Factorizer, which leverages the power of low-rank matrix factorization for constructing an end-to-end segmentation model. Specifically, we propose a linearly scalable approach to context modeling, formulating Nonnegative Matrix Factorization (NMF) as a differentiable layer integrated into a U-shaped architecture. The shifted window technique is also utilized in combination with NMF to effectively aggregate local information. Factorizers compete favorably with CNNs and Transformers in terms of accuracy, scalability, and interpretability, achieving state-of-the-art results on the BraTS dataset for brain tumor segmentation and ISLES’22 dataset for stroke lesion segmentation. Highly meaningful NMF components give an additional interpretability advantage to Factorizers over CNNs and Transformers. Moreover, our ablation studies reveal a distinctive feature of Factorizers that enables a significant speed-up in inference for a trained Factorizer without any extra steps and without sacrificing much accuracy. The code and models are publicly available at https://github.com/pashtari/factorizer.",
+                "authors": [
+                    {
+                        "name": "Ashtari P."
+                    },
+                    {
+                        "name": "De Lathauwer L."
+                    },
+                    {
+                        "name": "Maes F."
+                    },
+                    {
+                        "name": "Sappey-Marinier D."
+                    },
+                    {
+                        "name": "Sima D.M."
+                    },
+                    {
+                        "name": "Van Huffel S."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "Medical Image Analysis",
+                "title": "Factorizer: A scalable interpretable approach to context modeling for medical image segmentation"
+            },
+            "pmid": "36516557"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/fair_data_station/fair_data_station.biotools.json b/data/fair_data_station/fair_data_station.biotools.json
new file mode 100644
index 0000000000000..5deb9afa721c8
--- /dev/null
+++ b/data/fair_data_station/fair_data_station.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-04T07:45:32.471965Z",
+    "biotoolsCURIE": "biotools:fair_data_station",
+    "biotoolsID": "fair_data_station",
+    "cost": "Free of charge",
+    "description": "FAIR Data Station for Lightweight Metadata Management & Validation of Omics Studies",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "http://docs.fairbydesign.nl"
+        }
+    ],
+    "download": [
+        {
+            "type": "Binaries",
+            "url": "http://download.systemsbiology.nl/unlock/fairds-latest.jar",
+            "version": "latest"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Resource metadata",
+                        "uri": "http://edamontology.org/data_2337"
+                    },
+                    "format": [
+                        {
+                            "term": "xlsx",
+                            "uri": "http://edamontology.org/format_3620"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Format validation",
+                    "uri": "http://edamontology.org/operation_0336"
+                },
+                {
+                    "term": "Text annotation",
+                    "uri": "http://edamontology.org/operation_3778"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Text data",
+                        "uri": "http://edamontology.org/data_2526"
+                    },
+                    "format": [
+                        {
+                            "term": "Turtle",
+                            "uri": "http://edamontology.org/format_3255"
+                        },
+                        {
+                            "term": "XML",
+                            "uri": "http://edamontology.org/format_2332"
+                        }
+                    ]
+                }
+            ]
+        }
+    ],
+    "homepage": "https://fairbydesign.nl",
+    "language": [
+        "Java"
+    ],
+    "lastUpdate": "2023-02-04T07:45:53.304037Z",
+    "license": "Apache-2.0",
+    "maturity": "Mature",
+    "name": "FAIR Data Station",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "jjkoehorst",
+    "publication": [
+        {
+            "doi": "10.1101/2022.08.03.502622",
+            "type": [
+                "Other"
+            ]
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biology",
+            "uri": "http://edamontology.org/topic_3070"
+        },
+        {
+            "term": "Computational biology",
+            "uri": "http://edamontology.org/topic_3307"
+        },
+        {
+            "term": "Experimental design and studies",
+            "uri": "http://edamontology.org/topic_3678"
+        },
+        {
+            "term": "Omics",
+            "uri": "http://edamontology.org/topic_3391"
+        }
+    ],
+    "version": [
+        "1.0"
+    ]
+}
diff --git a/data/farnet/farnet.biotools.json b/data/farnet/farnet.biotools.json
new file mode 100644
index 0000000000000..ac885aad27f61
--- /dev/null
+++ b/data/farnet/farnet.biotools.json
@@ -0,0 +1,77 @@
+{
+    "additionDate": "2023-01-28T12:59:46.000759Z",
+    "biotoolsCURIE": "biotools:farnet",
+    "biotoolsID": "farnet",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "aoyueyuan@qq.com",
+            "name": "Yueyuan Ao",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "hwu@uestc.edu.cn",
+            "name": "Hong Wu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A novel deep network named feature aggregation and refinement network (FARNet) for automatically detecting anatomical landmarks.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/JuvenileInWind/FARNet",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T12:59:46.003693Z",
+    "license": "Not licensed",
+    "name": "FARNet",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1007/S10278-022-00718-4",
+            "metadata": {
+                "abstract": "© 2022, The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.Localization of anatomical landmarks is essential for clinical diagnosis, treatment planning, and research. This paper proposes a novel deep network named feature aggregation and refinement network (FARNet) for automatically detecting anatomical landmarks. FARNet employs an encoder-decoder structure architecture. To alleviate the problem of limited training data in the medical domain, we adopt a backbone network pre-trained on natural images as the encoder. The decoder includes a multi-scale feature aggregation module for multi-scale feature fusion and a feature refinement module for high-resolution heatmap regression. Coarse-to-fine supervisions are applied to the two modules to facilitate end-to-end training. We further propose a novel loss function named Exponential Weighted Center loss for accurate heatmap regression, which focuses on the losses from the pixels near landmarks and suppresses the ones from far away. We evaluate FARNet on three publicly available anatomical landmark detection datasets, including cephalometric, hand, and spine radiographs. Our network achieves state-of-the-art performances on all three datasets. Code is available at https://github.com/JuvenileInWind/FARNet.",
+                "authors": [
+                    {
+                        "name": "Ao Y."
+                    },
+                    {
+                        "name": "Wu H."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Journal of Digital Imaging",
+                "title": "Feature Aggregation and Refinement Network for 2D Anatomical Landmark Detection"
+            },
+            "pmid": "36401132"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        }
+    ]
+}
diff --git a/data/fastaptamer_2.0/fastaptamer_2.0.biotools.json b/data/fastaptamer_2.0/fastaptamer_2.0.biotools.json
new file mode 100644
index 0000000000000..414c57f903fb8
--- /dev/null
+++ b/data/fastaptamer_2.0/fastaptamer_2.0.biotools.json
@@ -0,0 +1,142 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-22T02:06:22.115769Z",
+    "biotoolsCURIE": "biotools:fastaptamer_2.0",
+    "biotoolsID": "fastaptamer_2.0",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "burkedh@missouri.edu",
+            "name": "Donald H. Burke",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Dong Xu"
+        },
+        {
+            "name": "Khalid K. Alam"
+        },
+        {
+            "name": "Paige R. Gruenke"
+        },
+        {
+            "name": "Skyler T. Kramer",
+            "orcidid": "http://orcid.org/0000-0001-6539-1792"
+        }
+    ],
+    "description": "A Web Tool for Combinatorial Sequence Selections.",
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://hub.docker.com/repository/docker/skylerkramer/fastaptamer2"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence",
+                        "uri": "http://edamontology.org/data_2044"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        },
+                        {
+                            "term": "FASTQ",
+                            "uri": "http://edamontology.org/format_1930"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Sequence cluster visualisation",
+                    "uri": "http://edamontology.org/operation_0566"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://fastaptamer2.missouri.edu/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-22T02:06:22.118273Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/SkylerKramer/FASTAptameR-2.0"
+        }
+    ],
+    "name": "FASTAptameR 2.0",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.omtn.2022.08.030",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsCombinatorial selections are powerful strategies for identifying biopolymers with specific biological, biomedical, or chemical characteristics. Unfortunately, most available software tools for high-throughput sequencing analysis have high entrance barriers for many users because they require extensive programming expertise. FASTAptameR 2.0 is an R-based reimplementation of FASTAptamer designed to minimize this barrier while maintaining the ability to answer complex sequence-level and population-level questions. This open-source toolkit features a user-friendly web tool, interactive graphics, up to 100 times faster clustering, an expanded module set, and an extensive user guide. FASTAptameR 2.0 accepts diverse input polymer types and can be applied to any sequence-encoded selection.",
+                "authors": [
+                    {
+                        "name": "Alam K.K."
+                    },
+                    {
+                        "name": "Burke D.H."
+                    },
+                    {
+                        "name": "Gruenke P.R."
+                    },
+                    {
+                        "name": "Kramer S.T."
+                    },
+                    {
+                        "name": "Xu D."
+                    }
+                ],
+                "date": "2022-09-13T00:00:00Z",
+                "journal": "Molecular Therapy - Nucleic Acids",
+                "title": "FASTAptameR 2.0: A web tool for combinatorial sequence selections"
+            },
+            "pmcid": "PMC9464650",
+            "pmid": "36159593"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ],
+    "version": [
+        "2.0"
+    ]
+}
diff --git a/data/fates/fates.biotools.json b/data/fates/fates.biotools.json
new file mode 100644
index 0000000000000..26be5e0b452c7
--- /dev/null
+++ b/data/fates/fates.biotools.json
@@ -0,0 +1,98 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T13:07:59.584559Z",
+    "biotoolsCURIE": "biotools:fates",
+    "biotoolsID": "fates",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "igor.adameyko@meduniwien.ac.at",
+            "name": "Igor Adameyko",
+            "orcidid": "https://orcid.org/0000-0001-5471-0356",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A scalable python package for advanced pseudotime and bifurcation analysis from single-cell data.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Simulation analysis",
+                    "uri": "http://edamontology.org/operation_0244"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pypi.org/project/scFates/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-10T13:07:59.587197Z",
+    "license": "BSD-3-Clause",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/LouisFaure/scFates/"
+        }
+    ],
+    "name": "Fates",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC746",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: scFates provides an extensive toolset for the analysis of dynamic trajectories comprising tree learning, feature association testing, branch differential expression and with a focus on cell biasing and fate splits at the level of bifurcations. It is meant to be fully integrated into the scanpy ecosystem for seamless analysis of trajectories from single-cell data of various modalities (e.g. RNA and ATAC). AVAILABILITY AND IMPLEMENTATION: scFates is released as open-source software under the BSD 3-Clause 'New' License and is available from the Python Package Index at https://pypi.org/project/scFates/. The source code is available on GitHub at https://github.com/LouisFaure/scFates/. Code reproduction and tutorials on published datasets are available on GitHub at https://github.com/LouisFaure/scFates_notebooks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Adameyko I."
+                    },
+                    {
+                        "name": "Faure L."
+                    },
+                    {
+                        "name": "Kharchenko P.V."
+                    },
+                    {
+                        "name": "Soldatov R."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data"
+            },
+            "pmcid": "PMC9805561",
+            "pmid": "36394263"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Suite"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/fcclasses3/fcclasses3.biotools.json b/data/fcclasses3/fcclasses3.biotools.json
new file mode 100644
index 0000000000000..c90a73b1a0e5b
--- /dev/null
+++ b/data/fcclasses3/fcclasses3.biotools.json
@@ -0,0 +1,68 @@
+{
+    "additionDate": "2023-01-28T13:04:20.677259Z",
+    "biotoolsCURIE": "biotools:fcclasses3",
+    "biotoolsID": "fcclasses3",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Javier Cerezo"
+        }
+    ],
+    "description": "Vibrationally-resolved spectra simulated at the edge of the harmonic approximation.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein secondary structure assignment",
+                    "uri": "http://edamontology.org/operation_0319"
+                },
+                {
+                    "term": "Sorting",
+                    "uri": "http://edamontology.org/operation_3802"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.iccom.cnr.it/en/fcclasses/",
+    "lastUpdate": "2023-01-28T13:04:20.679954Z",
+    "license": "Not licensed",
+    "name": "FCclasses3",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1002/JCC.27027",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Journal of Computational Chemistry published by Wiley Periodicals LLC.We introduce FCclasses3, a code to carry out vibronic simulations of electronic spectra and nonradiative rates, based on the harmonic approximation. Key new features are: implementation of the full family of vertical and adiabatic harmonic models, vibrational analysis in curvilinear coordinates, extension to several electronic spectroscopies and implementation of time-dependent approaches. The use of curvilinear valence internal coordinates allows the adoption of quadratic model potential energy surfaces (PES) of the initial and final states expanded at arbitrary configurations. Moreover, the implementation of suitable projectors provides a robust framework for defining reduced-dimensionality models by sorting flexible coordinates out of the harmonic subset, so that they can then be treated at anharmonic level, or with mixed quantum classical approaches. A set of tools to facilitate input preparation and output analysis is also provided. We show the program at work in the simulation of different spectra (one and two-photon absorption, emission and resonance Raman) and internal conversion rate of a typical rigid molecule, anthracene. Then, we focus on absorption and emission spectra of a series of flexible polyphenyl molecules, highlighting the relevance of some of the newly implemented features. The code is freely available at http://www.iccom.cnr.it/en/fcclasses/.",
+                "authors": [
+                    {
+                        "name": "Cerezo J."
+                    },
+                    {
+                        "name": "Santoro F."
+                    }
+                ],
+                "date": "2023-02-05T00:00:00Z",
+                "journal": "Journal of Computational Chemistry",
+                "title": "FCclasses3: Vibrationally-resolved spectra simulated at the edge of the harmonic approximation"
+            },
+            "pmid": "36380723"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        }
+    ]
+}
diff --git a/data/fdalabel/fdalabel.biotools.json b/data/fdalabel/fdalabel.biotools.json
new file mode 100644
index 0000000000000..a817ac6f2145d
--- /dev/null
+++ b/data/fdalabel/fdalabel.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T23:40:38.810926Z",
+    "biotoolsCURIE": "biotools:fdalabel",
+    "biotoolsID": "fdalabel",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "p.johann@kitz-heidelberg.de",
+            "name": "Pascal Johann",
+            "orcidid": "https://orcid.org/0000-0002-8857-6148",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Dominic Lenz"
+        },
+        {
+            "name": "Markus Ries"
+        }
+    ],
+    "description": "The FDALabel Database is a web-based application used to perform customizable searches of over 140,000 human prescription, biological, over-the-counter (OTC), and animal drug labeling documents.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://nctr-crs.fda.gov/fdalabel/ui/search",
+    "lastUpdate": "2023-01-26T23:40:38.814303Z",
+    "name": "FDALabel",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/journal.pone.0252924",
+            "metadata": {
+                "abstract": "Copyright: © 2021 Johann et al.Background Glioblastoma (GBM) is the most common malignant brain tumour among adult patients and represents an almost universally fatal disease. Novel therapies for GBM are being developed under the orphan drug legislation and the knowledge on the molecular makeup of this disease has been increasing rapidly. However, the clinical outcomes in GBM patients with currently available therapies are still dismal. An insight into the current drug development pipeline for GBM is therefore of particular interest. Objectives To provide a quantitative clinical-regulatory insight into the status of FDA orphan drug designations for compounds intended to treat GBM. Methods Quantitative cross-sectional analysis of the U.S. Food and Drug Administration Orphan Drug Product database between 1983 and 2020. STROBE criteria were respected. Results Four orphan drugs out of 161 (2,4%) orphan drug designations were approved for the treatment for GBM by the FDA between 1983 and 2020. Fourteen orphan drug designations were subsequently withdrawn for unknown reasons. The number of orphan drug designations per year shows a growing trend. In the last decade, the therapeutic mechanism of action of designated compounds intended to treat glioblastoma shifted from cytotoxic drugs (median year of designation 2008) to immunotherapeutic approaches and small molecules (median year of designation 2014 and 2015 respectively) suggesting an increased focus on precision in the therapeutic mechanism of action for compounds the development pipeline. Conclusion Despite the fact that current pharmacological treatment options in GBM are sparse, the drug development pipeline is steadily growing. In particular, the surge of designated immunotherapies detected in the last years raises the hope that elaborate combination possibilities between classical therapeutic backbones (radiotherapy and chemotherapy) and novel, currently experimental therapeutics may help to provide better therapies for this deadly disease in the future.",
+                "authors": [
+                    {
+                        "name": "Johann P."
+                    },
+                    {
+                        "name": "Lenz D."
+                    },
+                    {
+                        "name": "Ries M."
+                    }
+                ],
+                "citationCount": 2,
+                "date": "2021-07-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "The drug development pipeline for glioblastoma—A cross sectional assessment of the FDA Orphan Drug Product designation database"
+            },
+            "pmcid": "PMC8263276",
+            "pmid": "34234357"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Allergy, clinical immunology and immunotherapeutics",
+            "uri": "http://edamontology.org/topic_3400"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Pharmacology",
+            "uri": "http://edamontology.org/topic_0202"
+        },
+        {
+            "term": "Pharmacovigilance",
+            "uri": "http://edamontology.org/topic_3378"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/fdrdb/fdrdb.biotools.json b/data/fdrdb/fdrdb.biotools.json
new file mode 100644
index 0000000000000..b5b54f96cc3a5
--- /dev/null
+++ b/data/fdrdb/fdrdb.biotools.json
@@ -0,0 +1,125 @@
+{
+    "additionDate": "2023-01-28T13:10:21.821035Z",
+    "biotoolsCURIE": "biotools:fdrdb",
+    "biotoolsID": "fdrdb",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "guyunyan@ems.hrbmu.edu.cn",
+            "name": "Yunyan Gu",
+            "orcidid": "https://orcid.org/0000-0001-5693-4126",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "lianghaihai@ems.hrbmu.edu.cn",
+            "name": "Haihai Liang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xxfan@must.edu.mo",
+            "name": "Xingxing Fan",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Fibrotic Disease-associated RNAome database, is an open and up-to-data online manually curated depository of RNAs alterations and high-throughput datasets about fibrotic diseases across various species.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.medsysbio.org/FDRdb",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T13:10:21.824163Z",
+    "name": "FDRdb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/DATABASE/BAAC095",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press.Fibrosis is a common and serious disease that exists as a complicated impairment in many organs and triggers a complex cascade of responses. The deregulation of Ribonucleic Acids (RNAs) plays important roles in a variety of organ fibrosis cases. However, for fibrotic diseases, there is still a lack of an integrated platform with up-To-date information on RNA deregulation and high-Throughput data. The Fibrotic Disease-Associated RNAome database (FDRdb) (http://www.medsysbio.org/FDRdb) is a manually curated database of fibrotic disease-Associated RNAome information and high-Throughput datasets. This initial release (i) contains 1947 associations between 912 RNAs and 92 fibrotic diseases in eight species; (ii) collects information on 764 datasets of fibrotic diseases; (iii) provides a user-friendly web interface that allows users to browse, search and download the RNAome information on fibrotic diseases and high-Throughput datasets and (iv) provides tools to analyze the expression profiles of fibrotic diseases, including differential expression analysis and pathway enrichment. The FDRdb is a valuable resource for researchers to explore the mechanisms of RNA dysregulation in organ fibrosis. Database URL: http://www.medsysbio.org/FDRdb",
+                "authors": [
+                    {
+                        "name": "Ai L."
+                    },
+                    {
+                        "name": "Chen T."
+                    },
+                    {
+                        "name": "Fan X."
+                    },
+                    {
+                        "name": "Gu Y."
+                    },
+                    {
+                        "name": "Liang H."
+                    },
+                    {
+                        "name": "Liang X."
+                    },
+                    {
+                        "name": "Mu Y."
+                    },
+                    {
+                        "name": "Wang C."
+                    },
+                    {
+                        "name": "Xiong K."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Database",
+                "title": "FDRdb: A manually curated database of fibrotic disease-Associated RNAome and high-Throughput datasets"
+            },
+            "pmcid": "PMC9650723",
+            "pmid": "36367312"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Cardiology",
+            "uri": "http://edamontology.org/topic_3335"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Urology and nephrology",
+            "uri": "http://edamontology.org/topic_3422"
+        }
+    ]
+}
diff --git a/data/febrna/febrna.biotools.json b/data/febrna/febrna.biotools.json
new file mode 100644
index 0000000000000..c2757b42a674a
--- /dev/null
+++ b/data/febrna/febrna.biotools.json
@@ -0,0 +1,124 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-22T02:12:18.372809Z",
+    "biotoolsCURIE": "biotools:febrna",
+    "biotoolsID": "febrna",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yltan@wtu.edu.cn",
+            "name": "Zhi-Jie Tan",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zjtan@whu.edu.cn",
+            "name": "Ya-Lan Tan",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Li Zhou"
+        },
+        {
+            "name": "Shixiong Yu"
+        },
+        {
+            "name": "Xunxun Wang"
+        }
+    ],
+    "description": "An automated fragment-ensemble-based model for building RNA 3D structures.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "RNA inverse folding",
+                    "uri": "http://edamontology.org/operation_0483"
+                },
+                {
+                    "term": "RNA secondary structure alignment",
+                    "uri": "http://edamontology.org/operation_0502"
+                },
+                {
+                    "term": "RNA secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0278"
+                },
+                {
+                    "term": "RNA structure prediction",
+                    "uri": "http://edamontology.org/operation_2441"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Tan-group/FebRNA",
+    "language": [
+        "C",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-22T02:12:18.375302Z",
+    "license": "GPL-3.0",
+    "name": "FebRNA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.bpj.2022.08.017",
+            "metadata": {
+                "abstract": "© 2022 Biophysical SocietyKnowledge of RNA three-dimensional (3D) structures is critical to understanding the important biological functions of RNAs. Although various structure prediction models have been developed, the high-accuracy predictions of RNA 3D structures are still limited to the RNAs with short lengths or with simple topology. In this work, we proposed a new model, namely FebRNA, for building RNA 3D structures through fragment assembly based on coarse-grained (CG) fragment ensembles. Specifically, FebRNA is composed of four processes: establishing the library of different types of non-redundant CG fragment ensembles regardless of the sequences, building CG 3D structure ensemble through fragment assembly, identifying top-scored CG structures through a specific CG scoring function, and rebuilding the all-atom structures from the top-scored CG ones. Extensive examination against different types of RNA structures indicates that FebRNA consistently gives the reliable predictions on RNA 3D structures, including pseudoknots, three-way junctions, four-way and five-way junctions, and RNAs in the RNA-Puzzles. FebRNA is available on the Web site: https://github.com/Tan-group/FebRNA.",
+                "authors": [
+                    {
+                        "name": "Tan Y.-L."
+                    },
+                    {
+                        "name": "Tan Z.-J."
+                    },
+                    {
+                        "name": "Wang X."
+                    },
+                    {
+                        "name": "Yu S."
+                    },
+                    {
+                        "name": "Zhou L."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Biophysical Journal",
+                "title": "FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures"
+            },
+            "pmcid": "PMC9515226",
+            "pmid": "35978551"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Nucleic acid structure analysis",
+            "uri": "http://edamontology.org/topic_0097"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        }
+    ]
+}
diff --git a/data/fegrow/fegrow.biotools.json b/data/fegrow/fegrow.biotools.json
new file mode 100644
index 0000000000000..02682fb0201e9
--- /dev/null
+++ b/data/fegrow/fegrow.biotools.json
@@ -0,0 +1,123 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T13:13:43.470450Z",
+    "biotoolsCURIE": "biotools:fegrow",
+    "biotoolsID": "fegrow",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "daniel.cole@ncl.ac.uk",
+            "name": "Daniel J. Cole",
+            "orcidid": "https://orcid.org/0000-0003-2933-0719",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An interactive workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://cole-group.github.io/FEgrow/"
+        },
+        {
+            "type": [
+                "Training material"
+            ],
+            "url": "https://github.com/cole-group/FEgrow/tree/master/notebooks"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein-ligand docking",
+                    "uri": "http://edamontology.org/operation_0482"
+                },
+                {
+                    "term": "Side chain modelling",
+                    "uri": "http://edamontology.org/operation_0480"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/cole-group/FEgrow",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T13:13:43.473106Z",
+    "license": "MIT",
+    "name": "FEgrow",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S42004-022-00754-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses for the ligands is challenging. Here, we introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations. For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein–ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein–ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole-group/FEgrow, along with a tutorial.",
+                "authors": [
+                    {
+                        "name": "Bieniek M.K."
+                    },
+                    {
+                        "name": "Cole D.J."
+                    },
+                    {
+                        "name": "Cree B."
+                    },
+                    {
+                        "name": "Horton J.T."
+                    },
+                    {
+                        "name": "Pirie R."
+                    },
+                    {
+                        "name": "Tatum N.J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Communications Chemistry",
+                "title": "An open-source molecular builder and free energy preparation workflow"
+            },
+            "pmcid": "PMC9607723",
+            "pmid": "36320862"
+        }
+    ],
+    "toolType": [
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Computational chemistry",
+            "uri": "http://edamontology.org/topic_3332"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Physics",
+            "uri": "http://edamontology.org/topic_3318"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/filt3r/filt3r.biotools.json b/data/filt3r/filt3r.biotools.json
new file mode 100644
index 0000000000000..bad787ea28df1
--- /dev/null
+++ b/data/filt3r/filt3r.biotools.json
@@ -0,0 +1,151 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T23:39:42.181248Z",
+    "biotoolsCURIE": "biotools:filt3r",
+    "biotoolsID": "filt3r",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mikael.salson@univ-lille.fr",
+            "name": "Mikaёl Salson",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Augustin Boudry"
+        },
+        {
+            "name": "Claude Preudhomme"
+        },
+        {
+            "name": "Sasha Darmon"
+        }
+    ],
+    "description": "Frugal alignment-free identification of FLT3-internal tandem duplications with FiLT3r.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Duplication detection",
+                    "uri": "http://edamontology.org/operation_3963"
+                },
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Indel detection",
+                    "uri": "http://edamontology.org/operation_0452"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://gitlab.univ-lille.fr/filt3r/filt3r",
+    "language": [
+        "C++"
+    ],
+    "lastUpdate": "2023-02-27T23:39:42.183836Z",
+    "license": "GPL-3.0",
+    "name": "FiLT3r",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/s12859-022-04983-6",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Internal tandem duplications in the FLT3 gene, termed FLT3-ITDs, are useful molecular markers in acute myeloid leukemia (AML) for patient risk stratification and follow-up. FLT3-ITDs are increasingly screened through high-throughput sequencing (HTS) raising the need for robust and efficient algorithms. We developed a new algorithm, which performs no alignment and uses little resources, to identify and quantify FLT3-ITDs in HTS data. Results: Our algorithm (FiLT3r) focuses on the k-mers from reads covering FLT3 exons 14 and 15. We show that those k-mers bring enough information to accurately detect, determine the length and quantify FLT3-ITD duplications. We compare the performances of FiLT3r to state-of-the-art alternatives and to fragment analysis, the gold standard method, on a cohort of 185 AML patients sequenced with capture-based HTS. On this dataset FiLT3r is more precise (no false positive nor false negative) than the other software evaluated. We also assess the software on public RNA-Seq data, which confirms the previous results and shows that FiLT3r requires little resources compared to other software. Conclusion: FiLT3r is a free software available at https://gitlab.univ-lille.fr/filt3r/filt3r. The repository also contains a Snakefile to reproduce our experiments. We show that FiLT3r detects FLT3-ITDs better than other software while using less memory and time.",
+                "authors": [
+                    {
+                        "name": "Boudry A."
+                    },
+                    {
+                        "name": "Bucci M."
+                    },
+                    {
+                        "name": "Celli-Lebras K."
+                    },
+                    {
+                        "name": "Darmon S."
+                    },
+                    {
+                        "name": "Dombret H."
+                    },
+                    {
+                        "name": "Duchmann M."
+                    },
+                    {
+                        "name": "Duployez N."
+                    },
+                    {
+                        "name": "Fenwarth L."
+                    },
+                    {
+                        "name": "Figeac M."
+                    },
+                    {
+                        "name": "Geffroy S."
+                    },
+                    {
+                        "name": "Goursaud L."
+                    },
+                    {
+                        "name": "Hunault M."
+                    },
+                    {
+                        "name": "Itzykson R."
+                    },
+                    {
+                        "name": "Joudinaud R."
+                    },
+                    {
+                        "name": "Nibourel O."
+                    },
+                    {
+                        "name": "Preudhomme C."
+                    },
+                    {
+                        "name": "Salson M."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "Frugal alignment-free identification of FLT3-internal tandem duplications with FiLT3r"
+            },
+            "pmcid": "PMC9617311",
+            "pmid": "36307762"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/finn/finn.biotools.json b/data/finn/finn.biotools.json
new file mode 100644
index 0000000000000..10fa809d1bd1b
--- /dev/null
+++ b/data/finn/finn.biotools.json
@@ -0,0 +1,81 @@
+{
+    "additionDate": "2023-02-19T11:54:55.712111Z",
+    "biotoolsCURIE": "biotools:finn",
+    "biotoolsID": "finn",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Alireza Gharabaghi"
+        }
+    ],
+    "description": "Find Neuropyhsiological Networks (FiNN). A Python Toolbox for the analysis of electrophysiological data.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/neurophysiological-analysis/FiNN",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-19T11:54:55.714814Z",
+    "license": "GPL-3.0",
+    "name": "FiNN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1002/HBM.26190",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.Neural communication across different spatial and temporal scales is a topic of great interest in clinical and basic science. Phase-amplitude coupling (PAC) has attracted particular interest due to its functional role in a wide range of cognitive and motor functions. Here, we introduce a novel measure termed the direct modulation index (dMI). Based on the classical modulation index, dMI provides an estimate of PAC that is (1) bound to an absolute interval between 0 and +1, (2) resistant against noise, and (3) reliable even for small amounts of data. To highlight the properties of this newly-proposed measure, we evaluated dMI by comparing it to the classical modulation index, mean vector length, and phase-locking value using simulated data. We ascertained that dMI provides a more accurate estimate of PAC than the existing methods and that is resilient to varying noise levels and signal lengths. As such, dMI permits a reliable investigation of PAC, which may reveal insights crucial to our understanding of functional brain architecture in key contexts such as behaviour and cognition. A Python toolbox that implements dMI and other measures of PAC is freely available at https://github.com/neurophysiological-analysis/FiNN.",
+                "authors": [
+                    {
+                        "name": "Gharabaghi A."
+                    },
+                    {
+                        "name": "Guggenberger R."
+                    },
+                    {
+                        "name": "Milosevic L."
+                    },
+                    {
+                        "name": "Scherer M."
+                    },
+                    {
+                        "name": "Wang T."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Human Brain Mapping",
+                "title": "Direct modulation index: A measure of phase amplitude coupling for neurophysiology data"
+            },
+            "pmid": "36579658"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        }
+    ]
+}
diff --git a/data/finsurf/finsurf.biotools.json b/data/finsurf/finsurf.biotools.json
index 43d28c09c8730..059a724d5edbb 100644
--- a/data/finsurf/finsurf.biotools.json
+++ b/data/finsurf/finsurf.biotools.json
@@ -1,9 +1,20 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2021-09-08T16:42:55Z",
     "biotoolsCURIE": "biotools:finsurf",
     "biotoolsID": "finsurf",
     "confidence_flag": "tool",
+    "cost": "Free of charge",
     "credit": [
+        {
+            "email": "alexandra.louis@bio.ens.psl.eu",
+            "name": "Alexandra Louis",
+            "orcidid": "https://orcid.org/0000-0001-7032-5650",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
         {
             "email": "hrc@bio.ens.psl.eu",
             "name": "Hugues Roest Crollius",
@@ -12,11 +23,51 @@
             "typeRole": [
                 "Primary contact"
             ]
+        },
+        {
+            "email": "moyon@bio.ens.psl.eu",
+            "name": "Lambert Moyon",
+            "orcidid": "https://orcid.org/0000-0003-2390-3942",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Developer"
+            ]
+        },
+        {
+            "email": "finsurf@bio.ens.psl.eu",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Camille Berthelot",
+            "orcidid": "https://orcid.org/0000-0001-5054-2690",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "name": "IBENS - DYOGEN Team",
+            "typeEntity": "Institute"
+        }
+    ],
+    "description": "FINSURF (Functional Identification of Non-coding Sequences Using Random Forests) is a tool designed to analyse lists of sequences variants in the human genome.\nIt assigns a score to each variant, reflecting its functional importance and therefore its likelihood to disrupt the physiology of its carrier. FINSURF scores Single Nucleotide Variants (SNV), insertions and deletions. Among SNVs, transitions and transversions are treated separately. Insertions are characterised by a score given to each base flanking the insertion point. Deletions are characterised by a score at every deleted base. FINSURF can (optionally) use a list of known or suspected disease genes, in order to restrict results to variants overlapping cis-regulatory elements linked to these genes.\n\nFor a variant of interest, users can generate a graphical representation of \"feature contributions », showing the relative contributions of genomic, functional or evolutionary information to its score.",
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://github.com/DyogenIBENS/FINSURF"
+        },
+        {
+            "type": "Test data",
+            "url": "https://www.opendata.bio.ens.psl.eu/finsurf/"
         }
     ],
-    "description": "FINSURF (Functional Identification of Non-coding Sequences Using Random Forests) is a tool designed to analyse lists of sequences variants in the human genome.",
     "editPermission": {
-        "type": "private"
+        "authors": [
+            "alouis"
+        ],
+        "type": "group"
     },
     "function": [
         {
@@ -48,8 +99,16 @@
     "language": [
         "Python"
     ],
-    "lastUpdate": "2021-09-13T12:04:56Z",
+    "lastUpdate": "2023-03-08T10:54:31.235738Z",
+    "license": "CECILL-C",
     "link": [
+        {
+            "note": "Web server",
+            "type": [
+                "Other"
+            ],
+            "url": "https://finsurf.bio.ens.psl.eu/"
+        },
         {
             "type": [
                 "Issue tracker"
@@ -64,16 +123,52 @@
         }
     ],
     "name": "FINSURF",
-    "owner": "Kigaard",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "alouis",
     "publication": [
         {
-            "doi": "10.1101/2021.05.03.442347",
+            "doi": "10.1371/journal.pgen.1010191",
+            "metadata": {
+                "abstract": "Whole genome sequencing is increasingly used to diagnose medical conditions of genetic origin. While both coding and non-coding DNA variants contribute to a wide range of diseases, most patients who receive a WGS-based diagnosis today harbour a protein-coding mutation. Functional interpretation and prioritization of non-coding variants represents a persistent challenge, and disease-causing non-coding variants remain largely unidentified. Depending on the disease, WGS fails to identify a candidate variant in 20–80% of patients, severely limiting the usefulness of sequencing for personalised medicine. Here we present FINSURF, a machine-learning approach to predict the functional impact of non-coding variants in regulatory regions. FINSURF outperforms state-of-the-art methods, owing in particular to optimized control variants selection during training. In addition to ranking candidate variants, FINSURF breaks down the score for each variant into contributions from individual annotations, facilitating the evaluation of their functional relevance. We applied FINSURF to a diverse set of 30 diseases with described causative non-coding mutations, and correctly identified the disease-causative non-coding variant within the ten top hits in 22 cases. FINSURF is implemented as an online server to as well as custom browser tracks, and provides a quick and efficient solution to prioritize candidate non-coding variants in realistic clinical settings.",
+                "authors": [
+                    {
+                        "name": "Berthelot C."
+                    },
+                    {
+                        "name": "Crollius H.R."
+                    },
+                    {
+                        "name": "Louis A."
+                    },
+                    {
+                        "name": "Moyon L."
+                    },
+                    {
+                        "name": "Nguyen N.T.T."
+                    }
+                ],
+                "date": "2022-04-29T00:00:00Z",
+                "journal": "PLoS Genetics",
+                "title": "Classification of non-coding variants with high pathogenic impact"
+            },
+            "pmcid": "PMC9094564",
+            "pmid": "35486646",
             "type": [
                 "Primary"
             ]
+        },
+        {
+            "doi": "10.1101/2021.05.03.442347",
+            "type": [
+                "Other"
+            ]
         }
     ],
     "toolType": [
+        "Command-line tool",
         "Web application"
     ],
     "topic": [
diff --git a/data/flapp/flapp.biotools.json b/data/flapp/flapp.biotools.json
new file mode 100644
index 0000000000000..e073dfc96970c
--- /dev/null
+++ b/data/flapp/flapp.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T23:28:02.867460Z",
+    "biotoolsCURIE": "biotools:flapp",
+    "biotoolsID": "flapp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "nchandra@iisc.ac.in",
+            "name": "Nagasuma Chandra",
+            "orcidid": "http://orcid.org/0000-0002-9939-8439",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Naren Chandran Sakthivel",
+            "orcidid": "http://orcid.org/0000-0001-7728-0985"
+        },
+        {
+            "name": "Santhosh Sankar",
+            "orcidid": "http://orcid.org/0000-0002-2755-5052"
+        }
+    ],
+    "description": "A system-compiled program for large-scale binding site alignment.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Ligand-binding site prediction",
+                    "uri": "http://edamontology.org/operation_3897"
+                },
+                {
+                    "term": "Protein structure prediction",
+                    "uri": "http://edamontology.org/operation_0474"
+                },
+                {
+                    "term": "Sequence alignment",
+                    "uri": "http://edamontology.org/operation_0292"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/santhoshgits/FLAPP",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-27T23:28:02.869988Z",
+    "license": "Apache-2.0",
+    "name": "FLAPP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/acs.jcim.2c00967",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society. All rights reserved.Protein function is a direct consequence of its sequence, structure, and the arrangement at the binding site. Bioinformatics using sequence analysis is typically used to gain a first insight into protein function. Protein structures, on the other hand, provide a higher resolution platform into understanding functions. As the protein structural information is increasingly becoming available through experimental structure determination and through advances in computational methods for structure prediction, the opportunity to utilize these data is also increasing. Structural analysis of small molecule ligand binding sites in particular provides a direct and more accurate window to infer protein function. However, it remains a poorly utilized resource due to the huge computational cost of existing methods that make large-scale structural comparisons of binding sites prohibitive. Here, we present an algorithm called FLAPP that produces very rapid atomic level alignments. By combining clique matching in graphs and the power of modern CPU architectures, FLAPP aligns a typical pair of binding sites at ∼12.5 ms using a single CPU core, ∼1 ms using 12 cores on a standard desktop machine, and performs a PDB-wide scan in 1-2 min. We perform rigorous validation of the algorithm at multiple levels of complexity and show that FLAPP provides accurate alignments. We also present a case study involving vitamin B12 binding sites to showcase the usefulness of FLAPP for performing an exhaustive alignment-based PDB-wide scan. We expect that this tool will be invaluable to the scientific community to quickly align millions of site pairs on a normal desktop machine to gain insights into protein function and drug discovery for drug target and off-target identification and polypharmacology.",
+                "authors": [
+                    {
+                        "name": "Chandra N."
+                    },
+                    {
+                        "name": "Chandran Sakthivel N."
+                    },
+                    {
+                        "name": "Sankar S."
+                    }
+                ],
+                "date": "2022-10-10T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "Fast Local Alignment of Protein Pockets (FLAPP): A System-Compiled Program for Large-Scale Binding Site Alignment"
+            },
+            "pmid": "36122166"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Nucleic acid sites, features and motifs",
+            "uri": "http://edamontology.org/topic_3511"
+        },
+        {
+            "term": "Protein binding sites",
+            "uri": "http://edamontology.org/topic_3534"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        }
+    ]
+}
diff --git a/data/flowsa/flowsa.biotools.json b/data/flowsa/flowsa.biotools.json
new file mode 100644
index 0000000000000..9929c7de875cb
--- /dev/null
+++ b/data/flowsa/flowsa.biotools.json
@@ -0,0 +1,116 @@
+{
+    "additionDate": "2023-01-28T13:21:35.087221Z",
+    "biotoolsCURIE": "biotools:flowsa",
+    "biotoolsID": "flowsa",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "birney.catherine@epa.gov",
+            "name": "Catherine Birney",
+            "orcidid": "https://orcid.org/0000-0003-4467-9927",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A Python Package Attributing Resource Use, Waste, Emissions, and Other Flows to Industries.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/USEPA/flowsa/wiki"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Formatting",
+                    "uri": "http://edamontology.org/operation_0335"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://edap-ord-data-commons.s3.amazonaws.com/index.html?prefix=flowsa/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T13:21:35.089926Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/USEPA/flowsa"
+        }
+    ],
+    "name": "FLOWSA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3390/APP12115742",
+            "metadata": {
+                "abstract": "© 2022 by the authors. Licensee MDPI, Basel, Switzerland.Quantifying industry consumption or production of resources, wastes, emissions, and losses—collectively called flows—is a complex and evolving process. The attribution of flows to industries often requires allocating multiple data sources that span spatial and temporal scopes and contain varied levels of aggregation. Once calculated, datasets can quickly become outdated with new releases of source data. The US Environmental Protection Agency (USEPA) developed the open-source Flow Sector Attribution (FLOWSA) Python package to address the challenges sur-rounding attributing flows to US industrial and final-use sectors. Models capture flows drawn from or released to the environment by sectors, as well as flow transfers between sectors. Data on flow use and generation by source-defined activities are imported from providers and transformed into standardized tables but are otherwise numerically unchanged in preparation for modeling. FLOWSA sector attribution models allocate primary data sources to industries using secondary data sources and file mapping activities to sectors. Users can modify methodological, spatial, and temporal parameters to explore and compare the impact of sector attribution methodological changes on model results. The standardized data outputs from these models are used as the environmental data inputs into the latest version of USEPA’s US Environmentally Extended Input–Output (USEEIO) models, life cycle models of US goods and services for ~400 categories. This communication demonstrates FLOWSA’s capability by describing how to build models and providing select model results for US industry use of water, land, and employment. FLOWSA is available on GitHub, and many of the data outputs are available on the USEPA’s Data Commons.",
+                "authors": [
+                    {
+                        "name": "Birney C."
+                    },
+                    {
+                        "name": "Conner M."
+                    },
+                    {
+                        "name": "Ingwersen W.W."
+                    },
+                    {
+                        "name": "Li M."
+                    },
+                    {
+                        "name": "Specht J."
+                    },
+                    {
+                        "name": "Young B."
+                    }
+                ],
+                "date": "2022-06-01T00:00:00Z",
+                "journal": "Applied Sciences (Switzerland)",
+                "title": "FLOWSA: A Python Package Attributing Resource Use, Waste, Emissions, and Other Flows to Industries"
+            },
+            "pmcid": "PMC9628186",
+            "pmid": "36330151"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Ecology",
+            "uri": "http://edamontology.org/topic_0610"
+        },
+        {
+            "term": "Physics",
+            "uri": "http://edamontology.org/topic_3318"
+        }
+    ]
+}
diff --git a/data/flowuti/flowuti.biotools.json b/data/flowuti/flowuti.biotools.json
new file mode 100644
index 0000000000000..90686776c0022
--- /dev/null
+++ b/data/flowuti/flowuti.biotools.json
@@ -0,0 +1,85 @@
+{
+    "additionDate": "2023-01-28T13:24:29.911740Z",
+    "biotoolsCURIE": "biotools:flowuti",
+    "biotoolsID": "flowuti",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "gmartin-ibis@us.es",
+            "name": "Guillermo Martín-Gutiérrez ,",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections.",
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://github.com/GuillermoMG-HUVR/Microbiology-applications/tree/FlowUTI/FlowUTI"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "homepage": "https://covidiario.shinyapps.io/flowuti/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-28T13:24:29.914384Z",
+    "license": "Not licensed",
+    "name": "FlowUTI",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PONE.0277340",
+            "metadata": {
+                "abstract": "© 2022 Martín-Gutiérrez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Due to the high prevalence of patients attending with urinary tract infection (UTI) symptoms, the use of flow-cytometry as a rapid screening tool to avoid unnecessary cultures is becoming a widely used system in clinical practice. However, the recommended cut-points applied in flow-cytometry systems differ substantially among authors, making it difficult to obtain reliable conclusions. Here, we present FlowUTI, a shiny web-application created to establish optimal cut-off values in flow-cytometry for different UTI markers, such as bacterial or leukocyte counts, in urine from patients with UTI symptoms. This application provides a user-friendly graphical interface to perform robust statistical analysis without a specific training. Two datasets are analyzed in this manuscript: one composed of 204 urine samples from neonates and infants (≤3 months old) attended in the emergency department with suspected UTI; and the second dataset including 1174 urines samples from an elderly population attended at the primary care level. The source code is available on GitHub (https://github.com/GuillermoMG-HUVR/Microbiology-applications/tree/FlowUTI/FlowUTI). The web application can be executed locally from the R console. Alternatively, it can be freely accessed at https://covidiario.shinyapps.io/flowuti/. FlowUTI provides an easy-to-use environment for evaluating the efficiency of the urinary screening process with flow-cytometry, reducing the computational burden associated with this kind of analysis.",
+                "authors": [
+                    {
+                        "name": "Lepe J.A."
+                    },
+                    {
+                        "name": "Martin-Gutierrez G."
+                    },
+                    {
+                        "name": "Martin-Perez C."
+                    },
+                    {
+                        "name": "Sanchez-Cantalejo E."
+                    },
+                    {
+                        "name": "Toledo H."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections"
+            },
+            "pmcid": "PMC9642874",
+            "pmid": "36346782"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cytometry",
+            "uri": "http://edamontology.org/topic_3934"
+        },
+        {
+            "term": "Geriatric medicine",
+            "uri": "http://edamontology.org/topic_3399"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/fluspred/fluspred.biotools.json b/data/fluspred/fluspred.biotools.json
index 6aee36dd73655..0f1ca09603fb1 100644
--- a/data/fluspred/fluspred.biotools.json
+++ b/data/fluspred/fluspred.biotools.json
@@ -1,10 +1,30 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2022-10-03T09:24:57.447429Z",
     "biotoolsCURIE": "biotools:fluspred",
     "biotoolsID": "fluspred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "credit": [
+        {
+            "name": "Anjali Dhall",
+            "orcidid": "http://orcid.org/0000-0002-0400-2084"
+        },
+        {
+            "name": "Khushal Sharma",
+            "orcidid": "http://orcid.org/0000-0002-6993-5408"
+        },
+        {
+            "name": "Sumeet Patiyal",
+            "orcidid": "http://orcid.org/0000-0003-1358-292X"
+        },
+        {
+            "name": "Trinita Roy",
+            "orcidid": "http://orcid.org/0000-0002-2049-1391"
+        },
         {
             "name": "Dr Gajendra P.S. Raghava",
+            "orcidid": "http://orcid.org/0000-0002-8902-2876",
             "url": "https://webs.iiitd.edu.in/raghava/fluspred/index.html"
         }
     ],
@@ -24,15 +44,37 @@
         {
             "operation": [
                 {
-                    "term": "Analysis",
-                    "uri": "http://edamontology.org/operation_2945"
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Protein feature detection",
+                    "uri": "http://edamontology.org/operation_3092"
+                },
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                },
+                {
+                    "term": "Protein structure validation",
+                    "uri": "http://edamontology.org/operation_0321"
                 }
             ]
         }
     ],
     "homepage": "https://webs.iiitd.edu.in/raghava/fluspred/index.html",
-    "lastUpdate": "2022-10-03T09:27:11.430395Z",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-17T02:35:05.562232Z",
+    "license": "GPL-3.0",
     "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/raghavagps/FluSPred"
+        },
         {
             "type": [
                 "Software catalogue"
@@ -47,13 +89,59 @@
         "Windows"
     ],
     "owner": "raghavagps",
+    "publication": [
+        {
+            "doi": "10.1099/jgv.0.001802",
+            "metadata": {
+                "abstract": "Influenza A is a contagious viral disease responsible for four pandemics in the past and a major public health concern. Being zoonotic in nature, the virus can cross the species barrier and transmit from wild aquatic bird reservoirs to humans via intermediate hosts. In this study, we have developed a computational method for the prediction of human-associated and non-human-associated influenza A virus sequences. The models were trained and validated on proteins and genome sequences of influenza A virus. Firstly, we have developed prediction models for 15 types of influenza A proteins using composition-based and one-hot-encoding features. We have achieved a highest AUC of 0.98 for HA protein on a validation dataset using dipeptide composition-based features. Of note, we obtained a maximum AUC of 0.99 using one-hot-encoding features for protein-based models on a validation dataset. Secondly, we built models using whole genome sequences which achieved an AUC of 0.98 on a validation dataset. In addition, we showed that our method outperforms a similarity-based approach (i.e., blast) on the same validation dataset. Finally, we integrated our best models into a user-friendly web server 'FluSPred' (https://webs.iiitd.edu.in/raghava/fluspred/index.html) and a standalone version (https://github.com/raghavagps/FluSPred) for the prediction of human-associated/non-human-associated influenza A virus strains.",
+                "authors": [
+                    {
+                        "name": "Dhall A."
+                    },
+                    {
+                        "name": "Patiyal S."
+                    },
+                    {
+                        "name": "Raghava G.P.S."
+                    },
+                    {
+                        "name": "Roy T."
+                    },
+                    {
+                        "name": "Sharma K."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "The Journal of general virology",
+                "title": "In silico method for predicting infectious strains of influenza A virus from its genome and protein sequences"
+            },
+            "pmid": "36318663"
+        }
+    ],
     "toolType": [
+        "Command-line tool",
         "Web application"
     ],
     "topic": [
         {
-            "term": "Computational biology",
-            "uri": "http://edamontology.org/topic_3307"
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
         }
     ]
 }
diff --git a/data/fmriflows/fmriflows.biotools.json b/data/fmriflows/fmriflows.biotools.json
new file mode 100644
index 0000000000000..b823ad9cf3f5f
--- /dev/null
+++ b/data/fmriflows/fmriflows.biotools.json
@@ -0,0 +1,98 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T19:45:29.332792Z",
+    "biotoolsCURIE": "biotools:fmriflows",
+    "biotoolsID": "fmriflows",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "michaelnotter@hotmail.com",
+            "name": "Michael P. Notter",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "micah.murray@chuv.ch",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A consortium of fully automatic neuroimaging pipelines for fMRI analysis, which performs standard preprocessing, as well as 1st- and 2nd-level univariate and multivariate analyses.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/miykael/fmriflows",
+    "language": [
+        "MATLAB",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-20T19:45:29.335301Z",
+    "license": "BSD-3-Clause",
+    "name": "fMRIflows",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1007/S10548-022-00935-8",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).How functional magnetic resonance imaging (fMRI) data are analyzed depends on the researcher and the toolbox used. It is not uncommon that the processing pipeline is rewritten for each new dataset. Consequently, code transparency, quality control and objective analysis pipelines are important for improving reproducibility in neuroimaging studies. Toolboxes, such as Nipype and fMRIPrep, have documented the need for and interest in automated pre-processing analysis pipelines. Recent developments in data-driven models combined with high resolution neuroimaging dataset have strengthened the need not only for a standardized preprocessing workflow, but also for a reliable and comparable statistical pipeline. Here, we introduce fMRIflows: a consortium of fully automatic neuroimaging pipelines for fMRI analysis, which performs standard preprocessing, as well as 1st- and 2nd-level univariate and multivariate analyses. In addition to the standardized pre-processing pipelines, fMRIflows provides flexible temporal and spatial filtering to account for datasets with increasingly high temporal resolution and to help appropriately prepare data for advanced machine learning analyses, improving signal decoding accuracy and reliability. This paper first describes fMRIflows’ structure and functionality, then explains its infrastructure and access, and lastly validates the toolbox by comparing it to other neuroimaging processing pipelines such as fMRIPrep, FSL and SPM. This validation was performed on three datasets with varying temporal sampling and acquisition parameters to prove its flexibility and robustness. fMRIflows is a fully automatic fMRI processing pipeline which uniquely offers univariate and multivariate single-subject and group analyses as well as pre-processing.",
+                "authors": [
+                    {
+                        "name": "Da Costa S."
+                    },
+                    {
+                        "name": "Gaglianese A."
+                    },
+                    {
+                        "name": "Gulban O.F."
+                    },
+                    {
+                        "name": "Herholz P."
+                    },
+                    {
+                        "name": "Isik A.I."
+                    },
+                    {
+                        "name": "Murray M.M."
+                    },
+                    {
+                        "name": "Notter M.P."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Brain Topography",
+                "title": "fMRIflows: A Consortium of Fully Automatic Univariate and Multivariate fMRI Processing Pipelines"
+            },
+            "pmid": "36575327"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/foec2/foec2.biotools.json b/data/foec2/foec2.biotools.json
new file mode 100644
index 0000000000000..ab31649c4ce7c
--- /dev/null
+++ b/data/foec2/foec2.biotools.json
@@ -0,0 +1,114 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T13:32:22.359918Z",
+    "biotoolsCURIE": "biotools:foec2",
+    "biotoolsID": "foec2",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "p.vandam@genetwister.nl",
+            "name": "Peter van Dam",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A pipeline that can identify putative effectors in a provided set of Fusarium oxysporum genomes and show their presence/absence variation across all input genomes.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Dendrogram visualisation",
+                    "uri": "http://edamontology.org/operation_2938"
+                },
+                {
+                    "term": "Multiple sequence alignment",
+                    "uri": "http://edamontology.org/operation_0492"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/pvdam3/FoEC2",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-01-28T13:32:22.362756Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://github.com/pvdam3/FoEC"
+        }
+    ],
+    "name": "FoEC2",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FPLS.2022.1012688",
+            "metadata": {
+                "abstract": "Copyright © 2022 Brenes Guallar, Fokkens, Rep, Berke and van Dam.The fungus Fusarium oxysporum is infamous for its devastating effects on economically important crops worldwide. F. oxysporum isolates are grouped into formae speciales based on their ability to cause disease on different hosts. Assigning F. oxysporum strains to formae speciales using non-experimental procedures has proven to be challenging due to their genetic heterogeneity and polyphyletic nature. However, genetically diverse isolates of the same forma specialis encode similar repertoires of effectors, proteins that are secreted by the fungus and contribute to the establishment of compatibility with the host. Based on this observation, we previously designed the F. oxysporum Effector Clustering (FoEC) pipeline which is able to classify F. oxysporum strains by forma specialis based on hierarchical clustering of the presence of predicted putative effector sequences, solely using genome assemblies as input. Here we present the updated FoEC2 pipeline which is more user friendly, customizable and, due to multithreading, has improved scalability. It is designed as a Snakemake pipeline and incorporates a new interactive visualization app. We showcase FoEC2 by clustering 537 publicly available F. oxysporum genomes and further analysis of putative effector families as multiple sequence alignments. We confirm classification of isolates into formae speciales and are able to further identify their subtypes. The pipeline is available on github: https://github.com/pvdam3/FoEC2.",
+                "authors": [
+                    {
+                        "name": "Berke L."
+                    },
+                    {
+                        "name": "Brenes Guallar M.A."
+                    },
+                    {
+                        "name": "Fokkens L."
+                    },
+                    {
+                        "name": "Rep M."
+                    },
+                    {
+                        "name": "van Dam P."
+                    }
+                ],
+                "date": "2022-10-19T00:00:00Z",
+                "journal": "Frontiers in Plant Science",
+                "title": "Fusarium oxysporum effector clustering version 2: An updated pipeline to infer host range"
+            },
+            "pmcid": "PMC9627151",
+            "pmid": "36340405"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/fpocketweb/fpocketweb.biotools.json b/data/fpocketweb/fpocketweb.biotools.json
new file mode 100644
index 0000000000000..e13d08de1906e
--- /dev/null
+++ b/data/fpocketweb/fpocketweb.biotools.json
@@ -0,0 +1,69 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T15:10:39.271011Z",
+    "biotoolsCURIE": "biotools:fpocketweb",
+    "biotoolsID": "fpocketweb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Jacob D. Durrant",
+            "orcidid": "http://orcid.org/0000-0002-5808-4097"
+        },
+        {
+            "name": "Yuri Kochnev",
+            "orcidid": "http://orcid.org/0000-0001-6605-6603"
+        }
+    ],
+    "description": "Protein pocket hunting in a web browser.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "http://durrantlab.com/fpocketweb",
+    "lastUpdate": "2023-02-26T15:10:39.273675Z",
+    "name": "FPocketWeb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/s13321-022-00637-0",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Detecting macromolecular (e.g., protein) cavities where small molecules bind is an early step in computer-aided drug discovery. Multiple pocket-detection algorithms have been developed over the past several decades. Among them, fpocket, created by Schmidtke and Le Guilloux, is particularly popular. Like many programs used in computational-biology research, fpocket requires users to download and install an executable file. That file must also be run via a command-line interface, further complicating use. An existing fpocket server application effectively addresses these challenges, but it requires users to upload their possibly proprietary structures to a third-party server. The FPocketWeb web app builds on this prior work. It runs the fpocket3 executable entirely in a web browser without requiring installation. The pocket-finding calculations occur on the user’s computer rather than on a remote server. A working version of the open-source FPocketWeb app can be accessed free of charge from http://durrantlab.com/fpocketweb.",
+                "authors": [
+                    {
+                        "name": "Durrant J.D."
+                    },
+                    {
+                        "name": "Kochnev Y."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Journal of Cheminformatics",
+                "title": "FPocketWeb: protein pocket hunting in a web browser"
+            },
+            "pmcid": "PMC9414105",
+            "pmid": "36008829"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/fungal_names/fungal_names.biotools.json b/data/fungal_names/fungal_names.biotools.json
new file mode 100644
index 0000000000000..74ae1caebd17a
--- /dev/null
+++ b/data/fungal_names/fungal_names.biotools.json
@@ -0,0 +1,97 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-08T00:58:34.960071Z",
+    "biotoolsCURIE": "biotools:fungal_names",
+    "biotoolsID": "fungal_names",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "wulh@im.ac.cn",
+            "name": "Linhuan Wu",
+            "orcidid": "https://orcid.org/0000-0002-5255-1846",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "yaoyj@im.ac.cn",
+            "name": "Yijian Yao",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Fang Wang"
+        },
+        {
+            "name": "Ke Wang"
+        }
+    ],
+    "description": "A comprehensive nomenclatural repository and knowledge base for fungal taxonomy.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Taxon",
+                        "uri": "http://edamontology.org/data_1868"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://nmdc.cn/fungalnames/",
+    "lastUpdate": "2023-01-08T00:58:34.963214Z",
+    "name": "fungal names",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC926",
+            "pmid": "36271801"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Microbiology",
+            "uri": "http://edamontology.org/topic_3301"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Sample collections",
+            "uri": "http://edamontology.org/topic_3277"
+        },
+        {
+            "term": "Taxonomy",
+            "uri": "http://edamontology.org/topic_0637"
+        }
+    ]
+}
diff --git a/data/funpart/funpart.biotools.json b/data/funpart/funpart.biotools.json
new file mode 100644
index 0000000000000..a0aec5bbeb696
--- /dev/null
+++ b/data/funpart/funpart.biotools.json
@@ -0,0 +1,153 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-11T13:36:49.681872Z",
+    "biotoolsCURIE": "biotools:funpart",
+    "biotoolsID": "funpart",
+    "collectionID": [
+        "LCSB-CBG"
+    ],
+    "credit": [
+        {
+            "email": "antonio.delsol@uni.lu",
+            "name": "Antonio del Sol",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ],
+            "url": "https://wwwen.uni.lu/lcsb/people/antonio_del_sol_mesa"
+        }
+    ],
+    "description": "FunPart is a computational tool that partitions heterogeneous cell populations into functionally distinct subpopulations and simultaneously identifies modules of functionally relevant set of genes for each of them.",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://github.com/BarlierC/FunPart"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "elixirNode": [
+        "Luxembourg"
+    ],
+    "elixirPlatform": [
+        "Tools"
+    ],
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene expression matrix",
+                        "uri": "http://edamontology.org/data_3112"
+                    },
+                    "format": [
+                        {
+                            "term": "TSV",
+                            "uri": "http://edamontology.org/format_3475"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Gene regulatory network analysis",
+                    "uri": "http://edamontology.org/operation_1781"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Annotated text",
+                        "uri": "http://edamontology.org/data_3779"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Clustered expression profiles",
+                        "uri": "http://edamontology.org/data_3768"
+                    }
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/BarlierC/FunPart",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-11T13:36:49.684660Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/BarlierC/FunPart"
+        }
+    ],
+    "name": "FunPart",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "KartikeyaS",
+    "publication": [
+        {
+            "doi": "10.1038/s41419-021-04075-y",
+            "metadata": {
+                "abstract": "© 2021, The Author(s).Immunomodulation strategies are crucial for several biomedical applications. However, the immune system is highly heterogeneous and its functional responses to infections remains elusive. Indeed, the characterization of immune response particularities to different pathogens is needed to identify immunomodulatory candidates. To address this issue, we compiled a comprehensive map of functional immune cell states of mouse in response to 12 pathogens. To create this atlas, we developed a single-cell-based computational method that partitions heterogeneous cell types into functionally distinct states and simultaneously identifies modules of functionally relevant genes characterizing them. We identified 295 functional states using 114 datasets of six immune cell types, creating a Catalogus Immune Muris. As a result, we found common as well as pathogen-specific functional states and experimentally characterized the function of an unknown macrophage cell state that modulates the response to Salmonella Typhimurium infection. Thus, we expect our Catalogus Immune Muris to be an important resource for studies aiming at discovering new immunomodulatory candidates.",
+                "authors": [
+                    {
+                        "name": "Anguita J."
+                    },
+                    {
+                        "name": "Barlier C."
+                    },
+                    {
+                        "name": "Barriales D."
+                    },
+                    {
+                        "name": "Jung S."
+                    },
+                    {
+                        "name": "Medvedeva Y.A."
+                    },
+                    {
+                        "name": "Ravichandran S."
+                    },
+                    {
+                        "name": "Samosyuk A."
+                    },
+                    {
+                        "name": "del Sol A."
+                    }
+                ],
+                "date": "2021-09-01T00:00:00Z",
+                "journal": "Cell Death and Disease",
+                "title": "A Catalogus Immune Muris of the mouse immune responses to diverse pathogens"
+            },
+            "pmcid": "PMC8370971",
+            "pmid": "34404761",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Function analysis",
+            "uri": "http://edamontology.org/topic_1775"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/funres/funres.biotools.json b/data/funres/funres.biotools.json
new file mode 100644
index 0000000000000..1b24ce309ba82
--- /dev/null
+++ b/data/funres/funres.biotools.json
@@ -0,0 +1,154 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-11T15:30:42.728886Z",
+    "biotoolsCURIE": "biotools:funres",
+    "biotoolsID": "funres",
+    "collectionID": [
+        "LCSB-CBG"
+    ],
+    "credit": [
+        {
+            "email": "antonio.delsol@uni.lu",
+            "name": "Antonio del Sol",
+            "note": "Group leader, Computational Biology group, Luxembourg Centre for Systems Biomedicine \nFull professor / Chief scientist 1 in Bioinformatics  at University of Luxembourg",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ],
+            "url": "https://wwwen.uni.lu/lcsb/people/antonio_del_sol_mesa"
+        }
+    ],
+    "description": "Resolving tissue-specific functional cell states based on a cell–cell communication network model",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://git-r3lab.uni.lu/kartikeya.singh/funres"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://git-r3lab.uni.lu/kartikeya.singh/funres"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "elixirNode": [
+        "Luxembourg"
+    ],
+    "elixirPlatform": [
+        "Tools"
+    ],
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene expression matrix",
+                        "uri": "http://edamontology.org/data_3112"
+                    },
+                    "format": [
+                        {
+                            "term": "TSV",
+                            "uri": "http://edamontology.org/format_3475"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Modelling and simulation",
+                    "uri": "http://edamontology.org/operation_2426"
+                },
+                {
+                    "term": "Prediction and recognition",
+                    "uri": "http://edamontology.org/operation_2423"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Annotated text",
+                        "uri": "http://edamontology.org/data_3779"
+                    },
+                    "format": [
+                        {
+                            "term": "TSV",
+                            "uri": "http://edamontology.org/format_3475"
+                        }
+                    ]
+                },
+                {
+                    "data": {
+                        "term": "Heat map",
+                        "uri": "http://edamontology.org/data_1636"
+                    },
+                    "format": [
+                        {
+                            "term": "tiff",
+                            "uri": "http://edamontology.org/format_3591"
+                        }
+                    ]
+                }
+            ]
+        }
+    ],
+    "homepage": "https://git-r3lab.uni.lu/kartikeya.singh/funres",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-11T15:30:42.731628Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://git-r3lab.uni.lu/kartikeya.singh/funres"
+        }
+    ],
+    "name": "FunRes",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "KartikeyaS",
+    "publication": [
+        {
+            "doi": "10.1093/bib/bbaa283",
+            "metadata": {
+                "abstract": "© 2020 The Author(s) 2020. Published by Oxford University Press.The functional specialization of cell types arises during development and is shaped by cell-cell communication networks determining a distribution of functional cell states that are collectively important for tissue functioning. However, the identification of these tissue-specific functional cell states remains challenging. Although a plethora of computational approaches have been successful in detecting cell types and subtypes, they fail in resolving tissue-specific functional cell states. To address this issue, we present FunRes, a computational method designed for the identification of functional cell states. FunRes relies on scRNA-seq data of a tissue to initially reconstruct the functional cell-cell communication network, which is leveraged for partitioning each cell type into functional cell states. We applied FunRes to 177 cell types in 10 different tissues and demonstrated that the detected states correspond to known functional cell states of various cell types, which cannot be recapitulated by existing computational tools. Finally, we characterize emerging and vanishing functional cell states in aging and disease, and demonstrate their involvement in key tissue functions. Thus, we believe that FunRes will be of great utility in the characterization of the functional landscape of cell types and the identification of dysfunctional cell states in aging and disease.",
+                "authors": [
+                    {
+                        "name": "Del Sol A."
+                    },
+                    {
+                        "name": "Jung S."
+                    },
+                    {
+                        "name": "Singh K."
+                    }
+                ],
+                "citationCount": 3,
+                "date": "2021-07-01T00:00:00Z",
+                "journal": "Briefings in Bioinformatics",
+                "title": "FunRes: Resolving tissue-specific functional cell states based on a cell-cell communication network model"
+            },
+            "pmcid": "PMC8293827",
+            "pmid": "33179736",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        }
+    ]
+}
diff --git a/data/fusta/fusta.biotools.json b/data/fusta/fusta.biotools.json
index 5f603df9aac25..19a3c783cb930 100644
--- a/data/fusta/fusta.biotools.json
+++ b/data/fusta/fusta.biotools.json
@@ -4,15 +4,63 @@
     "biotoolsCURIE": "biotools:fusta",
     "biotoolsID": "fusta",
     "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "IBENS - DYOGEN Team",
+            "typeEntity": "Institute",
+            "url": "http://www.ibens.ens.fr/?rubrique43&lang=en"
+        }
+    ],
     "description": "FUSTA is a FUSE-based virtual filesystem mirroring a (multi)FASTA file as a hierarchy of individual virtual files, simplifying efficient data extraction and bulk/automated processing of FASTA files.",
     "editPermission": {
-        "type": "private"
+        "authors": [
+            "alouis"
+        ],
+        "type": "group"
     },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Tool name (FASTA)",
+                        "uri": "http://edamontology.org/data_1193"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA-like",
+                            "uri": "http://edamontology.org/format_2546"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Tool name (FASTA)",
+                        "uri": "http://edamontology.org/data_1193"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ]
+        }
+    ],
     "homepage": "https://github.com/delehef/fusta",
     "language": [
         "Other"
     ],
-    "lastUpdate": "2022-10-24T18:33:34.171700Z",
+    "lastUpdate": "2023-03-03T17:36:10.462763Z",
     "license": "CECILL-C",
     "link": [
         {
@@ -35,6 +83,16 @@
         "Mac"
     ],
     "owner": "delehef",
+    "publication": [
+        {
+            "doi": "10.1093/bioadv/vbac091",
+            "pmcid": "PMC9875552",
+            "pmid": "36713287",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
     "toolType": [
         "Command-line tool"
     ],
diff --git a/data/g3mclass/g3mclass.biotools.json b/data/g3mclass/g3mclass.biotools.json
new file mode 100644
index 0000000000000..71c3c06f18a80
--- /dev/null
+++ b/data/g3mclass/g3mclass.biotools.json
@@ -0,0 +1,105 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T13:37:54.787795Z",
+    "biotoolsCURIE": "biotools:g3mclass",
+    "biotoolsID": "g3mclass",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "guvakova@pennmedicine.upenn.edu",
+            "name": "Marina A. Guvakova",
+            "orcidid": "https://orcid.org/0000-0001-5290-6726",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "G3Mclass is a software for Gaussian Mixture Model for Marker Classification. It has a complete set of help files that can be viewed after installation.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "Statistical calculation",
+                    "uri": "http://edamontology.org/operation_2238"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pypi.org/project/g3mclass",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T13:37:54.791017Z",
+    "license": "GPL-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/MathsCell/g3mclass"
+        }
+    ],
+    "name": "g3mclass",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41598-022-23438-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).The analytes qualified as biomarkers are potent tools to diagnose various diseases, monitor therapy responses, and design therapeutic interventions. The early assessment of the diverseness of human disease is essential for the speedy and cost-efficient implementation of personalized medicine. We developed g3mclass, the Gaussian mixture modeling software for molecular assay data classification. This software automates the validated multiclass classifier applicable to single analyte tests and multiplexing assays. The g3mclass achieves automation using the original semi-constrained expectation–maximization (EM) algorithm that allows inference from the test, control, and query data that human experts cannot interpret. In this study, we used real-world clinical data and gene expression datasets (ERBB2, ESR1, PGR) to provide examples of how g3mclass may help overcome the problems of over-/underdiagnosis and equivocal results in diagnostic tests for breast cancer. We showed the g3mclass output’s accuracy, robustness, scalability, and interpretability. The user-friendly interface and free dissemination of this multi-platform software aim to ease its use by research laboratories, biomedical pharma, companion diagnostic developers, and healthcare regulators. Furthermore, the g3mclass automatic extracting information through probabilistic modeling is adaptable for blending with machine learning and artificial intelligence.",
+                "authors": [
+                    {
+                        "name": "Guvakova M.A."
+                    },
+                    {
+                        "name": "Sokol S."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "The g3mclass is a practical software for multiclass classification on biomarkers"
+            },
+            "pmcid": "PMC9637185",
+            "pmid": "36335194"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/g4atlas/g4atlas.biotools.json b/data/g4atlas/g4atlas.biotools.json
new file mode 100644
index 0000000000000..77fc783d930e5
--- /dev/null
+++ b/data/g4atlas/g4atlas.biotools.json
@@ -0,0 +1,119 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-08T00:53:40.967549Z",
+    "biotoolsCURIE": "biotools:g4atlas",
+    "biotoolsID": "g4atlas",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "haopeng.yu@jic.ac.uk",
+            "name": "Haopeng Yu",
+            "orcidid": "https://orcid.org/0000-0002-5184-2430",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "yiliang.ding@jic.ac.uk",
+            "name": "Yiliang Ding",
+            "orcidid": "https://orcid.org/0000-0003-4161-6365",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Bibo Yang"
+        },
+        {
+            "name": "Xiaofei Yang"
+        },
+        {
+            "name": "Yiman Qi"
+        }
+    ],
+    "description": "A comprehensive transcriptome-wide G-quadruplex database.",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://www.g4atlas.org/download"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene ID",
+                        "uri": "http://edamontology.org/data_2295"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Gene name",
+                        "uri": "http://edamontology.org/data_2299"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Species name",
+                        "uri": "http://edamontology.org/data_1045"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "RNA secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0278"
+                },
+                {
+                    "term": "RNA structure prediction",
+                    "uri": "http://edamontology.org/operation_2441"
+                },
+                {
+                    "term": "Structure visualisation",
+                    "uri": "http://edamontology.org/operation_0570"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.g4atlas.org/",
+    "lastUpdate": "2023-01-08T00:55:41.242678Z",
+    "name": "G4Atlas",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC896",
+            "pmid": "36243987"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Protein structural motifs and surfaces",
+            "uri": "http://edamontology.org/topic_0166"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/gaitforemer/gaitforemer.biotools.json b/data/gaitforemer/gaitforemer.biotools.json
new file mode 100644
index 0000000000000..a605a08debddb
--- /dev/null
+++ b/data/gaitforemer/gaitforemer.biotools.json
@@ -0,0 +1,79 @@
+{
+    "additionDate": "2023-01-28T13:43:29.100681Z",
+    "biotoolsCURIE": "biotools:gaitforemer",
+    "biotoolsID": "gaitforemer",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "eadeli@stanford.edu",
+            "name": "Ehsan Adeli",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "GaitForeMer (Gait Forecasting and impairment estimation transforMer) predicts MDS-UPDRS gait impairment severity scores using learned motion features from the pre-training task of human motion forecasting.",
+    "editPermission": {
+        "type": "public"
+    },
+    "homepage": "https://github.com/markendo/GaitForeMer",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T13:43:29.103394Z",
+    "license": "GPL-3.0",
+    "name": "GaitForeMer",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1007/978-3-031-16452-1_13",
+            "metadata": {
+                "abstract": "© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Parkinson’s disease (PD) is a neurological disorder that has a variety of observable motor-related symptoms such as slow movement, tremor, muscular rigidity, and impaired posture. PD is typically diagnosed by evaluating the severity of motor impairments according to scoring systems such as the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Automated severity prediction using video recordings of individuals provides a promising route for non-intrusive monitoring of motor impairments. However, the limited size of PD gait data hinders model ability and clinical potential. Because of this clinical data scarcity and inspired by the recent advances in self-supervised large-scale language models like GPT-3, we use human motion forecasting as an effective self-supervised pre-training task for the estimation of motor impairment severity. We introduce GaitForeMer, Gait Forecasting and impairment estimation transforMer, which is first pre-trained on public datasets to forecast gait movements and then applied to clinical data to predict MDS-UPDRS gait impairment severity. Our method outperforms previous approaches that rely solely on clinical data by a large margin, achieving an F$$:1$$ score of 0.76, precision of 0.79, and recall of 0.75. Using GaitForeMer, we show how public human movement data repositories can assist clinical use cases through learning universal motion representations. The code is available at https://github.com/markendo/GaitForeMer.",
+                "authors": [
+                    {
+                        "name": "Adeli E."
+                    },
+                    {
+                        "name": "Endo M."
+                    },
+                    {
+                        "name": "Fei-Fei L."
+                    },
+                    {
+                        "name": "Pohl K.M."
+                    },
+                    {
+                        "name": "Poston K.L."
+                    },
+                    {
+                        "name": "Sullivan E.V."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
+                "title": "GaitForeMer: Self-supervised Pre-training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation"
+            },
+            "pmcid": "PMC9635991",
+            "pmid": "36342887"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Neurology",
+            "uri": "http://edamontology.org/topic_3334"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "RNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3794"
+        }
+    ]
+}
diff --git a/data/gavisunk/gavisunk.biotools.json b/data/gavisunk/gavisunk.biotools.json
new file mode 100644
index 0000000000000..86ed2554bf032
--- /dev/null
+++ b/data/gavisunk/gavisunk.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T15:06:26.973023Z",
+    "biotoolsCURIE": "biotools:gavisunk",
+    "biotoolsID": "gavisunk",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Allison N. Rozanski",
+            "orcidid": "http://orcid.org/0000-0002-5034-1773"
+        },
+        {
+            "name": "Evan E. Eichler",
+            "orcidid": "http://orcid.org/0000-0002-8246-4014"
+        },
+        {
+            "name": "Glennis A. Logsdon",
+            "orcidid": "http://orcid.org/0000-0003-2396-0656"
+        },
+        {
+            "name": "Philip C. Dishuck",
+            "orcidid": "http://orcid.org/0000-0003-2223-9787"
+        }
+    ],
+    "description": "Genome assembly validation via inter-SUNK distances in Oxford Nanopore reads.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "De-novo assembly",
+                    "uri": "http://edamontology.org/operation_0524"
+                },
+                {
+                    "term": "Genome assembly",
+                    "uri": "http://edamontology.org/operation_0525"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/pdishuck/GAVISUNK",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T15:06:26.975587Z",
+    "license": "MIT",
+    "name": "GAVISUNK",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac714",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Highly contiguous de novo phased diploid genome assemblies are now feasible for large numbers of species and individuals. Methods are needed to validate assembly accuracy and detect misassemblies with orthologous sequencing data to allow for confident downstream analyses. RESULTS: We developed GAVISUNK, an open-source pipeline that detects misassemblies and produces a set of reliable regions genome-wide by assessing concordance of distances between unique k-mers in Pacific Biosciences high-fidelity assemblies and raw Oxford Nanopore Technologies reads. AVAILABILITY AND IMPLEMENTATION: GAVISUNK is available at https://github.com/pdishuck/GAVISUNK. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Dishuck P.C."
+                    },
+                    {
+                        "name": "Eichler E.E."
+                    },
+                    {
+                        "name": "Logsdon G.A."
+                    },
+                    {
+                        "name": "Porubsky D."
+                    },
+                    {
+                        "name": "Rozanski A.N."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "GAVISUNK: genome assembly validation via inter-SUNK distances in Oxford Nanopore reads"
+            },
+            "pmcid": "PMC9805576",
+            "pmid": "36321867"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/gen-era_toolbox/gen-era_toolbox.biotools.json b/data/gen-era_toolbox/gen-era_toolbox.biotools.json
new file mode 100644
index 0000000000000..96ddd3bd2746a
--- /dev/null
+++ b/data/gen-era_toolbox/gen-era_toolbox.biotools.json
@@ -0,0 +1,29 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-12T15:38:53.335761Z",
+    "biotoolsCURIE": "biotools:gen-era_toolbox",
+    "biotoolsID": "gen-era_toolbox",
+    "cost": "Free of charge",
+    "description": "The GEN-ERA toolbox can be used to infer completely reproducible comparative genomic and metabolic analyses on prokaryotes and small eukaryotes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/Lcornet/GENERA",
+    "lastUpdate": "2023-01-12T15:40:43.463894Z",
+    "license": "GPL-3.0",
+    "maturity": "Mature",
+    "name": "GEN-ERA toolbox",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Lcornet",
+    "toolType": [
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Computational biology",
+            "uri": "http://edamontology.org/topic_3307"
+        }
+    ]
+}
diff --git a/data/genecloudomics/genecloudomics.biotools.json b/data/genecloudomics/genecloudomics.biotools.json
new file mode 100644
index 0000000000000..e305a9629ce2e
--- /dev/null
+++ b/data/genecloudomics/genecloudomics.biotools.json
@@ -0,0 +1,136 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T15:51:53.047663Z",
+    "biotoolsCURIE": "biotools:genecloudomics",
+    "biotoolsID": "genecloudomics",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Kumar_Selvarajoo@bii.a-star.edu.sg",
+            "name": "Kumar Selvarajoo",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "mohamed_helmy@bii.a-star.edu.sg",
+            "name": "Mohamed Helmy",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Rahul Agrawal"
+        },
+        {
+            "name": "Thuy Tien Bui"
+        }
+    ],
+    "description": "A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/buithuytien/GeneCloudOmics/blob/master/GeneCloudOmics_Help_1.0.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Expression data visualisation",
+                    "uri": "http://edamontology.org/operation_0571"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://combio-sifbi.org/GeneCloudOmics/",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2022-12-31T15:51:53.050195Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/cbio-astar-tools/GeneCloudOmics"
+        }
+    ],
+    "name": "GeneCloudOmics",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1007/978-1-0716-2617-7_12",
+            "metadata": {
+                "abstract": "© 2023, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.Research in synthetic biology and metabolic engineering require a deep understanding on the function and regulation of complex pathway genes. This can be achieved through gene expression profiling which quantifies the transcriptome-wide expression under any condition, such as a cell development stage, mutant, disease, or treatment with a drug. The expression profiling is usually done using high-throughput techniques such as RNA sequencing (RNA-Seq) or microarray. Although both methods are based on different technical approaches, they provide quantitative measures of the expression levels of thousands of genes. The expression levels of the genes are compared under different conditions to identify the differentially expressed genes (DEGs), the genes with different expression levels under different conditions. DEGs, usually involving thousands in number, are then investigated using bioinformatics and data analytic tools to infer and compare their functional roles between conditions. Dealing with such large datasets, therefore, requires intensive data processing and analyses to ensure its quality and produce results that are statistically sound. Thus, there is a need for deep statistical and bioinformatics knowledge to deal with high-throughput gene expression data. This represents a barrier for wet biologists with limited computational, programming, and data analytic skills that prevent them from getting the full potential of the data. In this chapter, we present a step-by-step protocol to perform transcriptome analysis using GeneCloudOmics, a cloud-based web server that provides an end-to-end platform for high-throughput gene expression analysis.",
+                "authors": [
+                    {
+                        "name": "Helmy M."
+                    },
+                    {
+                        "name": "Selvarajoo K."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Methods in Molecular Biology",
+                "title": "Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology"
+            },
+            "pmid": "36227547"
+        },
+        {
+            "doi": "10.3389/FBINF.2021.693836",
+            "pmcid": "PMC9581002",
+            "pmid": "36303746"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        },
+        {
+            "term": "Synthetic biology",
+            "uri": "http://edamontology.org/topic_3895"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/genenettools/genenettools.biotools.json b/data/genenettools/genenettools.biotools.json
new file mode 100644
index 0000000000000..a033ade57af20
--- /dev/null
+++ b/data/genenettools/genenettools.biotools.json
@@ -0,0 +1,90 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T15:42:16.791143Z",
+    "biotoolsCURIE": "biotools:genenettools",
+    "biotoolsID": "genenettools",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "p.l.horvatovich@rug.nl",
+            "name": "Peter Horvatovich",
+            "orcidid": "https://orcid.org/0000-0003-2218-1140",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Marco Grzegorczyk"
+        },
+        {
+            "name": "Victor Bernal",
+            "orcidid": "https://orcid.org/0000-0002-9134-7186"
+        }
+    ],
+    "description": "Tests for Gaussian graphical models with shrinkage.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/V-Bernal/GeneNetTools",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2022-12-31T15:42:16.793672Z",
+    "license": "Not licensed",
+    "name": "GeneNetTools",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC657",
+            "pmcid": "PMC9665865",
+            "pmid": "36179082"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/geneselectml/geneselectml.biotools.json b/data/geneselectml/geneselectml.biotools.json
new file mode 100644
index 0000000000000..c203295abb34a
--- /dev/null
+++ b/data/geneselectml/geneselectml.biotools.json
@@ -0,0 +1,98 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T13:49:02.460703Z",
+    "biotoolsCURIE": "biotools:geneselectml",
+    "biotoolsID": "geneselectml",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "osman.dag@hacettepe.edu.tr",
+            "name": "Osman Dag",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "This web-tool enables the researchers to find differentially expressed genes using different machine learning algorithms.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene prediction",
+                    "uri": "http://edamontology.org/operation_2454"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.softmed.hacettepe.edu.tr/GeneSelectML",
+    "lastUpdate": "2023-01-28T13:49:02.463681Z",
+    "license": "Not licensed",
+    "name": "GeneSelectML",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1007/S11517-022-02695-W",
+            "metadata": {
+                "abstract": "© 2022, International Federation for Medical and Biological Engineering.Abstract: Selection of differentially expressed genes (DEGs) is a vital process to discover the causes of diseases. It has been shown that modelling of genomics data by considering relation among genes increases the predictive performance of methods compared to univariate analysis. However, there exist serious differences among most studies analyzing the same dataset for the reasons arising from the methods. Therefore, there is a strong need for easily accessible, user-friendly, and interactive tool to perform gene selection for RNA-seq data via machine learning algorithms simultaneously not to miss DEGs. We develop an open-source and freely available web-based tool for gene selection via machine learning algorithms that can deal with high performance computation. This tool includes six machine learning algorithms having different aspects. Moreover, the tool involves classical pre-processing steps; filtering, normalization, transformation, and univariate analysis. It also offers well-arranged graphical approaches; network plot, heatmap, venn diagram, and box-and-whisker plot. Gene ontology analysis is provided for both mRNA and miRNA DEGs. The implementation is carried out on Alzheimer RNA-seq data to demonstrate the use of this web-based tool. Eleven genes are suggested by at least two out of six methods. One of these genes, hsa-miR-148a-3p, might be considered as a new biomarker for Alzheimer’s disease diagnosis. Kidney Chromophobe dataset is also analyzed to demonstrate the validity of GeneSelectML web tool on a different dataset. GeneSelectML is distinguished in that it simultaneously uses different machine learning algorithms for gene selection and can perform pre-processing, graphical representation, and gene ontology analyses on the same tool. This tool is freely available at www.softmed.hacettepe.edu.tr/GeneSelectML. Graphical abstract: [Figure not available: see fulltext.].",
+                "authors": [
+                    {
+                        "name": "Dag O."
+                    },
+                    {
+                        "name": "Ilk O."
+                    },
+                    {
+                        "name": "Kasikci M."
+                    },
+                    {
+                        "name": "Yesiltepe M."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Medical and Biological Engineering and Computing",
+                "title": "GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms"
+            },
+            "pmid": "36355333"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/geneticsmakie.jl/geneticsmakie.jl.biotools.json b/data/geneticsmakie.jl/geneticsmakie.jl.biotools.json
new file mode 100644
index 0000000000000..d1bd57745b0bf
--- /dev/null
+++ b/data/geneticsmakie.jl/geneticsmakie.jl.biotools.json
@@ -0,0 +1,102 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T19:50:30.774178Z",
+    "biotoolsCURIE": "biotools:geneticsmakie.jl",
+    "biotoolsID": "geneticsmakie.jl",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "minsookim@mednet.ucla.edu",
+            "name": "Minsoo Kim",
+            "orcidid": "https://orcid.org/0000-0001-6657-8786",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "michael.gandal@pennmedicine.upenn.edu",
+            "name": "Michael J Gandal",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A versatile and scalable toolkit for visualizing locus-level genetic and genomic data.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/mmkim1210/GeneticsMakie.jl",
+    "language": [
+        "Julia"
+    ],
+    "lastUpdate": "2023-02-20T19:50:30.776758Z",
+    "license": "MIT",
+    "name": "GeneticsMakie.jl",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC786",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: With the continued deluge of results from genome-wide association and functional genomic studies, it has become increasingly imperative to quickly combine and visualize different layers of genetic and genomic data within a given locus to facilitate exploratory and integrative data analyses. While several tools have been developed to visualize locus-level genetic results, the limited speed, scalability and flexibility of current approaches remain a significant bottleneck. Here, we present a Julia package for high-performance genetics and genomics-related data visualization that enables fast, simultaneous plotting of hundreds of association results along with multiple relevant genomic annotations. Leveraging the powerful plotting and layout utilities from Makie.jl facilitates the customization and extensibility of every component of a plot, enabling generation of publication-ready figures. AVAILABILITY AND IMPLEMENTATION: The GeneticsMakie.jl package is open source and distributed under the MIT license via GitHub (https://github.com/mmkim1210/GeneticsMakie.jl). The GitHub repository contains installation instructions as well as examples and documentation for built-in functions. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Gandal M.J."
+                    },
+                    {
+                        "name": "Jops C.T."
+                    },
+                    {
+                        "name": "Kim M."
+                    },
+                    {
+                        "name": "Kumagai M.E."
+                    },
+                    {
+                        "name": "Vo D.D."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic data"
+            },
+            "pmcid": "PMC9825774",
+            "pmid": "36495218"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        }
+    ]
+}
diff --git a/data/genie_web/genie_web.biotools.json b/data/genie_web/genie_web.biotools.json
new file mode 100644
index 0000000000000..05c1164c7c399
--- /dev/null
+++ b/data/genie_web/genie_web.biotools.json
@@ -0,0 +1,120 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T15:33:10.352003Z",
+    "biotoolsCURIE": "biotools:genie_web",
+    "biotoolsID": "genie_web",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mwilsons@asu.edu",
+            "name": "Melissa A. Wilson",
+            "orcidid": "https://orcid.org/0000-0002-2614-0285",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "cartwright@asu.edu",
+            "name": "Reed A. Cartwright",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Andreina I. Castillo"
+        },
+        {
+            "name": "Ben H. Roos"
+        },
+        {
+            "name": "Michael S. Rosenberg"
+        }
+    ],
+    "description": "An interactive real-time simulation for teaching genetic drift.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Allele frequency distribution analysis",
+                    "uri": "http://edamontology.org/operation_0554"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cartwrig.ht/apps/genie/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2022-12-31T15:33:10.354595Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/AndreinaCastillo/Genie_manuscript_data_analysis"
+        }
+    ],
+    "name": "Genie",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12052-022-00161-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Neutral evolution is a fundamental concept in evolutionary biology but teaching this and other non-adaptive concepts is especially challenging. Here we present Genie, a browser-based educational tool that demonstrates population-genetic concepts such as genetic drift, population isolation, gene flow, and genetic mutation. Because it does not need to be downloaded and installed, Genie can scale to large groups of students and is useful for both in-person and online instruction. Genie was used to teach genetic drift to Evolution students at Arizona State University during Spring 2016 and Spring 2017. The effectiveness of Genie to teach key genetic drift concepts and misconceptions was assessed with the Genetic Drift Inventory developed by Price et al. (CBE Life Sci Educ 13(1):65–75, 2014). Overall, Genie performed comparably to that of traditional static methods across all evaluated classes. We have empirically demonstrated that Genie can be successfully integrated with traditional instruction to reduce misconceptions about genetic drift.",
+                "authors": [
+                    {
+                        "name": "Cartwright R.A."
+                    },
+                    {
+                        "name": "Castillo A.I."
+                    },
+                    {
+                        "name": "Roos B.H."
+                    },
+                    {
+                        "name": "Rosenberg M.S."
+                    },
+                    {
+                        "name": "Wilson M.A."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Evolution: Education and Outreach",
+                "title": "Genie: an interactive real-time simulation for teaching genetic drift"
+            },
+            "pmcid": "PMC9555832",
+            "pmid": "36237301"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Physics",
+            "uri": "http://edamontology.org/topic_3318"
+        },
+        {
+            "term": "Population genetics",
+            "uri": "http://edamontology.org/topic_3056"
+        }
+    ]
+}
diff --git a/data/genomepaint/genomepaint.biotools.json b/data/genomepaint/genomepaint.biotools.json
new file mode 100644
index 0000000000000..4a558d49daf77
--- /dev/null
+++ b/data/genomepaint/genomepaint.biotools.json
@@ -0,0 +1,157 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T23:46:42.535571Z",
+    "biotoolsCURIE": "biotools:genomepaint",
+    "biotoolsID": "genomepaint",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jinghui.zhang@stjude.org",
+            "name": "Jinghui Zhang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xin.zhou@stjude.org",
+            "name": "Xin Zhou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jian Wang"
+        },
+        {
+            "name": "John Easton"
+        }
+    ],
+    "description": "Exploration of Coding and Non-coding Variants in Cancer Using GenomePaint.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://genomepaint.stjude.cloud/",
+    "lastUpdate": "2023-01-26T23:46:42.538157Z",
+    "name": "GenomePaint",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CCELL.2020.12.011",
+            "metadata": {
+                "abstract": "© 2020 Elsevier Inc.GenomePaint (https://genomepaint.stjude.cloud/) is an interactive visualization platform for whole-genome, whole-exome, transcriptome, and epigenomic data of tumor samples. Its design captures the inter-relatedness between DNA variations and RNA expression, supporting in-depth exploration of both individual cancer genomes and full cohorts. Regulatory non-coding variants can be inspected and analyzed along with coding variants, and their functional impact further explored by examining 3D genome data from cancer cell lines. Further, GenomePaint correlates mutation and expression patterns with patient outcomes, and supports custom data upload. We used GenomePaint to unveil aberrant splicing that disrupts the RING domain of CREBBP, discover cis activation of the MYC oncogene by duplication of the NOTCH1-MYC enhancer in B-lineage acute lymphoblastic leukemia, and explore the inter- and intra-tumor heterogeneity at EGFR in adult glioblastomas. These examples demonstrate that deep multi-omics exploration of individual cancer genomes enabled by GenomePaint can lead to biological insights for follow-up validation. © 2020 Elsevier Inc.Zhou et al. develop GenomePaint for visualizing coding and non-coding variants in cancer. With a primary focus on pediatric cancer, GenomePaint enables detection of common and rare driver variants within cancer subtypes and discovery of novel oncogenic events by integrating DNA, RNA, and epigenetic data from individual cancer genomes.",
+                "authors": [
+                    {
+                        "name": "Baker S.J."
+                    },
+                    {
+                        "name": "Brady S.W."
+                    },
+                    {
+                        "name": "Easton J."
+                    },
+                    {
+                        "name": "Edmonson M.N."
+                    },
+                    {
+                        "name": "Flasch D."
+                    },
+                    {
+                        "name": "Li C."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Ma X."
+                    },
+                    {
+                        "name": "Newman S."
+                    },
+                    {
+                        "name": "Patel J."
+                    },
+                    {
+                        "name": "Paul R."
+                    },
+                    {
+                        "name": "Rusch M.C."
+                    },
+                    {
+                        "name": "Shao Y."
+                    },
+                    {
+                        "name": "Sioson E."
+                    },
+                    {
+                        "name": "Tian L."
+                    },
+                    {
+                        "name": "Valentine M."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Zhang J."
+                    },
+                    {
+                        "name": "Zhou X."
+                    }
+                ],
+                "citationCount": 10,
+                "date": "2021-01-11T00:00:00Z",
+                "journal": "Cancer Cell",
+                "title": "Exploration of Coding and Non-coding Variants in Cancer Using GenomePaint"
+            },
+            "pmcid": "PMC7884056",
+            "pmid": "33434514"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/genomesidekick/genomesidekick.biotools.json b/data/genomesidekick/genomesidekick.biotools.json
new file mode 100644
index 0000000000000..2645eaf015c4b
--- /dev/null
+++ b/data/genomesidekick/genomesidekick.biotools.json
@@ -0,0 +1,103 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-19T10:31:01.688390Z",
+    "biotoolsCURIE": "biotools:genomesidekick",
+    "biotoolsID": "genomesidekick",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dchapski@ucla.edu",
+            "name": "Douglas J. Chapski",
+            "orcidid": "http://orcid.org/0000-0002-6730-7627",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ashley J. Zhu"
+        },
+        {
+            "name": "Junjie Chen"
+        },
+        {
+            "name": "René R. Sevag Packard"
+        },
+        {
+            "name": "Thomas M. Vondriska"
+        }
+    ],
+    "description": "The genomeSidekick data analysis tool is a simple and efficient application that allows users to analyze and visualize RNA-seq and ATAC-seq data without having to learn the nitty gritty bioinformatics. This document will provide a comprehensive overview of the functions and capabilities of each tab within the application. For your convenience, the app can be used both online as a website or locally run in your RStudio. If you run into any problems while using the app in RStudio, refer to the Troubleshooting section to see some common errors and solutions that may occur.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "RNA-Seq analysis",
+                    "uri": "http://edamontology.org/operation_3680"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://genomesidekick.shinyapps.io/genomesidekick/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-19T10:31:01.690754Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://www.github.com/dchapski/genomeSidekick"
+        }
+    ],
+    "name": "genomeSidekick",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/fbinf.2022.831025",
+            "pmcid": "PMC9580848",
+            "pmid": "36304311"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/genomickb/genomickb.biotools.json b/data/genomickb/genomickb.biotools.json
new file mode 100644
index 0000000000000..50962c885c101
--- /dev/null
+++ b/data/genomickb/genomickb.biotools.json
@@ -0,0 +1,117 @@
+{
+    "additionDate": "2023-01-28T13:54:40.841859Z",
+    "biotoolsCURIE": "biotools:genomickb",
+    "biotoolsID": "genomickb",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "drjieliu@umich.edu",
+            "name": "Jie Liu",
+            "orcidid": "https://orcid.org/0000-0002-9504-0587",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Genomic Knowledgebase (GenomicKB) is a graph database which use a knowledge graph to consolidates genomic datasets and annotations from over 30 consortia and portals.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                },
+                {
+                    "term": "Relation extraction",
+                    "uri": "http://edamontology.org/operation_3625"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://gkb.dcmb.med.umich.edu/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T13:54:40.845230Z",
+    "license": "Not licensed",
+    "name": "GenomicKB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC957",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Genomic Knowledgebase (GenomicKB) is a graph database for researchers to explore and investigate human genome, epigenome, transcriptome, and 4D nucleome with simple and efficient queries. The database uses a knowledge graph to consolidate genomic datasets and annotations from over 30 consortia and portals, including 347 million genomic entities, 1.36 billion relations, and 3.9 billion entity and relation properties. GenomicKB is equipped with a web-based query system (https://gkb.dcmb.med.umich.edu/) which allows users to query the knowledge graph with customized graph patterns and specific constraints on entities and relations. Compared with traditional tabular-structured data stored in separate data portals, GenomicKB emphasizes the relations among genomic entities, intuitively connects isolated data matrices, and supports efficient queries for scientific discoveries. GenomicKB transforms complicated analysis among multiple genomic entities and relations into coding-free queries, and facilitates data-driven genomic discoveries in the future.",
+                "authors": [
+                    {
+                        "name": "Feng F."
+                    },
+                    {
+                        "name": "Gao Y."
+                    },
+                    {
+                        "name": "Huang Y."
+                    },
+                    {
+                        "name": "Li T."
+                    },
+                    {
+                        "name": "Liu J."
+                    },
+                    {
+                        "name": "Tang F."
+                    },
+                    {
+                        "name": "Yang S."
+                    },
+                    {
+                        "name": "Yao Y."
+                    },
+                    {
+                        "name": "Zhu D."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "GenomicKB: a knowledge graph for the human genome"
+            },
+            "pmcid": "PMC9825430",
+            "pmid": "36318240"
+        }
+    ],
+    "toolType": [
+        "Desktop application",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Human biology",
+            "uri": "http://edamontology.org/topic_2815"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/genomicus-metazoa/genomicus-metazoa.biotools.json b/data/genomicus-metazoa/genomicus-metazoa.biotools.json
index 1fece128c3264..add192b5fc750 100644
--- a/data/genomicus-metazoa/genomicus-metazoa.biotools.json
+++ b/data/genomicus-metazoa/genomicus-metazoa.biotools.json
@@ -69,7 +69,7 @@
         }
     ],
     "homepage": "http://www.genomicus.biologie.ens.fr/genomicus-metazoa/",
-    "lastUpdate": "2020-06-16T10:55:27Z",
+    "lastUpdate": "2023-03-03T08:58:46.160733Z",
     "link": [
         {
             "note": "Genomicus Twitter",
@@ -111,33 +111,6 @@
     "name": "Genomicus-metazoa",
     "owner": "alouis",
     "publication": [
-        {
-            "doi": "10.1093/bioinformatics/btq079",
-            "metadata": {
-                "abstract": "Summary: Comparative genomics remains a pivotal strategy to study the evolution of gene organization, and this primacy is reinforced by the growing number of full genome sequences available in public repositories. Despite this growth, bioinformatic tools available to visualize and compare genomes and to infer evolutionary events remain restricted to two or three genomes at a time, thus limiting the breadth and the nature of the question that can be investigated. Here we present Genomicus, a new synteny browser that can represent and compare unlimited numbers of genomes in a broad phylogenetic view. In addition, Genomicus includes reconstructed ancestral gene organization, thus greatly facilitating the interpretation of the data. Availability: Genomicus is freely available for online use at http://www.dyogen.ens.fr/genomicus while data can be downloaded at ftp://ftp.biologie.ens.fr/pub/dyogen/genomicus. Contact: hrc@biologie.ens.fr. © The Author(s) 2010. Published by Oxford University Press.",
-                "authors": [
-                    {
-                        "name": "Crollius H.R."
-                    },
-                    {
-                        "name": "Louis A."
-                    },
-                    {
-                        "name": "Muffato M."
-                    },
-                    {
-                        "name": "Poisnel C.-E."
-                    }
-                ],
-                "citationCount": 161,
-                "date": "2010-02-24T00:00:00Z",
-                "journal": "Bioinformatics",
-                "title": "Genomicus: A database and a browser to study gene synteny in modern and ancestral genomes"
-            },
-            "type": [
-                "Other"
-            ]
-        },
         {
             "doi": "10.1093/nar/gks1156",
             "metadata": {
@@ -153,7 +126,7 @@
                         "name": "Muffato M."
                     }
                 ],
-                "citationCount": 122,
+                "citationCount": 134,
                 "date": "2013-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "Genomicus: Five genome browsers for comparative genomics in eukaryota"
@@ -165,7 +138,7 @@
         {
             "doi": "10.1093/nar/gku1112",
             "metadata": {
-                "abstract": "© The Author(s) 2014.The Genomicus web server (http://www.genomicus. biologie.ens.fr/genomicus) is a visualization tool allowing comparative genomics in four different phyla (Vertebrate, Fungi, Metazoan and Plants). It provides access to genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants. Here we present the new features available for vertebrate genome with a focus on new graphical tools. The interface to enter the database has been improved, two pairwise genome comparison tools are now available (KaryoView and MatrixView) and the multiple genome comparison tools (PhyloView and Align-View) propose three new kinds of representation and a more intuitive menu. These new developments have been implemented for Genomicus portal dedicated to vertebrates. This allows the analysis of 68 extant animal genomes, as well as 58 ancestral reconstructed genomes. The Genomicus server also provides access to ancestral gene orders, to facilitate evolutionary and comparative genomics studies, as well as computationally predicted regulatory interactions, thanks to the representation of conserved noncoding elements with their putative gene targets.",
+                "abstract": "The Genomicus web server (http://www.genomicus. biologie.ens.fr/genomicus) is a visualization tool allowing comparative genomics in four different phyla (Vertebrate, Fungi, Metazoan and Plants). It provides access to genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants. Here we present the new features available for vertebrate genome with a focus on new graphical tools. The interface to enter the database has been improved, two pairwise genome comparison tools are now available (KaryoView and MatrixView) and the multiple genome comparison tools (PhyloView and Align-View) propose three new kinds of representation and a more intuitive menu. These new developments have been implemented for Genomicus portal dedicated to vertebrates. This allows the analysis of 68 extant animal genomes, as well as 58 ancestral reconstructed genomes. The Genomicus server also provides access to ancestral gene orders, to facilitate evolutionary and comparative genomics studies, as well as computationally predicted regulatory interactions, thanks to the representation of conserved noncoding elements with their putative gene targets.",
                 "authors": [
                     {
                         "name": "Crollius H.R."
@@ -180,7 +153,7 @@
                         "name": "Nguyen N.T.T."
                     }
                 ],
-                "citationCount": 78,
+                "citationCount": 84,
                 "date": "2015-01-28T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "Genomicus update 2015: KaryoView and MatrixView provide a genome-wide perspective to multispecies comparative genomics"
@@ -192,7 +165,7 @@
         {
             "doi": "10.1093/nar/gkx1003",
             "metadata": {
-                "abstract": "© 2017 The Author(s).Since 2010, the Genomicus web server is available online at http://genomicus.biologie.ens.fr/genomicus. This graphical browser provides access to comparative genomic analyses in four different phyla (Vertebrate, Plants, Fungi, and non vertebrate Metazoans). Users can analyse genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants, in an integrated evolutionary context. New analyses and visualization tools have recently been implemented in Genomicus Vertebrate. Karyotype structures from several genomes can now be compared along an evolutionary pathway (Multi-KaryotypeView), and synteny blocks can be computed and visualized between any two genomes (PhylDiagView).",
+                "abstract": "Since 2010, the Genomicus web server is available online at http://genomicus.biologie.ens.fr/genomicus. This graphical browser provides access to comparative genomic analyses in four different phyla (Vertebrate, Plants, Fungi, and non vertebrate Metazoans). Users can analyse genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants, in an integrated evolutionary context. New analyses and visualization tools have recently been implemented in Genomicus Vertebrate. Karyotype structures from several genomes can now be compared along an evolutionary pathway (Multi-KaryotypeView), and synteny blocks can be computed and visualized between any two genomes (PhylDiagView).",
                 "authors": [
                     {
                         "name": "Crollius H.R."
@@ -207,7 +180,7 @@
                         "name": "Vincens P."
                     }
                 ],
-                "citationCount": 44,
+                "citationCount": 75,
                 "date": "2018-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "Genomicus 2018: Karyotype evolutionary trees and on-the-fly synteny computing"
@@ -215,6 +188,12 @@
             "type": [
                 "Other"
             ]
+        },
+        {
+            "doi": "10.1093/bioinformatics/btq079",
+            "type": [
+                "Other"
+            ]
         }
     ],
     "relation": [
diff --git a/data/genomicus/genomicus.biotools.json b/data/genomicus/genomicus.biotools.json
index 801c4389f4f56..54f0186009c10 100644
--- a/data/genomicus/genomicus.biotools.json
+++ b/data/genomicus/genomicus.biotools.json
@@ -98,7 +98,7 @@
         }
     ],
     "homepage": "http://www.genomicus.biologie.ens.fr/genomicus/",
-    "lastUpdate": "2022-09-14T11:32:15.411640Z",
+    "lastUpdate": "2023-03-06T14:10:23.175189Z",
     "link": [
         {
             "note": "Genomicus Twitter",
@@ -140,37 +140,9 @@
     "name": "GENOMICUS",
     "owner": "alouis",
     "publication": [
-        {
-            "doi": "10.1093/bioinformatics/btq079",
-            "metadata": {
-                "abstract": "Summary: Comparative genomics remains a pivotal strategy to study the evolution of gene organization, and this primacy is reinforced by the growing number of full genome sequences available in public repositories. Despite this growth, bioinformatic tools available to visualize and compare genomes and to infer evolutionary events remain restricted to two or three genomes at a time, thus limiting the breadth and the nature of the question that can be investigated. Here we present Genomicus, a new synteny browser that can represent and compare unlimited numbers of genomes in a broad phylogenetic view. In addition, Genomicus includes reconstructed ancestral gene organization, thus greatly facilitating the interpretation of the data. Availability: Genomicus is freely available for online use at http://www.dyogen.ens.fr/genomicus while data can be downloaded at ftp://ftp.biologie.ens.fr/pub/dyogen/genomicus. Contact: hrc@biologie.ens.fr. © The Author(s) 2010. Published by Oxford University Press.",
-                "authors": [
-                    {
-                        "name": "Crollius H.R."
-                    },
-                    {
-                        "name": "Louis A."
-                    },
-                    {
-                        "name": "Muffato M."
-                    },
-                    {
-                        "name": "Poisnel C.-E."
-                    }
-                ],
-                "citationCount": 173,
-                "date": "2010-02-24T00:00:00Z",
-                "journal": "Bioinformatics",
-                "title": "Genomicus: A database and a browser to study gene synteny in modern and ancestral genomes"
-            },
-            "type": [
-                "Primary"
-            ]
-        },
         {
             "doi": "10.1093/nar/gkab1091",
             "metadata": {
-                "abstract": "© 2022 The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.Genomicus is a database and web-server dedicated to comparative genomics in eukaryotes. Its main functionality is to graphically represent the conservation of genomic blocks between multiple genomes, locally around a specific gene of interest or genome-wide through karyotype comparisons. Since 2010 and its first release, Genomicus has synchronized with 60 Ensembl releases and seen the addition of functions that have expanded the type of analyses that users can perform. Today, five public instances of Genomicus are supporting a total number of 1029 extant genomes and 621 ancestral reconstructions from all eukaryotes kingdoms available in Ensembl and Ensembl Genomes databases complemented with four additional instances specific to taxonomic groups of interest. New visualization and query tools are described in this manuscript. Genomicus is freely available at http://www.genomicus.bio.ens.psl.eu/genomicus.",
                 "authors": [
                     {
                         "name": "Dufayard J.F."
@@ -187,11 +159,7 @@
                     {
                         "name": "Vincens P."
                     }
-                ],
-                "citationCount": 2,
-                "date": "2022-01-07T00:00:00Z",
-                "journal": "Nucleic Acids Research",
-                "title": "Genomicus in 2022: Comparative tools for thousands of genomes and reconstructed ancestors"
+                ]
             },
             "type": [
                 "Primary"
@@ -200,7 +168,6 @@
         {
             "doi": "10.1093/nar/gks1156",
             "metadata": {
-                "abstract": "Genomicus (http://www.dyogen.ens.fr/genomicus/) is a database and an online tool that allows easy comparative genomic visualization in >150 eukaryote genomes. It provides a way to explore spatial information related to gene organization within and between genomes and temporal relationships related to gene and genome evolution. For the specific vertebrate phylum, it also provides access to ancestral gene order reconstructions and conserved non-coding elements information. We extended the Genomicus database originally dedicated to vertebrate to four new clades, including plants, non-vertebrate metazoa, protists and fungi. This visualization tool allows evolutionary phylogenomics analysis and exploration. Here, we describe the graphical modules of Genomicus and show how it is capable of revealing differential gene loss and gain, segmental or genome duplications and study the evolution of a locus through homology relationships. © The Author(s) 2012.",
                 "authors": [
                     {
                         "name": "Crollius H.R."
@@ -211,11 +178,7 @@
                     {
                         "name": "Muffato M."
                     }
-                ],
-                "citationCount": 132,
-                "date": "2013-01-01T00:00:00Z",
-                "journal": "Nucleic Acids Research",
-                "title": "Genomicus: Five genome browsers for comparative genomics in eukaryota"
+                ]
             },
             "type": [
                 "Other"
@@ -224,7 +187,6 @@
         {
             "doi": "10.1093/nar/gku1112",
             "metadata": {
-                "abstract": "© The Author(s) 2014.The Genomicus web server (http://www.genomicus. biologie.ens.fr/genomicus) is a visualization tool allowing comparative genomics in four different phyla (Vertebrate, Fungi, Metazoan and Plants). It provides access to genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants. Here we present the new features available for vertebrate genome with a focus on new graphical tools. The interface to enter the database has been improved, two pairwise genome comparison tools are now available (KaryoView and MatrixView) and the multiple genome comparison tools (PhyloView and Align-View) propose three new kinds of representation and a more intuitive menu. These new developments have been implemented for Genomicus portal dedicated to vertebrates. This allows the analysis of 68 extant animal genomes, as well as 58 ancestral reconstructed genomes. The Genomicus server also provides access to ancestral gene orders, to facilitate evolutionary and comparative genomics studies, as well as computationally predicted regulatory interactions, thanks to the representation of conserved noncoding elements with their putative gene targets.",
                 "authors": [
                     {
                         "name": "Crollius H.R."
@@ -238,11 +200,7 @@
                     {
                         "name": "Nguyen N.T.T."
                     }
-                ],
-                "citationCount": 83,
-                "date": "2015-01-28T00:00:00Z",
-                "journal": "Nucleic Acids Research",
-                "title": "Genomicus update 2015: KaryoView and MatrixView provide a genome-wide perspective to multispecies comparative genomics"
+                ]
             },
             "type": [
                 "Other"
@@ -251,7 +209,6 @@
         {
             "doi": "10.1093/nar/gkx1003",
             "metadata": {
-                "abstract": "© 2017 The Author(s).Since 2010, the Genomicus web server is available online at http://genomicus.biologie.ens.fr/genomicus. This graphical browser provides access to comparative genomic analyses in four different phyla (Vertebrate, Plants, Fungi, and non vertebrate Metazoans). Users can analyse genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants, in an integrated evolutionary context. New analyses and visualization tools have recently been implemented in Genomicus Vertebrate. Karyotype structures from several genomes can now be compared along an evolutionary pathway (Multi-KaryotypeView), and synteny blocks can be computed and visualized between any two genomes (PhylDiagView).",
                 "authors": [
                     {
                         "name": "Crollius H.R."
@@ -265,18 +222,24 @@
                     {
                         "name": "Vincens P."
                     }
-                ],
-                "citationCount": 63,
-                "date": "2018-01-01T00:00:00Z",
-                "journal": "Nucleic Acids Research",
-                "title": "Genomicus 2018: Karyotype evolutionary trees and on-the-fly synteny computing"
+                ]
             },
             "type": [
                 "Other"
             ]
+        },
+        {
+            "doi": "10.1093/bioinformatics/btq079",
+            "type": [
+                "Primary"
+            ]
         }
     ],
     "relation": [
+        {
+            "biotoolsID": "agora",
+            "type": "uses"
+        },
         {
             "biotoolsID": "ensembl",
             "type": "uses"
diff --git a/data/geospm/geospm.biotools.json b/data/geospm/geospm.biotools.json
new file mode 100644
index 0000000000000..08a84741d5f65
--- /dev/null
+++ b/data/geospm/geospm.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T19:55:14.028501Z",
+    "biotoolsCURIE": "biotools:geospm",
+    "biotoolsID": "geospm",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "h.engleitner@ucl.ac.uk",
+            "name": "Holger Engleitner",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "p.nachev@ucl.ac.uk",
+            "name": "Parashkev Nachev",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "GeoSPM allows the spatial analysis of diverse geographic point data. It draws upon differential geometry and random field theory, by leveraging the procedures used in statistical parametric mapping (SPM): a framework for making topological inferences about spatially structured effects, with well-behaved spatial dependencies.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/high-dimensional/geospm",
+    "language": [
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-02-20T19:55:14.030891Z",
+    "license": "GPL-3.0",
+    "name": "GeoSPM",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.PATTER.2022.100656",
+            "metadata": {
+                "abstract": "© 2022 The Author(s)The characteristics and determinants of health and disease are often organized in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial relations. Drawing on statistical parametric mapping, a framework for topological inference well established in the realm of neuroimaging, we propose and validate an approach to the spatial analysis of diverse clinical data—GeoSPM—based on differential geometry and random field theory. We evaluate GeoSPM across an extensive array of synthetic simulations encompassing diverse spatial relationships, sampling, and corruption by noise, and demonstrate its application on large-scale data from UK Biobank. GeoSPM is readily interpretable, can be implemented with ease by non-specialists, enables flexible modeling of complex spatial relations, exhibits robustness to noise and under-sampling, offers principled criteria of statistical significance, and is through computational efficiency readily scalable to large datasets. We provide a complete, open-source software implementation.",
+                "authors": [
+                    {
+                        "name": "Engleitner H."
+                    },
+                    {
+                        "name": "Friston K."
+                    },
+                    {
+                        "name": "Herron D."
+                    },
+                    {
+                        "name": "Jha A."
+                    },
+                    {
+                        "name": "Nachev P."
+                    },
+                    {
+                        "name": "Nelson A."
+                    },
+                    {
+                        "name": "Pinilla M.S."
+                    },
+                    {
+                        "name": "Rees G."
+                    },
+                    {
+                        "name": "Rossor M."
+                    }
+                ],
+                "date": "2022-12-09T00:00:00Z",
+                "journal": "Patterns",
+                "title": "GeoSPM: Geostatistical parametric mapping for medicine"
+            },
+            "pmcid": "PMC9768692",
+            "pmid": "36569555"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Medical informatics",
+            "uri": "http://edamontology.org/topic_3063"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        }
+    ]
+}
diff --git a/data/ggmob/ggmob.biotools.json b/data/ggmob/ggmob.biotools.json
new file mode 100644
index 0000000000000..1ede837225ae4
--- /dev/null
+++ b/data/ggmob/ggmob.biotools.json
@@ -0,0 +1,122 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-25T13:22:23.617281Z",
+    "biotoolsCURIE": "biotools:ggmob",
+    "biotoolsID": "ggmob",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "nnoyes@umn.edu",
+            "name": "Noelle R. Noyes",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Elucidation of genomic conjugative features and associated cargo genes across bacterial genera using genus-genus mobilization networks.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Filtering",
+                    "uri": "http://edamontology.org/operation_3695"
+                },
+                {
+                    "term": "Network visualisation",
+                    "uri": "http://edamontology.org/operation_3925"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ruiz-hci-lab.github.io/ggMOB/",
+    "lastUpdate": "2023-02-25T13:22:23.619969Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/Ruiz-HCI-Lab/ggMOB"
+        }
+    ],
+    "name": "ggMOB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FGENE.2022.1024577",
+            "metadata": {
+                "abstract": "Copyright © 2022 Nayar, Terrizzano, Seabolt, Agarwal, Boucher, Ruiz, Slizovskiy, Kaufman and Noyes.Horizontal gene transfer mediated by conjugation is considered an important evolutionary mechanism of bacteria. It allows organisms to quickly evolve new phenotypic properties including antimicrobial resistance (AMR) and virulence. The frequency of conjugation-mediated cargo gene exchange has not yet been comprehensively studied within and between bacterial taxa. We developed a frequency-based network of genus-genus conjugation features and candidate cargo genes from whole-genome sequence data of over 180,000 bacterial genomes, representing 1,345 genera. Using our method, which we refer to as ggMOB, we revealed that over half of the bacterial genomes contained one or more known conjugation features that matched exactly to at least one other genome. Moreover, the proportion of genomes containing these conjugation features varied substantially by genus and conjugation feature. These results and the genus-level network structure can be viewed interactively in the ggMOB interface, which allows for user-defined filtering of conjugation features and candidate cargo genes. Using the network data, we observed that the ratio of AMR gene representation in conjugative versus non-conjugative genomes exceeded 5:1, confirming that conjugation is a critical force for AMR spread across genera. Finally, we demonstrated that clustering genomes by conjugation profile sometimes correlated well with classical phylogenetic structuring; but that in some cases the clustering was highly discordant, suggesting that the importance of the accessory genome in driving bacterial evolution may be highly variable across both time and taxonomy. These results can advance scientific understanding of bacterial evolution, and can be used as a starting point for probing genus-genus gene exchange within complex microbial communities that include unculturable bacteria. ggMOB is publicly available under the GNU licence at https://ruiz-hci-lab.github.io/ggMOB/.",
+                "authors": [
+                    {
+                        "name": "Agarwal A."
+                    },
+                    {
+                        "name": "Boucher C."
+                    },
+                    {
+                        "name": "Kaufman J.H."
+                    },
+                    {
+                        "name": "Nayar G."
+                    },
+                    {
+                        "name": "Noyes N.R."
+                    },
+                    {
+                        "name": "Ruiz J."
+                    },
+                    {
+                        "name": "Seabolt E."
+                    },
+                    {
+                        "name": "Slizovskiy I.B."
+                    },
+                    {
+                        "name": "Terrizzano I."
+                    }
+                ],
+                "date": "2022-12-08T00:00:00Z",
+                "journal": "Frontiers in Genetics",
+                "title": "ggMOB: Elucidation of genomic conjugative features and associated cargo genes across bacterial genera using genus-genus mobilization networks"
+            },
+            "pmcid": "PMC9779932",
+            "pmid": "36568361"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Mobile genetic elements",
+            "uri": "http://edamontology.org/topic_0798"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Taxonomy",
+            "uri": "http://edamontology.org/topic_0637"
+        }
+    ]
+}
diff --git a/data/ggmotif/ggmotif.biotools.json b/data/ggmotif/ggmotif.biotools.json
new file mode 100644
index 0000000000000..430766445e549
--- /dev/null
+++ b/data/ggmotif/ggmotif.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T13:56:44.338082Z",
+    "biotoolsCURIE": "biotools:ggmotif",
+    "biotoolsID": "ggmotif",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "absklhhc@gmail.com",
+            "name": "Huichuan Huang",
+            "orcidid": "https://orcid.org/0000-0002-8400-7116",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An R Package for the extraction and visualization of motifs from MEME software.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phylogenetic inference",
+                    "uri": "http://edamontology.org/operation_0323"
+                },
+                {
+                    "term": "Phylogenetic tree editing",
+                    "uri": "http://edamontology.org/operation_0326"
+                },
+                {
+                    "term": "Phylogenetic tree visualisation",
+                    "uri": "http://edamontology.org/operation_0567"
+                },
+                {
+                    "term": "Sequence motif recognition",
+                    "uri": "http://edamontology.org/operation_0239"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/lixiang117423/ggmotif",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-28T13:56:44.340770Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://CRAN.R-project.org/package=ggmotif"
+        }
+    ],
+    "name": "ggmotif",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PONE.0276979",
+            "metadata": {
+                "abstract": "© 2022 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.MEME (Multiple Em for Motif Elicitation) is the most commonly used tool to identify motifs within deoxyribonucleic acid (DNA) or protein sequences. However, the results generated by the MEMEare saved using file formats .xml and .txt, which are difficult to read, visualize, or integrate with other widely used phylogenetic tree packages, such as ggtree. To overcome this problem, we developed the ggmotif R package, which provides two easy-to-use functions that can facilitate the extraction and visualization of motifs from the results files generated by the MEME. ggmotif can extract the information of the location of motif(s) on the corresponding sequence(s) from the .xml format file and visualize it. Additionally, the data extracted by ggmotif can be easily integrated with the phylogenetic data. On the other hand, ggmotif can obtain the sequence of each motif from the .txt format file and draw the sequence logo with the function ggseqlogo from the ggseqlogo R package. The ggmotif R package is freely available (including examples and vignettes) from GitHub at https://github. com/lixiang117423/ggmotif or from CRAN at https://CRAN.R-project.org/package=ggmotif.",
+                "authors": [
+                    {
+                        "name": "Huang H."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Ma L."
+                    },
+                    {
+                        "name": "Mei X."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "ggmotif: An R Package for the extraction and visualization of motifs from MEME software"
+            },
+            "pmcid": "PMC9632824",
+            "pmid": "36327240"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/ggplot2/ggplot2.biotools.json b/data/ggplot2/ggplot2.biotools.json
index dfd22de811e75..49cae2573939e 100644
--- a/data/ggplot2/ggplot2.biotools.json
+++ b/data/ggplot2/ggplot2.biotools.json
@@ -37,11 +37,10 @@
         }
     ],
     "homepage": "http://ggplot2.org/",
-    "homepage_status": 1,
     "language": [
         "R"
     ],
-    "lastUpdate": "2018-12-10T12:58:55Z",
+    "lastUpdate": "2023-01-17T02:43:35.495397Z",
     "name": "ggplot2",
     "operatingSystem": [
         "Linux",
@@ -54,6 +53,12 @@
             "doi": "10.1007/978-3-319-24277-4"
         }
     ],
+    "relation": [
+        {
+            "biotoolsID": "ggtranscript",
+            "type": "usedBy"
+        }
+    ],
     "toolType": [
         "Library"
     ],
diff --git a/data/ggtranscript/ggtranscript.biotools.json b/data/ggtranscript/ggtranscript.biotools.json
new file mode 100644
index 0000000000000..f2411f4736d05
--- /dev/null
+++ b/data/ggtranscript/ggtranscript.biotools.json
@@ -0,0 +1,132 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T02:41:54.108279Z",
+    "biotoolsCURIE": "biotools:ggtranscript",
+    "biotoolsID": "ggtranscript",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "David Zhang",
+            "orcidid": "http://orcid.org/0000-0003-2382-8460"
+        },
+        {
+            "name": "Emil K. Gustavsson",
+            "orcidid": "http://orcid.org/0000-0003-0541-7537"
+        },
+        {
+            "name": "Mina Ryten",
+            "orcidid": "http://orcid.org/0000-0001-9520-6957"
+        },
+        {
+            "name": "Regina H. Reynolds",
+            "orcidid": "http://orcid.org/0000-0001-6470-7919"
+        },
+        {
+            "name": "Sonia Garcia-Ruiz",
+            "orcidid": "http://orcid.org/0000-0003-4913-5312"
+        }
+    ],
+    "description": "An R package for the visualization and interpretation of transcript isoforms using ggplot2.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://dzhang32.github.io/ggtranscript/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/dzhang32/ggtranscript/tree/v0.99.3",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-17T02:43:14.074635Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://doi.org/10.5281/zenodo.6374061"
+        }
+    ],
+    "name": "ggtranscript",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac409",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press.Motivation: The advent of long-read sequencing technologies has increased demand for the visualization and interpretation of transcripts. However, tools that perform such visualizations remain inflexible and lack the ability to easily identify differences between transcript structures. Here, we introduce ggtranscript, an R package that provides a fast and flexible method to visualize and compare transcripts. As a ggplot2 extension, ggtranscript inherits the functionality and familiarity of ggplot2 making it easy to use.",
+                "authors": [
+                    {
+                        "name": "Garcia-Ruiz S."
+                    },
+                    {
+                        "name": "Gustavsson E.K."
+                    },
+                    {
+                        "name": "Reynolds R.H."
+                    },
+                    {
+                        "name": "Ryten M."
+                    },
+                    {
+                        "name": "Zhang D."
+                    }
+                ],
+                "citationCount": 4,
+                "date": "2022-08-01T00:00:00Z",
+                "journal": "Bioinformatics",
+                "title": "ggtranscript: An R package for the visualization and interpretation of transcript isoforms using ggplot2"
+            },
+            "pmcid": "PMC9344834",
+            "pmid": "35751589"
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "ggplot2",
+            "type": "uses"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        }
+    ]
+}
diff --git a/data/gifdti/gifdti.biotools.json b/data/gifdti/gifdti.biotools.json
new file mode 100644
index 0000000000000..1c22dc9efe158
--- /dev/null
+++ b/data/gifdti/gifdti.biotools.json
@@ -0,0 +1,73 @@
+{
+    "additionDate": "2023-01-28T14:04:18.419955Z",
+    "biotoolsCURIE": "biotools:gifdti",
+    "biotoolsID": "gifdti",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Jianxin Wang",
+            "orcidid": "https://orcid.org/0000-0003-1516-0480"
+        }
+    ],
+    "description": "Prediction of drug-target interactions based on global molecular and intermolecular interaction representation learning.",
+    "editPermission": {
+        "type": "public"
+    },
+    "homepage": "https://github.com/zhaoqichang/GIFDTI",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T14:04:18.422455Z",
+    "license": "Not licensed",
+    "name": "GIFDTI",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1109/TCBB.2022.3225423",
+            "metadata": {
+                "abstract": "IEEEDrug discovery and drug repurposing often rely on the successful prediction of drug-target interactions (DTIs). Recent advances have shown great promise in applying deep learning to drug-target interaction prediction. One challenge in building deep learning-based models is to adequately represent drugs and proteins that encompass the fundamental local chemical environments and long-distance information among amino acids of proteins (or atoms of drugs). Another challenge is to efficiently model the intermolecular interactions between drugs and proteins, which plays vital roles in the DTIs. To this end, we propose a novel model, GIFDTI, which consists of three key components: the sequence feature extractor (CNNFormer), the global molecular feature extractor (GF), and the intermolecular interaction modeling module (IIF). Specifically, CNNFormer incorporates CNN and Transformer to capture the local patterns and encode the long-distance relationship among tokens (atoms or amino acids) in a sequence. Then, GF and IIF extract the global molecular features and the intermolecular interaction features, respectively. We evaluate GIFDTI on six realistic evaluation strategies and the results show it improves DTI prediction performance compared to state-of-the-art methods. Moreover, case studies confirm that our model can be a useful tool to accurately yield low-cost DTIs. The codes of GIFDTI are available at <uri>https://github.com/zhaoqichang/GIFDTI</uri>.",
+                "authors": [
+                    {
+                        "name": "Duan G."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Zhao H."
+                    },
+                    {
+                        "name": "Zhao Q."
+                    },
+                    {
+                        "name": "Zheng K."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
+                "title": "GIFDTI: Prediction of drug-target interactions based on global molecular and intermolecular interaction representation learning"
+            },
+            "pmid": "36445997"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/gift_imagej/gift_imagej.biotools.json b/data/gift_imagej/gift_imagej.biotools.json
new file mode 100644
index 0000000000000..ca23c07aa3f15
--- /dev/null
+++ b/data/gift_imagej/gift_imagej.biotools.json
@@ -0,0 +1,101 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T15:26:33.500444Z",
+    "biotoolsCURIE": "biotools:gift_imagej",
+    "biotoolsID": "gift_imagej",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Jennifer.huling@uni-rostock.de",
+            "name": "Jennifer Huling",
+            "orcidid": "https://orcid.org/0000-0001-5588-8922",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Niels Grabow"
+        },
+        {
+            "name": "Andreas Götz",
+            "orcidid": "https://orcid.org/0000-0003-0463-8741"
+        },
+        {
+            "name": "Sabine Illner",
+            "orcidid": "https://orcid.org/0000-0002-2033-2964"
+        }
+    ],
+    "description": "General Image Fiber Tool (GIFT) is an ImageJ macro tool which allows the users to measure the average diameter of electrospun fibers in scanning electron microscopy (SEM) images.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/IBMTRostock/GIFT",
+    "lastUpdate": "2022-12-31T15:27:15.676018Z",
+    "license": "MIT",
+    "name": "GIFT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PONE.0275528",
+            "metadata": {
+                "abstract": "© 2022 Huling et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This paper details the development and testing of the GIFT macro, which is a freely available program for ImageJ for the automated measurement of fiber diameters in SEM images of electrospun materials. The GIFT macro applies a validated method which distinguishes fiber diameters based on distance frequencies within an image. In this work, we introduce an applied version of the GIFT method which has been designed to be user-friendly while still allowing complete control over the various parameters involved in the image processing steps. The macro quickly processes large data sets and creates results that are reproducible and accurate. The program outputs both raw data and fiber diameter averages, so that the user can quickly assess the results and has the opportunity for further analysis if desired. The GIFT macro was compared directly to other software designed for fiber diameter measurements and was found to have comparable or lower average error, especially when measuring very small fibers, and reduced processing times per image. The macro, detailed instructions for use, and sample images are freely available online (https://github.com/IBMTRostock/GIFT). We believe that the GIFT macro is a valuable new tool for researchers looking to quickly, easily and reliably assess fiber diameters in electrospun materials.",
+                "authors": [
+                    {
+                        "name": "Gotz A."
+                    },
+                    {
+                        "name": "Grabow N."
+                    },
+                    {
+                        "name": "Huling J."
+                    },
+                    {
+                        "name": "Illner S."
+                    }
+                ],
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "GIFT: An ImageJ macro for automated fiber diameter quantification"
+            },
+            "pmcid": "PMC9529089",
+            "pmid": "36191031"
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "imagej",
+            "type": "uses"
+        }
+    ],
+    "toolType": [
+        "Plug-in"
+    ],
+    "topic": [
+        {
+            "term": "Electron microscopy",
+            "uri": "http://edamontology.org/topic_0611"
+        }
+    ]
+}
diff --git a/data/gigasom.jl/gigasom.jl.biotools.json b/data/gigasom.jl/gigasom.jl.biotools.json
index fc36d63081b61..12335375c2237 100644
--- a/data/gigasom.jl/gigasom.jl.biotools.json
+++ b/data/gigasom.jl/gigasom.jl.biotools.json
@@ -90,7 +90,7 @@
     "language": [
         "Julia"
     ],
-    "lastUpdate": "2022-08-23T14:09:50.569709Z",
+    "lastUpdate": "2023-01-05T08:45:18.955295Z",
     "license": "Apache-2.0",
     "link": [
         {
@@ -148,7 +148,7 @@
                         "name": "Vondrasek J."
                     }
                 ],
-                "citationCount": 3,
+                "citationCount": 4,
                 "date": "2020-11-01T00:00:00Z",
                 "journal": "GigaScience",
                 "title": "GigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets"
diff --git a/data/giloop/giloop.biotools.json b/data/giloop/giloop.biotools.json
new file mode 100644
index 0000000000000..311451db48d77
--- /dev/null
+++ b/data/giloop/giloop.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T14:07:42.668199Z",
+    "biotoolsCURIE": "biotools:giloop",
+    "biotoolsID": "giloop",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "kc.w@cityu.edu.hk",
+            "name": "Ka-Chun Wong",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "lixt314@jlu.edu.cn",
+            "name": "Xiangtao Li",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "GILoop is a deep learning model for detecting CTCF-mediated loops on Hi-C contact maps.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Loop modelling",
+                    "uri": "http://edamontology.org/operation_0481"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/fzbio/GILoop",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T14:07:42.670757Z",
+    "license": "MIT",
+    "name": "GILoop",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.ISCI.2022.105535",
+            "metadata": {
+                "abstract": "© 2022Graph and image are two common representations of Hi-C cis-contact maps. Existing computational tools have only adopted Hi-C data modeled as unitary data structures but neglected the potential advantages of synergizing the information of different views. Here we propose GILoop, a dual-branch neural network that learns from both representations to identify genome-wide CTCF-mediated loops. With GILoop, we explore the combined strength of integrating the two view representations of Hi-C data and corroborate the complementary relationship between the views. In particular, the model outperforms the state-of-the-art loop calling framework and is also more robust against low-quality Hi-C libraries. We also uncover distinct preferences for matrix density by graph-based and image-based models, revealing interesting insights into Hi-C data elucidation. Finally, along with multiple transfer-learning case studies, we demonstrate that GILoop can accurately model the organizational and functional patterns of CTCF-mediated looping across different cell lines.",
+                "authors": [
+                    {
+                        "name": "Gao T."
+                    },
+                    {
+                        "name": "Huang L."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Lin J."
+                    },
+                    {
+                        "name": "Toseef M."
+                    },
+                    {
+                        "name": "Wang F."
+                    },
+                    {
+                        "name": "Wong K.-C."
+                    },
+                    {
+                        "name": "Zheng Z."
+                    }
+                ],
+                "date": "2022-12-22T00:00:00Z",
+                "journal": "iScience",
+                "title": "GILoop: Robust chromatin loop calling across multiple sequencing depths on Hi-C data"
+            },
+            "pmcid": "PMC9700007",
+            "pmid": "36444296"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/glaucoma-trel/glaucoma-trel.biotools.json b/data/glaucoma-trel/glaucoma-trel.biotools.json
new file mode 100644
index 0000000000000..ad83a30331b5c
--- /dev/null
+++ b/data/glaucoma-trel/glaucoma-trel.biotools.json
@@ -0,0 +1,100 @@
+{
+    "additionDate": "2023-01-28T14:10:55.050207Z",
+    "biotoolsCURIE": "biotools:glaucoma-trel",
+    "biotoolsID": "glaucoma-trel",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "julio.vera-gonzalez@uk-erlangen.de",
+            "name": "Julio Vera",
+            "orcidid": "https://orcid.org/0000-0002-3076-5122",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Bettina Hohberger",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A web-based interactive database to build evidence-based hypotheses on the role of trace elements in glaucoma.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Enrichment analysis",
+                    "uri": "http://edamontology.org/operation_3501"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.jveralab.net/glaucoma-trel/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-28T14:10:55.052952Z",
+    "license": "CC-BY-4.0",
+    "name": "Glaucoma-TrEl",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S13104-022-06210-0",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Objective: Glaucoma is a chronic neurological disease that is associated with high intraocular pressure (IOP), causes gradual damage to retinal ganglion cells, and often culminates in vision loss. Recent research suggests that glaucoma is a complex multifactorial disease in which multiple interlinked genes and pathways play a role during onset and development. Also, differential availability of trace elements seems to play a role in glaucoma pathophysiology, although their mechanism of action is unknown. The aim of this work is to disseminate a web-based repository on interactions between trace elements and protein-coding genes linked to glaucoma pathophysiology. Results: In this study, we present Glaucoma-TrEl, a web database containing information about interactions between trace elements and protein-coding genes that are linked to glaucoma. In the database, we include interactions between 437 unique genes and eight trace elements. Our analysis found a large number of interactions between trace elements and protein-coding genes mutated or linked to the pathophysiology of glaucoma. We associated genes interacting with multiple trace elements to pathways known to play a role in glaucoma. The web-based platform provides an easy-to-use and interactive tool, which serves as an information hub facilitating future research work on trace elements in glaucoma.",
+                "authors": [
+                    {
+                        "name": "Chatterjee T."
+                    },
+                    {
+                        "name": "Choudhari J.K."
+                    },
+                    {
+                        "name": "Eberhardt M."
+                    },
+                    {
+                        "name": "Hohberger B."
+                    },
+                    {
+                        "name": "Vera J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Research Notes",
+                "title": "Glaucoma-TrEl: A web-based interactive database to build evidence-based hypotheses on the role of trace elements in glaucoma"
+            },
+            "pmcid": "PMC9673420",
+            "pmid": "36401306"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Genetics",
+            "uri": "http://edamontology.org/topic_3053"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Neurology",
+            "uri": "http://edamontology.org/topic_3334"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/glmsingle/glmsingle.biotools.json b/data/glmsingle/glmsingle.biotools.json
new file mode 100644
index 0000000000000..9bac40ee95e15
--- /dev/null
+++ b/data/glmsingle/glmsingle.biotools.json
@@ -0,0 +1,109 @@
+{
+    "additionDate": "2023-01-28T14:14:42.045529Z",
+    "biotoolsCURIE": "biotools:glmsingle",
+    "biotoolsID": "glmsingle",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "jacob.samuel.prince@gmail.com",
+            "name": "Jacob S Prince",
+            "orcidid": "https://orcid.org/0000-0001-6169-9503",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "GLMsingle is a toolbox for obtaining accurate single-trial estimates in fMRI time-series data. We provide both MATLAB and Python implementations.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/cvnlab/GLMsingle",
+    "language": [
+        "MATLAB",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T14:14:42.048090Z",
+    "license": "BSD-3-Clause",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/jacob-prince/GLMsingle_paper"
+        }
+    ],
+    "name": "GLMsingle",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.7554/ELIFE.77599",
+            "metadata": {
+                "abstract": "© Prince et al.Advances in artificial intelligence have inspired a paradigm shift in human neurosci-ence, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to thousands of naturalistic visual stimuli. Because such experiments necessarily involve brief stimulus durations and few repetitions of each stimulus, achieving sufficient signal-to-noise ratio can be a major challenge. We address this challenge by introducing GLMsingle, a scalable, user-friendly toolbox available in MATLAB and Python that enables accurate estimation of single-trial fMRI responses (glmsingle.org). Requiring only fMRI time-series data and a design matrix as inputs, GLMsingle integrates three techniques for improving the accuracy of trial-wise general linear model (GLM) beta estimates. First, for each voxel, a custom hemodynamic response function (HRF) is identified from a library of candidate functions. Second, cross-validation is used to derive a set of noise regressors from voxels unrelated to the experiment. Third, to improve the stability of beta estimates for closely spaced trials, betas are regularized on a voxel-wise basis using ridge regression. Applying GLMsingle to the Natural Scenes Dataset and BOLD5000, we find that GLMsingle substantially improves the reliability of beta estimates across visually-responsive cortex in all subjects. Comparable improvements in reliability are also observed in a smaller-scale auditory dataset from the StudyForrest experiment. These improvements translate into tangible benefits for higher-level analyses relevant to systems and cognitive neuroscience. We demonstrate that GLMsingle: (i) helps decorrelate response estimates between trials nearby in time; (ii) enhances representational similarity between subjects within and across datasets; and (iii) boosts one-versus-many decoding of visual stimuli. GLMsingle is a publicly available tool that can significantly improve the quality of past, present, and future neuroimaging datasets sampling brain activity across many experimental conditions.",
+                "authors": [
+                    {
+                        "name": "Charest I."
+                    },
+                    {
+                        "name": "Kay K.N."
+                    },
+                    {
+                        "name": "Kurzawski J.W."
+                    },
+                    {
+                        "name": "Prince J.S."
+                    },
+                    {
+                        "name": "Pyles J.A."
+                    },
+                    {
+                        "name": "Tarr M.J."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "eLife",
+                "title": "Improving the accuracy of single-trial fMRI response estimates using GLMsingle"
+            },
+            "pmcid": "PMC9708069",
+            "pmid": "36444984"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Script",
+        "Suite"
+    ],
+    "topic": [
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        }
+    ]
+}
diff --git a/data/glycoenzonto/glycoenzonto.biotools.json b/data/glycoenzonto/glycoenzonto.biotools.json
new file mode 100644
index 0000000000000..19c1706fafc54
--- /dev/null
+++ b/data/glycoenzonto/glycoenzonto.biotools.json
@@ -0,0 +1,126 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T14:58:37.739762Z",
+    "biotoolsCURIE": "biotools:glycoenzonto",
+    "biotoolsID": "glycoenzonto",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "neel@buffalo.edu",
+            "name": "Sriram Neelamegham",
+            "orcidid": "https://orcid.org/0000-0002-1371-8500",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Theodore Groth"
+        },
+        {
+            "name": "Alexander D. Diehl",
+            "orcidid": "http://orcid.org/0000-0001-9990-8331"
+        },
+        {
+            "name": "Rudiyanto Gunawan",
+            "orcidid": "http://orcid.org/0000-0002-6480-7976"
+        }
+    ],
+    "description": "A GlycoEnzyme Pathway and Molecular Function Ontology..",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/neel-lab/GlycoEnzOnto/tree/main/enrichment_analysis"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Enrichment analysis",
+                    "uri": "http://edamontology.org/operation_3501"
+                },
+                {
+                    "term": "Gene functional annotation",
+                    "uri": "http://edamontology.org/operation_3672"
+                },
+                {
+                    "term": "Pathway analysis",
+                    "uri": "http://edamontology.org/operation_3928"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/neel-lab/GlycoEnzOnto",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T14:58:37.742228Z",
+    "license": "CC-BY-4.0",
+    "name": "GlycoEnzOnto",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac704",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The 'glycoEnzymes' include a set of proteins having related enzymatic, metabolic, transport, structural and cofactor functions. Currently, there is no established ontology to describe glycoEnzyme properties and to relate them to glycan biosynthesis pathways. RESULTS: We present GlycoEnzOnto, an ontology describing 403 human glycoEnzymes curated along 139 glycosylation pathways, 134 molecular functions and 22 cellular compartments. The pathways described regulate nucleotide-sugar metabolism, glycosyl-substrate/donor transport, glycan biosynthesis and degradation. The role of each enzyme in the glycosylation initiation, elongation/branching and capping/termination phases is described. IUPAC linear strings present systematic human/machine-readable descriptions of individual reaction steps and enable automated knowledge-based curation of biochemical networks. All GlycoEnzOnto knowledge is integrated with the Gene Ontology biological processes. GlycoEnzOnto enables improved transcript overrepresentation analyses and glycosylation pathway identification compared to other available schema, e.g. KEGG and Reactome. Overall, GlycoEnzOnto represents a holistic glycoinformatics resource for systems-level analyses. AVAILABILITY AND IMPLEMENTATION: https://github.com/neel-lab/GlycoEnzOnto. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Diehl A.D."
+                    },
+                    {
+                        "name": "Groth T."
+                    },
+                    {
+                        "name": "Gunawan R."
+                    },
+                    {
+                        "name": "Neelamegham S."
+                    }
+                ],
+                "date": "2022-12-13T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "GlycoEnzOnto: a GlycoEnzyme pathway and molecular function ontology"
+            },
+            "pmcid": "PMC9750110",
+            "pmid": "36282863"
+        }
+    ],
+    "toolType": [
+        "Plug-in"
+    ],
+    "topic": [
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Enzymes",
+            "uri": "http://edamontology.org/topic_0821"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/gmembeddings/gmembeddings.biotools.json b/data/gmembeddings/gmembeddings.biotools.json
new file mode 100644
index 0000000000000..b02a2dfc246bb
--- /dev/null
+++ b/data/gmembeddings/gmembeddings.biotools.json
@@ -0,0 +1,90 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T15:20:00.203144Z",
+    "biotoolsCURIE": "biotools:gmembeddings",
+    "biotoolsID": "gmembeddings",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tataruc@oregonstate.edu",
+            "name": "Christine Tataru",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Austin Eaton"
+        },
+        {
+            "name": "Maude M David"
+        }
+    ],
+    "description": "An R Package to Apply Embedding Techniques to Microbiome Data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Filtering",
+                    "uri": "http://edamontology.org/operation_3695"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MaudeDavidLab/GMEmbeddings",
+    "language": [
+        "Python",
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2022-12-31T15:20:00.205757Z",
+    "license": "GPL-3.0",
+    "name": "GMEmbeddings",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FBINF.2022.828703",
+            "pmcid": "PMC9580954",
+            "pmid": "36304322"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        }
+    ]
+}
diff --git a/data/gmwi-webtool/gmwi-webtool.biotools.json b/data/gmwi-webtool/gmwi-webtool.biotools.json
new file mode 100644
index 0000000000000..de6d54a44fd5a
--- /dev/null
+++ b/data/gmwi-webtool/gmwi-webtool.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T23:20:09.688775Z",
+    "biotoolsCURIE": "biotools:gmwi-webtool",
+    "biotoolsID": "gmwi-webtool",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Benjamin Hur"
+        },
+        {
+            "name": "Daniel Chang"
+        },
+        {
+            "name": "Jaeyun Sung"
+        },
+        {
+            "name": "Kevin Y Cunningham"
+        },
+        {
+            "name": "Vinod K Gupta"
+        }
+    ],
+    "description": "A user-friendly browser application for assessing health through metagenomic gut microbiome profiling.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://gmwi-webtool.github.io",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-02-27T23:20:09.691579Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/danielchang2002/GMWI-webtool"
+        }
+    ],
+    "name": "GMWI-webtool",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btad061",
+            "metadata": {
+                "abstract": "© The Author(s) 2023. Published by Oxford University Press.SUMMARY: We recently introduced the Gut Microbiome Wellness Index (GMWI), a stool metagenome-based indicator for assessing health by determining the likelihood of disease given the state of one's gut microbiome. The calculation of our wellness index depends on the relative abundances of health-prevalent and health-scarce species. Encouragingly, GMWI has already been utilized in various studies focusing on differences in the gut microbiome between cases and controls. Herein, we introduce the GMWI-webtool, a user-friendly browser application that computes GMWI, health-prevalent/-scarce species' relative abundances, and α-diversities from stool shotgun metagenome taxonomic profiles. Users of our interactive online tool can visualize their results and compare them side-by-side with those from our pooled reference dataset of metagenomes, as well as export data in.csv format and high-resolution figures. AVAILABILITY AND IMPLEMENTATION: GMWI-webtool is freely available here: https://gmwi-webtool.github.io/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Chang D."
+                    },
+                    {
+                        "name": "Cunningham K.Y."
+                    },
+                    {
+                        "name": "Gupta V.K."
+                    },
+                    {
+                        "name": "Hur B."
+                    },
+                    {
+                        "name": "Sung J."
+                    }
+                ],
+                "date": "2023-02-03T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "GMWI-webtool: a user-friendly browser application for assessing health through metagenomic gut microbiome profiling"
+            },
+            "pmcid": "PMC9897175",
+            "pmid": "36707995"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        }
+    ]
+}
diff --git a/data/gocompare/gocompare.biotools.json b/data/gocompare/gocompare.biotools.json
new file mode 100644
index 0000000000000..35a9f7cd784a8
--- /dev/null
+++ b/data/gocompare/gocompare.biotools.json
@@ -0,0 +1,98 @@
+{
+    "additionDate": "2023-02-25T13:30:51.907200Z",
+    "biotoolsCURIE": "biotools:gocompare",
+    "biotoolsID": "gocompare",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Chrystian C. Sosa"
+        }
+    ],
+    "description": "An R package to compare functional enrichment analysis between two species.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ccsosa/GOCompare",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-25T13:30:51.911394Z",
+    "license": "GPL-3.0",
+    "name": "GOCompare",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.YGENO.2022.110528",
+            "metadata": {
+                "abstract": "© 2022Functional enrichment analysis is a cornerstone in bioinformatics as it makes possible to identify functional information by using a gene list as source. Different tools are available to compare gene ontology (GO) terms, based on a directed acyclic graph structure or content-based algorithms which are time-consuming and require a priori information of GO terms. Nevertheless, quantitative procedures to compare GO terms among gene lists and species are not available. Here we present a computational procedure, implemented in R, to infer functional information derived from comparative strategies. GOCompare provides a framework for functional comparative genomics starting from comparable lists from GO terms. The program uses functional enrichment analysis (FEA) results and implement graph theory to identify statistically relevant GO terms for both, GO categories and analyzed species. Thus, GOCompare allows finding new functional information complementing current FEA approaches and extending their use to a comparative perspective. To test our approach GO terms were obtained for a list of aluminum tolerance-associated genes in Oryza sativa subsp. japonica and their orthologues in Arabidopsis thaliana. GOCompare was able to detect functional similarities for reactive oxygen species and ion binding capabilities which are common in plants as molecular mechanisms to tolerate aluminum toxicity. Consequently, the R package exhibited a good performance when implemented in complex datasets, allowing to establish hypothesis that might explain a biological process from a functional perspective, and narrowing down the possible landscapes to design wet lab experiments.",
+                "authors": [
+                    {
+                        "name": "Clavijo-Buritica D.C."
+                    },
+                    {
+                        "name": "Diaz M.V."
+                    },
+                    {
+                        "name": "Garcia-Merchan V.H."
+                    },
+                    {
+                        "name": "Londono D.A."
+                    },
+                    {
+                        "name": "Lopez-Rozo N."
+                    },
+                    {
+                        "name": "Quimbaya M.A."
+                    },
+                    {
+                        "name": "Riccio-Rengifo C."
+                    },
+                    {
+                        "name": "Sosa C.C."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Genomics",
+                "title": "GOCompare: An R package to compare functional enrichment analysis between two species"
+            },
+            "pmid": "36462728"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Comparative genomics",
+            "uri": "http://edamontology.org/topic_0797"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        }
+    ]
+}
diff --git a/data/gold_db/gold_db.biotools.json b/data/gold_db/gold_db.biotools.json
new file mode 100644
index 0000000000000..a329358fedfa2
--- /dev/null
+++ b/data/gold_db/gold_db.biotools.json
@@ -0,0 +1,145 @@
+{
+    "additionDate": "2023-01-28T14:21:00.864475Z",
+    "biotoolsCURIE": "biotools:gold_db",
+    "biotoolsID": "gold_db",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tbreddy@lbl.gov",
+            "name": "T B K Reddy",
+            "orcidid": "https://orcid.org/0000-0002-0871-5567",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "GOLD: Genomes Online Database, is a World Wide Web resource for comprehensive access to information regarding genome and metagenome sequencing projects, and their associated metadata, around the world.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://gold.jgi.doe.gov/",
+    "language": [
+        "Bash",
+        "Java",
+        "Perl",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T14:21:00.867115Z",
+    "license": "Other",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://gold.jgi.doe.gov/statistics"
+        }
+    ],
+    "name": "GOLD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC974",
+            "metadata": {
+                "abstract": "Published by Oxford University Press on behalf of Nucleic Acids Research 2022.The Genomes OnLine Database (GOLD) (https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute (DOE-JGI) continues to maintain its role as one of the flagship genomic metadata repositories of the world. The ever-increasing number of projects and metadata are freely available to the user community world-wide. GOLD's metadata is consumed by scientists and remains an important source for large-scale comparative genomics analysis initiatives. Encouraged by this active user engagement and growth, GOLD has continued to add new components and capabilities. The new features such as a public Application Programming Interface (API) and Ecosystem landing page as well as the growth of different entities in this current GOLD v.9 edition are described in detail in this manuscript.",
+                "authors": [
+                    {
+                        "name": "Bertsch J."
+                    },
+                    {
+                        "name": "Chen I.-M.A."
+                    },
+                    {
+                        "name": "Favognano A."
+                    },
+                    {
+                        "name": "Kandimalla M."
+                    },
+                    {
+                        "name": "Kyrpides N.C."
+                    },
+                    {
+                        "name": "Li C.T."
+                    },
+                    {
+                        "name": "Mukherjee S."
+                    },
+                    {
+                        "name": "Nicolopoulos P.A."
+                    },
+                    {
+                        "name": "Ovchinnikova G."
+                    },
+                    {
+                        "name": "Reddy T.B.K."
+                    },
+                    {
+                        "name": "Stamatis D."
+                    },
+                    {
+                        "name": "Sundaramurthi J.C."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "Twenty-five years of Genomes OnLine Database (GOLD): data updates and new features in v.9"
+            },
+            "pmcid": "PMC9825498",
+            "pmid": "36318257"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Metagenomic sequencing",
+            "uri": "http://edamontology.org/topic_3837"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Metatranscriptomics",
+            "uri": "http://edamontology.org/topic_3941"
+        },
+        {
+            "term": "Sample collections",
+            "uri": "http://edamontology.org/topic_3277"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ],
+    "version": [
+        "9.0"
+    ]
+}
diff --git a/data/goldvariants/goldvariants.biotools.json b/data/goldvariants/goldvariants.biotools.json
new file mode 100644
index 0000000000000..0cf61d5e54a7b
--- /dev/null
+++ b/data/goldvariants/goldvariants.biotools.json
@@ -0,0 +1,164 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T23:55:19.382691Z",
+    "biotoolsCURIE": "biotools:goldvariants",
+    "biotoolsID": "goldvariants",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Kathleen.freson@kuleuven.be",
+            "name": "Kathleen Freson",
+            "orcidid": "https://orcid.org/0000-0002-4381-2442",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "David-Alexandre Trégouët"
+        },
+        {
+            "name": "Karyn Megy"
+        },
+        {
+            "name": "Kate Downes"
+        }
+    ],
+    "description": "Resource for sharing rare genetic variants detected in bleeding, thrombotic, and platelet disorders.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "Variant classification",
+                    "uri": "http://edamontology.org/operation_3225"
+                },
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://redcap.isth.org/surveys/index.php?s=MK94LDCXTW",
+    "lastUpdate": "2023-01-26T23:55:19.385465Z",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://www.isth.org/page/GinTh_GeneLists"
+        }
+    ],
+    "name": "GoldVariants",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1111/JTH.15459",
+            "metadata": {
+                "abstract": "© 2021 The Authors. Journal of Thrombosis and Haemostasis published by Wiley Periodicals LLC on behalf of International Society on Thrombosis and Haemostasis.The implementation of high-throughput sequencing (HTS) technologies in research and diagnostic laboratories has linked many new genes to rare bleeding, thrombotic, and platelet disorders (BTPD), and revealed multiple genetic variants linked to those disorders, many of them being of uncertain pathogenicity when considering the accepted evidence (variant consequence, frequency in control datasets, number of reported patients, prediction models, and functional assays). The sequencing effort has also resulted in resources for gathering disease-causing variants associated with specific genes, but for BTPD, such well-curated databases exist only for a few genes. On the other hand, submissions by individuals or diagnostic laboratories to the variant database ClinVar are hampered by the lack of a submission process tailored to capture the specific features of hemostatic diseases. As we move toward the implementation of HTS in the diagnosis of BTPD, the Scientific and Standardization Committee for Genetics in Thrombosis and Haemostasis has developed and tested a REDCap-based interface, aimed at the community, to submit curated genetic variants for diagnostic-grade BTPD genes. Here, we describe the use of the interface and the initial submission of 821 variants from 30 different centers covering 14 countries. This open-access variant resource will be shared with the community to improve variant classification and regular bulk data transfer to ClinVar.",
+                "authors": [
+                    {
+                        "name": "Bastida J.M."
+                    },
+                    {
+                        "name": "Brooks S."
+                    },
+                    {
+                        "name": "Bury L."
+                    },
+                    {
+                        "name": "Downes K."
+                    },
+                    {
+                        "name": "Freson K."
+                    },
+                    {
+                        "name": "Gomez K."
+                    },
+                    {
+                        "name": "Leinoe E."
+                    },
+                    {
+                        "name": "Megy K."
+                    },
+                    {
+                        "name": "Morel-Kopp M.-C."
+                    },
+                    {
+                        "name": "Morgan N.V."
+                    },
+                    {
+                        "name": "Othman M."
+                    },
+                    {
+                        "name": "Ouwehand W.H."
+                    },
+                    {
+                        "name": "Perez Botero J."
+                    },
+                    {
+                        "name": "Rivera J."
+                    },
+                    {
+                        "name": "Schulze H."
+                    },
+                    {
+                        "name": "Tregouet D.-A."
+                    }
+                ],
+                "citationCount": 7,
+                "date": "2021-10-01T00:00:00Z",
+                "journal": "Journal of Thrombosis and Haemostasis",
+                "title": "GoldVariants, a resource for sharing rare genetic variants detected in bleeding, thrombotic, and platelet disorders: Communication from the ISTH SSC Subcommittee on Genomics in Thrombosis and Hemostasis"
+            },
+            "pmcid": "PMC9291976",
+            "pmid": "34355501"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Haematology",
+            "uri": "http://edamontology.org/topic_3408"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/google_colab/google_colab.biotools.json b/data/google_colab/google_colab.biotools.json
new file mode 100644
index 0000000000000..59d30bb4304b6
--- /dev/null
+++ b/data/google_colab/google_colab.biotools.json
@@ -0,0 +1,26 @@
+{
+    "additionDate": "2023-01-31T06:52:49.892367Z",
+    "biotoolsCURIE": "biotools:google_colab",
+    "biotoolsID": "google_colab",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "description": "Colaboratory, or “Colab” for short, is a product from Google Research. Colab allows anybody to write and execute arbitrary python code through the browser, and is especially well suited to machine learning, data analysis and education.",
+    "editPermission": {
+        "type": "public"
+    },
+    "homepage": "https://colab.research.google.com/",
+    "lastUpdate": "2023-02-01T12:46:02.977840Z",
+    "license": "Proprietary",
+    "link": [
+        {
+            "note": "write and execute arbitrary python code through the browser,",
+            "type": [
+                "Other"
+            ],
+            "url": "https://colab.research.google.com/"
+        }
+    ],
+    "name": "Google Colab",
+    "owner": "iacs-biocomputacion"
+}
diff --git a/data/gpsadb/gpsadb.biotools.json b/data/gpsadb/gpsadb.biotools.json
new file mode 100644
index 0000000000000..c2b54bba70184
--- /dev/null
+++ b/data/gpsadb/gpsadb.biotools.json
@@ -0,0 +1,138 @@
+{
+    "additionDate": "2023-01-28T14:25:56.036571Z",
+    "biotoolsCURIE": "biotools:gpsadb",
+    "biotoolsID": "gpsadb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "guoshipeng2008@126.com",
+            "name": "Shipeng Guo",
+            "orcidid": "https://orcid.org/0000-0002-9286-7132",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "liushengchun1968@163.com",
+            "name": "Shengchun Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "weihong@wmu.edu.cn",
+            "name": "Weihong Song",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A comprehensive web resource for interactive exploration of genetic perturbation RNA-seq datasets.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.gpsadb.com/",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-01-28T14:25:56.039197Z",
+    "license": "CC-BY-4.0",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "http://guotosky.vip:13838/GPSA/"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/xuzhougeng/auto_sra_rnaseq_pipeline"
+        }
+    ],
+    "name": "GPSAdb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1066",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Gene knock-out/down methods are commonly used to explore the functions of genes of interest, but a database that systematically collects perturbed data is not available currently. Manual curation of all the available human cell line perturbed RNA-seq datasets enabled us to develop a comprehensive human perturbation database (GPSAdb, https://www.gpsadb.com/). The current version of GPSAdb collected 3048 RNA-seq datasets associated with 1458 genes, which were knocked out/down by siRNA, shRNA, CRISPR/Cas9, or CRISPRi. The database provides full exploration of these datasets and generated 6096 new perturbed gene sets (up and down separately). GPSAdb integrated the gene sets and developed an online tool, genetic perturbation similarity analysis (GPSA), to identify candidate causal perturbations from differential gene expression data. In summary, GPSAdb is a powerful platform that aims to assist life science researchers to easily access and analyze public perturbed data and explore differential gene expression data in depth.",
+                "authors": [
+                    {
+                        "name": "Dong X."
+                    },
+                    {
+                        "name": "Guo S."
+                    },
+                    {
+                        "name": "Hu D."
+                    },
+                    {
+                        "name": "Jiang Y."
+                    },
+                    {
+                        "name": "Liu S."
+                    },
+                    {
+                        "name": "Song W."
+                    },
+                    {
+                        "name": "Wang Q."
+                    },
+                    {
+                        "name": "Xu Z."
+                    },
+                    {
+                        "name": "Zhang J."
+                    },
+                    {
+                        "name": "Zhou Q."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "GPSAdb: a comprehensive web resource for interactive exploration of genetic perturbation RNA-seq datasets"
+            },
+            "pmcid": "PMC9825484",
+            "pmid": "36416261"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/gr2d2/gr2d2.biotools.json b/data/gr2d2/gr2d2.biotools.json
new file mode 100644
index 0000000000000..e888e6b4d3295
--- /dev/null
+++ b/data/gr2d2/gr2d2.biotools.json
@@ -0,0 +1,104 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T15:13:38.604006Z",
+    "biotoolsCURIE": "biotools:gr2d2",
+    "biotoolsID": "gr2d2",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "doraz@hku.hk",
+            "name": "Yan Dora Zhang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Dailin Gan"
+        },
+        {
+            "name": "Guosheng Yin"
+        }
+    ],
+    "description": "The GR2D2 estimator for the precision matrices.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/RavenGan/GR2D2",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2022-12-31T15:13:38.606604Z",
+    "license": "Not licensed",
+    "name": "GR2D2",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC426",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Biological networks are important for the analysis of human diseases, which summarize the regulatory interactions and other relationships between different molecules. Understanding and constructing networks for molecules, such as DNA, RNA and proteins, can help elucidate the mechanisms of complex biological systems. The Gaussian Graphical Models (GGMs) are popular tools for the estimation of biological networks. Nonetheless, reconstructing GGMs from high-dimensional datasets is still challenging. The current methods cannot handle the sparsity and high-dimensionality issues arising from datasets very well. Here, we developed a new GGM, called the GR2D2 (Graphical $R^2$-induced Dirichlet Decomposition) model, based on the R2D2 priors for linear models. Besides, we provided a data-augmented block Gibbs sampler algorithm. The R code is available at https://github.com/RavenGan/GR2D2. The GR2D2 estimator shows superior performance in estimating the precision matrices compared with the existing techniques in various simulation settings. When the true precision matrix is sparse and of high dimension, the GR2D2 provides the estimates with smallest information divergence from the underlying truth. We also compare the GR2D2 estimator with the graphical horseshoe estimator in five cancer RNA-seq gene expression datasets grouped by three cancer types. Our results show that GR2D2 successfully identifies common cancer pathways and cancer-specific pathways for each dataset.",
+                "authors": [
+                    {
+                        "name": "Gan D."
+                    },
+                    {
+                        "name": "Yin G."
+                    },
+                    {
+                        "name": "Zhang Y.D."
+                    }
+                ],
+                "date": "2022-11-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "The GR2D2 estimator for the precision matrices"
+            },
+            "pmid": "36184191"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/gr_predictor/gr_predictor.biotools.json b/data/gr_predictor/gr_predictor.biotools.json
new file mode 100644
index 0000000000000..13065e0e248c0
--- /dev/null
+++ b/data/gr_predictor/gr_predictor.biotools.json
@@ -0,0 +1,105 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-19T10:42:07.264361Z",
+    "biotoolsCURIE": "biotools:gr_predictor",
+    "biotoolsID": "gr_predictor",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Kosuke Kawama"
+        },
+        {
+            "name": "Yusaku Fukushima"
+        },
+        {
+            "name": "Masateru Ohta",
+            "orcidid": "http://orcid.org/0000-0002-6580-7185"
+        },
+        {
+            "name": "Mitsunori Ikeguchi",
+            "orcidid": "http://orcid.org/0000-0003-3199-6931"
+        },
+        {
+            "name": "Takashi Yoshidome",
+            "orcidid": "http://orcid.org/0000-0001-7407-1942"
+        }
+    ],
+    "description": "A Deep-Learning Model for Predicting the Hydration Structures around Proteins.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/YoshidomeGroup-Hydration/gr-predictor",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-19T10:42:07.266784Z",
+    "license": "GPL-3.0",
+    "name": "gr Predictor",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/acs.jcim.2c00987",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society. All rights reserved.Among the factors affecting biological processes such as protein folding and ligand binding, hydration, which is represented by a three-dimensional water site distribution function around the protein, is crucial. The typical methods for computing the distribution functions, including molecular dynamics simulations and the three-dimensional reference interaction site model (3D-RISM) theory, require a long computation time ranging from hours to tens of hours. Here, we propose a deep learning (DL) model that rapidly estimates the distribution functions around proteins obtained using the 3D-RISM theory from the protein 3D structure. The distribution functions predicted using our DL model are in good agreement with those obtained using the 3D-RISM theory. Particularly, the coefficient of determination between the distribution function obtained by the DL model and that obtained using the 3D-RISM theory is approximately 0.98. Furthermore, using a graphics processing unit, the prediction by the DL model is completed in less than 1 min, more than 2 orders of magnitude faster than the calculation time of the 3D-RISM theory. The position of water molecules around the protein was estimated based on the distribution function obtained by our DL model, and the position of waters estimated by our DL model was in good agreement with that of water molecules estimated using the 3D-RISM theory and of crystallographic waters. Therefore, our DL model provides a practical and efficient way to calculate the three-dimensional water site distribution functions and to estimate the position of water molecules around the protein. The program called \"gr Predictor\" is available under the GNU General Public License from https://github.com/YoshidomeGroup-Hydration/gr-predictor.",
+                "authors": [
+                    {
+                        "name": "Fukushima Y."
+                    },
+                    {
+                        "name": "Ikeguchi M."
+                    },
+                    {
+                        "name": "Kawama K."
+                    },
+                    {
+                        "name": "Ohta M."
+                    },
+                    {
+                        "name": "Yoshidome T."
+                    }
+                ],
+                "date": "2022-09-26T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "Gr Predictor: A Deep Learning Model for Predicting the Hydration Structures around Proteins"
+            },
+            "pmid": "36068974"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Molecular dynamics",
+            "uri": "http://edamontology.org/topic_0176"
+        },
+        {
+            "term": "Protein folding, stability and design",
+            "uri": "http://edamontology.org/topic_0130"
+        },
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        }
+    ]
+}
diff --git a/data/gra-gcn/gra-gcn.biotools.json b/data/gra-gcn/gra-gcn.biotools.json
new file mode 100644
index 0000000000000..09ba4d08d8d0b
--- /dev/null
+++ b/data/gra-gcn/gra-gcn.biotools.json
@@ -0,0 +1,89 @@
+{
+    "additionDate": "2023-01-28T14:29:30.363249Z",
+    "biotoolsCURIE": "biotools:gra-gcn",
+    "biotoolsID": "gra-gcn",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Zhenyu Yue",
+            "orcidid": "https://orcid.org/0000-0002-9370-2540"
+        }
+    ],
+    "description": "Dense granule protein prediction in Apicomplexa protozoa through graph convolutional network.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://dgpd.tlds.cc/GRAGCN/index/",
+    "lastUpdate": "2023-01-28T14:29:30.365986Z",
+    "license": "Other",
+    "name": "GRA-GCN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1109/TCBB.2022.3224836",
+            "metadata": {
+                "abstract": "IEEEDense granule proteins (GRAs) are secreted by Apicomplexa protozoa, which are closely related to an extensive variety of farm animal diseases. Predicting GRAs is an integral part in prevention and treatment of parasitic diseases. Considering that biological experiment approach is time-consuming and labor-intensive, computational method is a superior choice. Hence, developing an effective computational method for GRAs prediction is of urgency. In this paper, we present a novel computational method named GRA-GCN through graph convolutional network. In terms of the graph theory, the GRAs prediction can be regarded as a node classification task. GRA-GCN leverages k-nearest neighbor algorithm to construct the feature graph for aggregating more informative representation. To our knowledge, this is the first attempt to utilize computational approach for GRAs prediction. Evaluated by 5-fold cross-validations, the GRA-GCN method achieves satisfactory performance, and is superior to four classic machine learning-based methods and three state-of-the-art models. The analysis of the comprehensive experiment results and a case study could offer valuable information for understanding complex mechanisms, and would contribute to accurate prediction of GRAs. Moreover, we also implement a web server at <uri>http://dgpd.tlds.cc/GRAGCN/index/</uri>, for facilitating the process of using our model.",
+                "authors": [
+                    {
+                        "name": "Feng H."
+                    },
+                    {
+                        "name": "Lu Z."
+                    },
+                    {
+                        "name": "Shi H."
+                    },
+                    {
+                        "name": "Xue W."
+                    },
+                    {
+                        "name": "Yang C."
+                    },
+                    {
+                        "name": "Yue Z."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
+                "title": "GRA-GCN: dense granule protein prediction in Apicomplexa protozoa through graph convolutional network"
+            },
+            "pmid": "36441896"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Laboratory techniques",
+            "uri": "http://edamontology.org/topic_3361"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/grace-ako/grace-ako.biotools.json b/data/grace-ako/grace-ako.biotools.json
new file mode 100644
index 0000000000000..cc42dfb5d2ac5
--- /dev/null
+++ b/data/grace-ako/grace-ako.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-19T10:49:41.365863Z",
+    "biotoolsCURIE": "biotools:grace-ako",
+    "biotoolsID": "grace-ako",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "doraz@hku.hk",
+            "name": "Yan Dora Zhang",
+            "orcidid": "http://orcid.org/0000-0002-5302-3690",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Peixin Tian"
+        },
+        {
+            "name": "Yiqian Hu"
+        },
+        {
+            "name": "Zhonghua Liu"
+        }
+    ],
+    "description": "A Novel and Stable Knockoff Filter for Variable Selection Incorporating Gene Network Structures.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Feature selection",
+                    "uri": "http://edamontology.org/operation_3936"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/mxxptian/GraceAKO",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-19T10:49:41.368434Z",
+    "license": "Not licensed",
+    "name": "Grace-AKO",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/s12859-022-05016-y",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Motivation: Variable selection is a common statistical approach to identifying genes associated with clinical outcomes of scientific interest. There are thousands of genes in genomic studies, while only a limited number of individual samples are available. Therefore, it is important to develop a method to identify genes associated with outcomes of interest that can control finite-sample false discovery rate (FDR) in high-dimensional data settings. Results: This article proposes a novel method named Grace-AKO for graph-constrained estimation (Grace), which incorporates aggregation of multiple knockoffs (AKO) with the network-constrained penalty. Grace-AKO can control FDR in finite-sample settings and improve model stability simultaneously. Simulation studies show that Grace-AKO has better performance in finite-sample FDR control than the original Grace model. We apply Grace-AKO to the prostate cancer data in The Cancer Genome Atlas program by incorporating prostate-specific antigen (PSA) pathways in the Kyoto Encyclopedia of Genes and Genomes as the prior information. Grace-AKO finally identifies 47 candidate genes associated with PSA level, and more than 75% of the detected genes can be validated.",
+                "authors": [
+                    {
+                        "name": "Hu Y."
+                    },
+                    {
+                        "name": "Liu Z."
+                    },
+                    {
+                        "name": "Tian P."
+                    },
+                    {
+                        "name": "Zhang Y.D."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "Grace-AKO: a novel and stable knockoff filter for variable selection incorporating gene network structures"
+            },
+            "pmcid": "PMC9664829",
+            "pmid": "36376815"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/graltr-lda/graltr-lda.biotools.json b/data/graltr-lda/graltr-lda.biotools.json
new file mode 100644
index 0000000000000..dbce7c2e2abec
--- /dev/null
+++ b/data/graltr-lda/graltr-lda.biotools.json
@@ -0,0 +1,76 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-25T13:43:19.584469Z",
+    "biotoolsCURIE": "biotools:graltr-lda",
+    "biotoolsID": "graltr-lda",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "bliu@bliulab.net",
+            "name": "Bin Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "hwu@bliulab.net",
+            "name": "Hao Wu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A tool that for LncRNA-disease association identification using graph auto-encoder and learning to rank.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bliulab.net/GraLTR-LDA",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-25T13:43:19.587425Z",
+    "license": "Other",
+    "name": "GraLTR-LDA",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC539",
+            "pmid": "36545805"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/grape_pipeline/grape_pipeline.biotools.json b/data/grape_pipeline/grape_pipeline.biotools.json
new file mode 100644
index 0000000000000..30396d70cf766
--- /dev/null
+++ b/data/grape_pipeline/grape_pipeline.biotools.json
@@ -0,0 +1,110 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T21:01:21.188605Z",
+    "biotoolsCURIE": "biotools:grape_pipeline",
+    "biotoolsID": "grape_pipeline",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "gloriouslair@gmail.com",
+            "name": "Mikhail Lebedev",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Aleksandr Medvedev",
+            "orcidid": "http://orcid.org/0000-0002-6871-4240"
+        },
+        {
+            "name": "Dmitry Kolobkov",
+            "orcidid": "http://orcid.org/0000-0003-4225-2057"
+        },
+        {
+            "name": "Pavel Nikonorov",
+            "orcidid": "http://orcid.org/0000-0002-8471-2069"
+        }
+    ],
+    "description": "Genomic Relatedness Detection Pipeline.",
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://hub.docker.com/r/genxnetwork/grape"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Relation extraction",
+                    "uri": "http://edamontology.org/operation_3625"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/genxnetwork/grape",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-17T21:01:21.191427Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://dockstore.org/organizations/GenX/collections/GRAPE"
+        }
+    ],
+    "name": "GRAPE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1101/2022.03.11.483988"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/graph4med/graph4med.biotools.json b/data/graph4med/graph4med.biotools.json
new file mode 100644
index 0000000000000..b2e435a8c9a34
--- /dev/null
+++ b/data/graph4med/graph4med.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-25T13:49:26.297348Z",
+    "biotoolsCURIE": "biotools:graph4med",
+    "biotoolsID": "graph4med",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jeschaef@cs.uni-frankfurt.de",
+            "name": "Jero Schäfer",
+            "orcidid": "https://orcid.org/0000-0001-7727-1181",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A web application and a graph database for visualizing and analyzing medical databases.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://graph4med.cs.uni-frankfurt.de/",
+    "language": [
+        "JavaScript",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-25T13:50:04.646500Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/jeschaef/Graph4Med"
+        }
+    ],
+    "name": "Graph4Med",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05092-0",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Medical databases normally contain large amounts of data in a variety of forms. Although they grant significant insights into diagnosis and treatment, implementing data exploration into current medical databases is challenging since these are often based on a relational schema and cannot be used to easily extract information for cohort analysis and visualization. As a consequence, valuable information regarding cohort distribution or patient similarity may be missed. With the rapid advancement of biomedical technologies, new forms of data from methods such as Next Generation Sequencing (NGS) or chromosome microarray (array CGH) are constantly being generated; hence it can be expected that the amount and complexity of medical data will rise and bring relational database systems to a limit. Description: We present Graph4Med, a web application that relies on a graph database obtained by transforming a relational database. Graph4Med provides a straightforward visualization and analysis of a selected patient cohort. Our use case is a database of pediatric Acute Lymphoblastic Leukemia (ALL). Along routine patients’ health records it also contains results of latest technologies such as NGS data. We developed a suitable graph data schema to convert the relational data into a graph data structure and store it in Neo4j. We used NeoDash to build a dashboard for querying and displaying patients’ cohort analysis. This way our tool (1) quickly displays the overview of patients’ cohort information such as distributions of gender, age, mutations (fusions), diagnosis; (2) provides mutation (fusion) based similarity search and display in a maneuverable graph; (3) generates an interactive graph of any selected patient and facilitates the identification of interesting patterns among patients. Conclusion: We demonstrate the feasibility and advantages of a graph database for storing and querying medical databases. Our dashboard allows a fast and interactive analysis and visualization of complex medical data. It is especially useful for patients similarity search based on mutations (fusions), of which vast amounts of data have been generated by NGS in recent years. It can discover relationships and patterns in patients cohorts that are normally hard to grasp. Expanding Graph4Med to more medical databases will bring novel insights into diagnostic and research.",
+                "authors": [
+                    {
+                        "name": "Bergmann A.K."
+                    },
+                    {
+                        "name": "Luu D."
+                    },
+                    {
+                        "name": "Schafer J."
+                    },
+                    {
+                        "name": "Tang M."
+                    },
+                    {
+                        "name": "Wiese L."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "Graph4Med: a web application and a graph database for visualizing and analyzing medical databases"
+            },
+            "pmcid": "PMC9743588",
+            "pmid": "36503436"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biological databases",
+            "uri": "http://edamontology.org/topic_3071"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/graphbio/graphbio.biotools.json b/data/graphbio/graphbio.biotools.json
new file mode 100644
index 0000000000000..00140d0102dc3
--- /dev/null
+++ b/data/graphbio/graphbio.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T21:07:35.933839Z",
+    "biotoolsCURIE": "biotools:graphbio",
+    "biotoolsID": "graphbio",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Tian-Xin Zhao"
+        },
+        {
+            "name": "Ze-Lin Wang"
+        }
+    ],
+    "description": "A shiny web app to easily perform popular visualization analysis for omics data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene expression profile",
+                        "uri": "http://edamontology.org/data_0928"
+                    },
+                    "format": [
+                        {
+                            "term": "CSV",
+                            "uri": "http://edamontology.org/format_3752"
+                        },
+                        {
+                            "term": "xls",
+                            "uri": "http://edamontology.org/format_3468"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Dot plot plotting",
+                    "uri": "http://edamontology.org/operation_0490"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                },
+                {
+                    "term": "Scatter plot plotting",
+                    "uri": "http://edamontology.org/operation_2940"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.graphbio1.com/en/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-17T21:07:35.936381Z",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "http://www.graphbio1.com/"
+        }
+    ],
+    "name": "GraphBio",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/fgene.2022.957317",
+            "metadata": {
+                "abstract": "Copyright © 2022 Zhao and Wang.Background: Massive amounts of omics data are produced and usually require sophisticated visualization analysis. These analyses often require programming skills, which are difficult for experimental biologists. Thus, more user-friendly tools are urgently needed. Methods and Results: Herein, we present GraphBio, a shiny web app to easily perform visualization analysis for omics data. GraphBio provides 15 popular visualization analysis methods, including heatmap, volcano plots, MA plots, network plots, dot plots, chord plots, pie plots, four quadrant diagrams, Venn diagrams, cumulative distribution curves, principal component analysis (PCA), survival analysis, receiver operating characteristic (ROC) analysis, correlation analysis, and text cluster analysis. It enables experimental biologists without programming skills to easily perform popular visualization analysis and get publication-ready figures. Conclusion: GraphBio, as an online web application, is freely available at http://www.graphbio1.com/en/ (English version) and http://www.graphbio1.com/ (Chinese version). The source code of GraphBio is available at https://github.com/databio2022/GraphBio.",
+                "authors": [
+                    {
+                        "name": "Wang Z."
+                    },
+                    {
+                        "name": "Zhao T."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-09-07T00:00:00Z",
+                "journal": "Frontiers in Genetics",
+                "title": "GraphBio: A shiny web app to easily perform popular visualization analysis for omics data"
+            },
+            "pmcid": "PMC9490469",
+            "pmid": "36159985"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        }
+    ]
+}
diff --git a/data/graphlncloc/graphlncloc.biotools.json b/data/graphlncloc/graphlncloc.biotools.json
new file mode 100644
index 0000000000000..083d49f23f55b
--- /dev/null
+++ b/data/graphlncloc/graphlncloc.biotools.json
@@ -0,0 +1,116 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-25T13:54:06.762887Z",
+    "biotoolsCURIE": "biotools:graphlncloc",
+    "biotoolsID": "graphlncloc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zengmin@csu.edu.cn",
+            "name": "Min Zeng",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Long non-coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "De-novo assembly",
+                    "uri": "http://edamontology.org/operation_0524"
+                },
+                {
+                    "term": "Sequence feature detection",
+                    "uri": "http://edamontology.org/operation_0253"
+                },
+                {
+                    "term": "Subcellular localisation prediction",
+                    "uri": "http://edamontology.org/operation_2489"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://csuligroup.com:8000/GraphLncLoc/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-25T13:54:06.765612Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/CSUBioGroup/GraphLncLoc"
+        }
+    ],
+    "name": "GraphLncLoc",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC565",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.The subcellular localization of long non-coding RNAs (lncRNAs) is crucial for understanding lncRNA functions. Most of existing lncRNA subcellular localization prediction methods use k-mer frequency features to encode lncRNA sequences. However, k-mer frequency features lose sequence order information and fail to capture sequence patterns and motifs of different lengths. In this paper, we proposed GraphLncLoc, a graph convolutional network-based deep learning model, for predicting lncRNA subcellular localization. Unlike previous studies encoding lncRNA sequences by using k-mer frequency features, GraphLncLoc transforms lncRNA sequences into de Bruijn graphs, which transforms the sequence classification problem into a graph classification problem. To extract the high-level features from the de Bruijn graph, GraphLncLoc employs graph convolutional networks to learn latent representations. Then, the high-level feature vectors derived from de Bruijn graph are fed into a fully connected layer to perform the prediction task. Extensive experiments show that GraphLncLoc achieves better performance than traditional machine learning models and existing predictors. In addition, our analyses show that transforming sequences into graphs has more distinguishable features and is more robust than k-mer frequency features. The case study shows that GraphLncLoc can uncover important motifs for nucleus subcellular localization. GraphLncLoc web server is available at http://csuligroup.com:8000/GraphLncLoc/.",
+                "authors": [
+                    {
+                        "name": "Guo F."
+                    },
+                    {
+                        "name": "Li M."
+                    },
+                    {
+                        "name": "Lu C."
+                    },
+                    {
+                        "name": "Yin R."
+                    },
+                    {
+                        "name": "Zeng M."
+                    },
+                    {
+                        "name": "Zhao B."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "GraphLncLoc: long non-coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation"
+            },
+            "pmid": "36545797"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ]
+}
diff --git a/data/grasp_web/grasp_web.biotools.json b/data/grasp_web/grasp_web.biotools.json
new file mode 100644
index 0000000000000..68f8c93642aa6
--- /dev/null
+++ b/data/grasp_web/grasp_web.biotools.json
@@ -0,0 +1,208 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T01:57:12.480428Z",
+    "biotoolsCURIE": "biotools:grasp_web",
+    "biotoolsID": "grasp_web",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "m.boden@uq.edu.au",
+            "name": "Mikael Bodén",
+            "orcidid": "https://orcid.org/0000-0003-3548-268X",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "e.gillam@uq.edu.au",
+            "name": "Elizabeth M. J. Gillam",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ariane Mora",
+            "orcidid": "https://orcid.org/0000-0003-1331-8192"
+        },
+        {
+            "name": "Gabriel Foley",
+            "orcidid": "https://orcid.org/0000-0002-0487-2629"
+        }
+    ],
+    "description": "Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP).",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Phylogenetic tree",
+                        "uri": "http://edamontology.org/data_0872"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Sequence alignment",
+                        "uri": "http://edamontology.org/data_0863"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Ancestral reconstruction",
+                    "uri": "http://edamontology.org/operation_3745"
+                },
+                {
+                    "term": "Phylogenetic inference",
+                    "uri": "http://edamontology.org/operation_0323"
+                },
+                {
+                    "term": "Protein identification",
+                    "uri": "http://edamontology.org/operation_3767"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://grasp.scmb.uq.edu.au",
+    "language": [
+        "Java",
+        "JavaScript"
+    ],
+    "lastUpdate": "2022-12-31T01:57:12.483780Z",
+    "license": "AGPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://bodenlab.github.io/GRASP-suite"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/bodenlab/GRASP"
+        }
+    ],
+    "name": "GRASP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PCBI.1010633",
+            "metadata": {
+                "abstract": "Copyright: © 2022 Foley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.",
+                "authors": [
+                    {
+                        "name": "Balderson B."
+                    },
+                    {
+                        "name": "Barnard R.T."
+                    },
+                    {
+                        "name": "Boden M."
+                    },
+                    {
+                        "name": "Bottoms S."
+                    },
+                    {
+                        "name": "Carsten J."
+                    },
+                    {
+                        "name": "Essebier A."
+                    },
+                    {
+                        "name": "Foley G."
+                    },
+                    {
+                        "name": "Gillam E.M.J."
+                    },
+                    {
+                        "name": "Guddat L."
+                    },
+                    {
+                        "name": "Gumulya Y."
+                    },
+                    {
+                        "name": "Haltrich D."
+                    },
+                    {
+                        "name": "Kobe B."
+                    },
+                    {
+                        "name": "Lamprecht M.L."
+                    },
+                    {
+                        "name": "Mora A."
+                    },
+                    {
+                        "name": "Newell R."
+                    },
+                    {
+                        "name": "Ross C.M."
+                    },
+                    {
+                        "name": "Rost B."
+                    },
+                    {
+                        "name": "Schenk G."
+                    },
+                    {
+                        "name": "Sieber V."
+                    },
+                    {
+                        "name": "Sutzl L."
+                    },
+                    {
+                        "name": "Thomson R.E.S."
+                    },
+                    {
+                        "name": "Zaugg J."
+                    }
+                ],
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP)"
+            },
+            "pmcid": "PMC9632902",
+            "pmid": "36279274"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Enzymes",
+            "uri": "http://edamontology.org/topic_0821"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Molecular evolution",
+            "uri": "http://edamontology.org/topic_3945"
+        },
+        {
+            "term": "Phylogeny",
+            "uri": "http://edamontology.org/topic_0084"
+        }
+    ]
+}
diff --git a/data/gravis/gravis.biotools.json b/data/gravis/gravis.biotools.json
new file mode 100644
index 0000000000000..1393bda16a1ac
--- /dev/null
+++ b/data/gravis/gravis.biotools.json
@@ -0,0 +1,100 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T01:48:54.201819Z",
+    "biotoolsCURIE": "biotools:gravis",
+    "biotoolsID": "gravis",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Chixiang Lu",
+            "orcidid": "https://orcid.org/0000-0003-0665-2627"
+        },
+        {
+            "name": "Hong-Yu Zhou",
+            "orcidid": "https://orcid.org/0000-0002-1256-7050"
+        },
+        {
+            "name": "Liansheng Wang",
+            "orcidid": "https://orcid.org/0000-0002-2096-454X"
+        },
+        {
+            "name": "Yizhou Yu",
+            "orcidid": "https://orcid.org/0000-0002-0470-5548"
+        }
+    ],
+    "description": "Grouping Augmented Views from Independent Sources for Dermatology Analysis.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Image annotation",
+                    "uri": "http://edamontology.org/operation_3553"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://bit.ly/3xiFyjx",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-31T01:48:54.204465Z",
+    "license": "Not licensed",
+    "name": "GraVIS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1109/TMI.2022.3216005",
+            "metadata": {
+                "abstract": "© 2022 IEEE.Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for downstream tasks. Recent self-supervised learning methods are dominated by noise-contrastive estimation (NCE, also known as contrastive learning), which aims to learn invariant visual representations by contrasting one homogeneous image pair with a large number of heterogeneous image pairs in each training step. Nonetheless, NCE-based approaches still suffer from one major problem that is one homogeneous pair is not enough to extract robust and invariant semantic information. Inspired by the archetypical triplet loss, we propose GraVIS, which is specifically optimized for learning self-supervised features from dermatology images, to group homogeneous dermatology images while separating heterogeneous ones. In addition, a hardness-aware attention is introduced and incorporated to address the importance of homogeneous image views with similar appearance instead of those dissimilar homogeneous ones. GraVIS significantly outperforms its transfer learning and self-supervised learning counterparts in both lesion segmentation and disease classification tasks, sometimes by 5 percents under extremely limited supervision. More importantly, when equipped with the pre-trained weights provided by GraVIS, a single model could achieve better results than winners that heavily rely on ensemble strategies in the well-known ISIC 2017 challenge.",
+                "authors": [
+                    {
+                        "name": "Lu C."
+                    },
+                    {
+                        "name": "Wang L."
+                    },
+                    {
+                        "name": "Yu Y."
+                    },
+                    {
+                        "name": "Zhou H.-Y."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "IEEE Transactions on Medical Imaging",
+                "title": "GraVIS: Grouping Augmented Views From Independent Sources for Dermatology Analysis"
+            },
+            "pmid": "36260573"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Dermatology",
+            "uri": "http://edamontology.org/topic_3404"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/groovdb/groovdb.biotools.json b/data/groovdb/groovdb.biotools.json
new file mode 100644
index 0000000000000..a7d80408acbcc
--- /dev/null
+++ b/data/groovdb/groovdb.biotools.json
@@ -0,0 +1,92 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T23:13:27.779577Z",
+    "biotoolsCURIE": "biotools:groovdb",
+    "biotoolsID": "groovdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Andrew D. Ellington",
+            "orcidid": "http://orcid.org/0000-0001-6246-5338"
+        },
+        {
+            "name": "Simon d’Oelsnitz",
+            "orcidid": "http://orcid.org/0000-0001-7512-9157"
+        }
+    ],
+    "description": "A database of ligand-inducible transcription factors.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Transcription factor binding site prediction",
+                    "uri": "http://edamontology.org/operation_0445"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://groov.bio",
+    "lastUpdate": "2023-02-27T23:13:27.783822Z",
+    "name": "GroovDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/acssynbio.2c00382",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society. All rights reserved.Genetic biosensors are integral to synthetic biology. In particular, ligand-inducible prokaryotic transcription factors are frequently used in high-throughput screening, for dynamic feedback regulation, as multilayer logic gates, and in diagnostic applications. In order to provide a curated source that users can rely on for engineering applications, we have developed GroovDB (available at https://groov.bio), a Web-accessible database of ligand-inducible transcription factors that contains all information necessary to build chemically responsive genetic circuits, including biosensor sequence, ligand, and operator data. Ligand and DNA interaction data have been verified against the literature, while an automated data curation pipeline is used to programmatically fetch metadata, structural information, and references for every entry. A custom tool to visualize the natural genetic context of biosensor entries provides potential insights into alternative ligands and systems biology.",
+                "authors": [
+                    {
+                        "name": "D'oelsnitz S."
+                    },
+                    {
+                        "name": "Diaz D.J."
+                    },
+                    {
+                        "name": "Ellington A.D."
+                    },
+                    {
+                        "name": "Love J.D."
+                    }
+                ],
+                "date": "2022-10-21T00:00:00Z",
+                "journal": "ACS Synthetic Biology",
+                "title": "GroovDB: A Database of Ligand-Inducible Transcription Factors"
+            },
+            "pmid": "36178800"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Synthetic biology",
+            "uri": "http://edamontology.org/topic_3895"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/grop/grop.biotools.json b/data/grop/grop.biotools.json
new file mode 100644
index 0000000000000..8a0f68204cab3
--- /dev/null
+++ b/data/grop/grop.biotools.json
@@ -0,0 +1,137 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T01:41:26.301394Z",
+    "biotoolsCURIE": "biotools:grop",
+    "biotoolsID": "grop",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "keanjin.lim@zafu.edu.cn",
+            "name": "Kean-Jin Lim",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "wzhj21@163.com",
+            "name": "Zhengjia Wang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Hongmiao Jin"
+        },
+        {
+            "name": "Wenlei Guo"
+        }
+    ],
+    "description": "A genomic information repository for oilplants.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Genome assembly",
+                    "uri": "http://edamontology.org/operation_0525"
+                },
+                {
+                    "term": "Genome visualisation",
+                    "uri": "http://edamontology.org/operation_3208"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.grop.site",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-31T01:41:26.304394Z",
+    "name": "GROP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FPLS.2022.1023938",
+            "metadata": {
+                "abstract": "Copyright © 2022 Guo, Jin, Chen, Huang, Zheng, Cheng, Liu, Yang, Chen, Lim and Wang.Biomass energy is an essential component of the agriculture economy and represents an important and particularly significant renewable energy source in the fight against fossil fuel depletion and global warming. The recognition that many plants naturally synthesize hydrocarbons makes these oil plants indispensable resources for biomass energy, and the advancement of next-generation sequencing technology in recent years has now made available mountains of data on plants that synthesize oil. We have utilized a combination of bioinformatic protocols to acquire key information from this massive amount of genomic data and to assemble it into an oil plant genomic information repository, built through website technology, including Django, Bootstrap, and echarts, to create the Genomic Information Repository for Oil Plants (GROP) portal (http://grop.site/) for genomics research on oil plants. The current version of GROP integrates the coding sequences, protein sequences, genome structure, functional annotation information, and other information from 18 species, 22 genome assemblies, and 46 transcriptomes. GROP also provides BLAST, genome browser, functional enrichment, and search tools. The integration of the massive amounts of oil plant genomic data with key bioinformatics tools in a database with a user-friendly interface allows GROP to serve as a central information repository to facilitate studies on oil plants by researchers worldwide.",
+                "authors": [
+                    {
+                        "name": "Chen F."
+                    },
+                    {
+                        "name": "Chen J."
+                    },
+                    {
+                        "name": "Cheng Z."
+                    },
+                    {
+                        "name": "Guo W."
+                    },
+                    {
+                        "name": "Huang J."
+                    },
+                    {
+                        "name": "Jin H."
+                    },
+                    {
+                        "name": "Lim K.-J."
+                    },
+                    {
+                        "name": "Liu X."
+                    },
+                    {
+                        "name": "Wang Z."
+                    },
+                    {
+                        "name": "Yang Z."
+                    },
+                    {
+                        "name": "Zheng D."
+                    }
+                ],
+                "date": "2022-10-06T00:00:00Z",
+                "journal": "Frontiers in Plant Science",
+                "title": "GROP: A genomic information repository for oilplants"
+            },
+            "pmcid": "PMC9583018",
+            "pmid": "36275551"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/gsca_cancer/gsca_cancer.biotools.json b/data/gsca_cancer/gsca_cancer.biotools.json
new file mode 100644
index 0000000000000..3f98ca1533ff4
--- /dev/null
+++ b/data/gsca_cancer/gsca_cancer.biotools.json
@@ -0,0 +1,112 @@
+{
+    "additionDate": "2023-02-25T14:00:51.220568Z",
+    "biotoolsCURIE": "biotools:gsca_cancer",
+    "biotoolsID": "gsca_cancer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "guoay@hust.edu.cn",
+            "name": "Yan Zeng",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zengyan68@wust.edu.cn",
+            "name": "An-Yuan Guo",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                },
+                {
+                    "term": "Statistical calculation",
+                    "uri": "http://edamontology.org/operation_2238"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bioinfo.life.hust.edu.cn/GSCA",
+    "lastUpdate": "2023-02-25T14:00:51.223294Z",
+    "license": "Other",
+    "name": "GSCA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC558",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify potential biological insights on the gene set level. However, platforms exploring cancer multi-omics data using GSA methods are lacking. In this study, we upgraded our GSCALite to GSCA (gene set cancer analysis, http://bioinfo.life.hust.edu.cn/GSCA) for cancer GSA at genomic, pharmacogenomic and immunogenomic levels. In this improved GSCA, we integrated expression, mutation, drug sensitivity and clinical data from four public data sources for 33 cancer types. We introduced useful features to GSCA, including associations between immune infiltration with gene expression and genomic variations, and associations between gene set expression/mutation and clinical outcomes. GSCA has four main functional modules for cancer GSA to explore, analyze and visualize expression, genomic variations, tumor immune infiltration, drug sensitivity and their associations with clinical outcomes. We used case studies of three gene sets: (i) seven cell cycle genes, (ii) tumor suppressor genes of PI3K pathway and (iii) oncogenes of PI3K pathway to prove the advantage of GSCA over single gene analysis. We found novel associations of gene set expression and mutation with clinical outcomes in different cancer types on gene set level, while on single gene analysis level, they are not significant associations. In conclusion, GSCA is a user-friendly web server and a useful resource for conducting hypothesis tests by using GSA methods at genomic, pharmacogenomic and immunogenomic levels.",
+                "authors": [
+                    {
+                        "name": "Guo A.-Y."
+                    },
+                    {
+                        "name": "Hu F.-F."
+                    },
+                    {
+                        "name": "Li X.-W."
+                    },
+                    {
+                        "name": "Liu C.-J."
+                    },
+                    {
+                        "name": "Miao Y.-R."
+                    },
+                    {
+                        "name": "Xie G.-Y."
+                    },
+                    {
+                        "name": "Zeng Y."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels"
+            },
+            "pmid": "36549921"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pharmacogenomics",
+            "uri": "http://edamontology.org/topic_0208"
+        }
+    ]
+}
diff --git a/data/gseapy/gseapy.biotools.json b/data/gseapy/gseapy.biotools.json
new file mode 100644
index 0000000000000..57d782ae326ed
--- /dev/null
+++ b/data/gseapy/gseapy.biotools.json
@@ -0,0 +1,99 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T14:32:01.587640Z",
+    "biotoolsCURIE": "biotools:gseapy",
+    "biotoolsID": "gseapy",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "gpeltz@stanford.edu",
+            "name": "Gary Peltz",
+            "orcidid": "https://orcid.org/0000-0001-6191-7697",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A comprehensive package for performing gene set enrichment analysis in Python.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pypi.org/project/gseapy/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T14:32:01.591567Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/zqfang/GSEApy"
+        }
+    ],
+    "name": "GSEApy",
+    "operatingSystem": [
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC757",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Gene set enrichment analysis (GSEA) is a commonly used algorithm for characterizing gene expression changes. However, the currently available tools used to perform GSEA have a limited ability to analyze large datasets, which is particularly problematic for the analysis of single-cell data. To overcome this limitation, we developed a GSEA package in Python (GSEApy), which could efficiently analyze large single-cell datasets. RESULTS: We present a package (GSEApy) that performs GSEA in either the command line or Python environment. GSEApy uses a Rust implementation to enable it to calculate the same enrichment statistic as GSEA for a collection of pathways. The Rust implementation of GSEApy is 3-fold faster than the Numpy version of GSEApy (v0.10.8) and uses >4-fold less memory. GSEApy also provides an interface between Python and Enrichr web services, as well as for BioMart. The Enrichr application programming interface enables GSEApy to perform over-representation analysis for an input gene list. Furthermore, GSEApy consists of several tools, each designed to facilitate a particular type of enrichment analysis. AVAILABILITY AND IMPLEMENTATION: The new GSEApy with Rust extension is deposited in PyPI: https://pypi.org/project/gseapy/. The GSEApy source code is freely available at https://github.com/zqfang/GSEApy. Also, the documentation website is available at https://gseapy.rtfd.io/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Fang Z."
+                    },
+                    {
+                        "name": "Liu X."
+                    },
+                    {
+                        "name": "Peltz G."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "GSEApy: a comprehensive package for performing gene set enrichment analysis in Python"
+            },
+            "pmcid": "PMC9805564",
+            "pmid": "36426870"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/gspa/gspa.biotools.json b/data/gspa/gspa.biotools.json
new file mode 100644
index 0000000000000..a57dbb689945a
--- /dev/null
+++ b/data/gspa/gspa.biotools.json
@@ -0,0 +1,106 @@
+{
+    "additionDate": "2023-01-28T14:35:06.543196Z",
+    "biotoolsCURIE": "biotools:gspa",
+    "biotoolsID": "gspa",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "cousinsh@stanford.edu",
+            "name": "Henry Cousins",
+            "orcidid": "https://orcid.org/0000-0002-8694-0604",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "russ.altman@stanford.edu",
+            "name": "Russ B Altman",
+            "orcidid": "https://orcid.org/0000-0003-3859-2905",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Gene set proximity analysis (GSPA) is a method for identifying critical gene sets in functional genetic datasets using low-dimensional gene embeddings.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                },
+                {
+                    "term": "Protein-protein interaction analysis",
+                    "uri": "http://edamontology.org/operation_2949"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/henrycousins/gspa",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T14:35:06.545928Z",
+    "license": "BSD-3-Clause",
+    "name": "GSPA",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC735",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. However, explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods. RESULTS: We propose an extension of gene set enrichment analysis to a latent embedding space reflecting PPI network topology, called gene set proximity analysis (GSPA). Compared with existing methods, GSPA provides improved ability to identify disease-associated pathways in disease-matched gene expression datasets, while improving reproducibility of enrichment statistics for similar gene sets. GSPA is statistically straightforward, reducing to a version of traditional gene set enrichment analysis through a single user-defined parameter. We apply our method to identify novel drug associations with SARS-CoV-2 viral entry. Finally, we validate our drug association predictions through retrospective clinical analysis of claims data from 8 million patients, supporting a role for gabapentin as a risk factor and metformin as a protective factor for severe COVID-19. AVAILABILITY AND IMPLEMENTATION: GSPA is available for download as a command-line Python package at https://github.com/henrycousins/gspa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Altman R.B."
+                    },
+                    {
+                        "name": "Cong L."
+                    },
+                    {
+                        "name": "Cousins H."
+                    },
+                    {
+                        "name": "Guo Y."
+                    },
+                    {
+                        "name": "Hall T."
+                    },
+                    {
+                        "name": "Tso L."
+                    },
+                    {
+                        "name": "Tzeng K.T.H."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19"
+            },
+            "pmcid": "PMC9805577",
+            "pmid": "36394254"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genetics",
+            "uri": "http://edamontology.org/topic_3053"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Pharmacology",
+            "uri": "http://edamontology.org/topic_0202"
+        }
+    ]
+}
diff --git a/data/haplodmf/haplodmf.biotools.json b/data/haplodmf/haplodmf.biotools.json
new file mode 100644
index 0000000000000..469d7eb62c596
--- /dev/null
+++ b/data/haplodmf/haplodmf.biotools.json
@@ -0,0 +1,87 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T01:30:53.802498Z",
+    "biotoolsCURIE": "biotools:haplodmf",
+    "biotoolsID": "haplodmf",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yannisun@cityu.edu.hk",
+            "name": "Yanni Sun",
+            "orcidid": "https://orcid.org/0000-0003-1373-8023",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Dehan Cai",
+            "orcidid": "https://orcid.org/0000-0002-8148-4574"
+        },
+        {
+            "name": "Jiayu Shang",
+            "orcidid": "https://orcid.org/0000-0001-5974-4985"
+        }
+    ],
+    "description": "Viral Haplotype reconstruction from long reads via Deep Matrix Factorization.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Haplotype mapping",
+                    "uri": "http://edamontology.org/operation_0487"
+                },
+                {
+                    "term": "Read mapping",
+                    "uri": "http://edamontology.org/operation_3198"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/dhcai21/HaploDMF",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2022-12-31T01:30:53.805018Z",
+    "license": "Not licensed",
+    "name": "HaploDMF",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC708",
+            "pmid": "36308467"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/hariboss/hariboss.biotools.json b/data/hariboss/hariboss.biotools.json
new file mode 100644
index 0000000000000..091d888783ff6
--- /dev/null
+++ b/data/hariboss/hariboss.biotools.json
@@ -0,0 +1,120 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-22T02:33:07.168655Z",
+    "biotoolsCURIE": "biotools:hariboss",
+    "biotoolsID": "hariboss",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Paraskevi.Gkeka@sanofi.com",
+            "name": "P. Gkeka",
+            "orcidid": "http://orcid.org/0000-0002-0752-3539",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "mbonomi@pasteur.fr",
+            "name": "M. Bonomi",
+            "orcidid": "http://orcid.org/0000-0002-7321-0004",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "F. P. Panei",
+            "orcidid": "http://orcid.org/0000-0002-6272-9126"
+        },
+        {
+            "name": "H. Menager",
+            "orcidid": "http://orcid.org/0000-0002-7552-1009"
+        },
+        {
+            "name": "R. Torchet",
+            "orcidid": "http://orcid.org/0000-0002-2306-5566"
+        }
+    ],
+    "description": "A curated database of RNA-small molecules structures to aid rational drug design.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein-ligand docking",
+                    "uri": "http://edamontology.org/operation_0482"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://hariboss.pasteur.cloud",
+    "lastUpdate": "2023-01-22T02:33:07.171327Z",
+    "name": "HARIBOSS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac483",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.MOTIVATION: RNA molecules are implicated in numerous fundamental biological processes and many human pathologies, such as cancer, neurodegenerative disorders, muscular diseases and bacterial infections. Modulating the mode of action of disease-implicated RNA molecules can lead to the discovery of new therapeutical agents and even address pathologies linked to 'undruggable' protein targets. This modulation can be achieved by direct targeting of RNA with small molecules. As of today, only a few RNA-targeting small molecules are used clinically. One of the main obstacles that have hampered the development of a rational drug design protocol to target RNA with small molecules is the lack of a comprehensive understanding of the molecular mechanisms at the basis of RNA-small molecule (RNA-SM) recognition. RESULTS: Here, we present Harnessing RIBOnucleic acid-Small molecule Structures (HARIBOSS), a curated collection of RNA-SM structures determined by X-ray crystallography, nuclear magnetic resonance spectroscopy and cryo-electron microscopy. HARIBOSS facilitates the exploration of drug-like compounds known to bind RNA, the analysis of ligands and pockets properties and ultimately the development of in silico strategies to identify RNA-targeting small molecules. AVAILABILITY AND IMPLEMENTATION: HARIBOSS can be explored via a web interface available at http://hariboss.pasteur.cloud. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Bonomi M."
+                    },
+                    {
+                        "name": "Gkeka P."
+                    },
+                    {
+                        "name": "Menager H."
+                    },
+                    {
+                        "name": "Panei F.P."
+                    },
+                    {
+                        "name": "Torchet R."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-09-02T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "HARIBOSS: a curated database of RNA-small molecules structures to aid rational drug design"
+            },
+            "pmid": "35799352"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "X-ray diffraction",
+            "uri": "http://edamontology.org/topic_2828"
+        }
+    ]
+}
diff --git a/data/hcdt/hcdt.biotools.json b/data/hcdt/hcdt.biotools.json
new file mode 100644
index 0000000000000..b66e824c15031
--- /dev/null
+++ b/data/hcdt/hcdt.biotools.json
@@ -0,0 +1,136 @@
+{
+    "additionDate": "2023-01-28T14:42:00.611396Z",
+    "biotoolsCURIE": "biotools:hcdt",
+    "biotoolsID": "hcdt",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "lijin@hainmc.edu.cn",
+            "name": "Jin Li",
+            "orcidid": "https://orcid.org/0000-0002-6131-456X",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "lixia@hrbmu.edu.cn",
+            "name": "Xia Li",
+            "orcidid": "https://orcid.org/0000-0002-9794-2648",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "wanglm@hainmc.edu.cn",
+            "name": "Limei Wang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "HCDT (Highly Confident Drug-Target Database) is a combined database that provides validated associations between drugs and target genes.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Pathway analysis",
+                    "uri": "http://edamontology.org/operation_3928"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://hainmu-biobigdata.com/hcdt",
+    "lastUpdate": "2023-01-28T14:42:00.614272Z",
+    "license": "Other",
+    "name": "HCDT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/DATABASE/BAAC101",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press.Drug-Target association plays an important role in drug discovery, drug repositioning, drug synergy prediction, etc. Currently, a lot of drug-related databases, such as DrugBank and BindingDB, have emerged. However, these databases are separate, incomplete and non-uniform with different criteria. Here, we integrated eight drug-related databases; collected, filtered and supplemented drugs, target genes and experimentally validated (highly confident) associations and built a highly confident drug-Target (HCDT: http://hainmu-biobigdata.com/hcdt) database. HCDT database includes 500 681 HCDT associations between 299 458 drugs and 5618 target genes. Compared to individual databases, HCDT database contains 1.1 to 254.2 times drugs, 1.8-5.5 times target genes and 1.4-27.7 times drug-Target associations. It is normative, publicly available and easy for searching, browsing and downloading. Together with multi-omics data, it will be a good resource in analyzing the drug functional mechanism, mining drug-related biological pathways, predicting drug synergy, etc. Database URL: http://hainmu-biobigdata.com/hcdt",
+                "authors": [
+                    {
+                        "name": "Bi X."
+                    },
+                    {
+                        "name": "Chen J."
+                    },
+                    {
+                        "name": "Chen R."
+                    },
+                    {
+                        "name": "Chen Z."
+                    },
+                    {
+                        "name": "Feng D."
+                    },
+                    {
+                        "name": "Han H."
+                    },
+                    {
+                        "name": "Li J."
+                    },
+                    {
+                        "name": "Li K."
+                    },
+                    {
+                        "name": "Li T."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Wang L."
+                    },
+                    {
+                        "name": "Wang Z."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Database",
+                "title": "HCDT: An integrated highly confident drug-Target resource"
+            },
+            "pmcid": "PMC9684616",
+            "pmid": "36420558"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Drug development",
+            "uri": "http://edamontology.org/topic_3373"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Safety sciences",
+            "uri": "http://edamontology.org/topic_3377"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/hclc-fc/hclc-fc.biotools.json b/data/hclc-fc/hclc-fc.biotools.json
new file mode 100644
index 0000000000000..86f6666279df0
--- /dev/null
+++ b/data/hclc-fc/hclc-fc.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T21:14:36.676546Z",
+    "biotoolsCURIE": "biotools:hclc-fc",
+    "biotoolsID": "hclc-fc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "shuzhang@mtu.edu",
+            "name": "Shuanglin Zhang",
+            "orcidid": "http://orcid.org/0000-0002-9478-1199",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Qiuying Sha",
+            "orcidid": "http://orcid.org/0000-0002-9342-3269"
+        },
+        {
+            "name": "Xiaoyu Liang",
+            "orcidid": "http://orcid.org/0000-0001-7796-2441"
+        },
+        {
+            "name": "Xuewei Cao",
+            "orcidid": "http://orcid.org/0000-0003-2136-0964"
+        }
+    ],
+    "description": "A novel statistical method for phenome-wide association studies.\n\nWe derived a novel and powerful multivariate method, which we referred to as HCLC-FC (Hierarchical Clustering Linear Combination with False discovery rate Control), to test the association between a genetic variant with multiple phenotypes for each phenotypic category in phenome-wide association studies (PheWAS). The R package HCLCFC is a novel tool that allows users to partition a large number of phenotypes into disjoint clusters; applicable to electronic medical records (EMR)-based PheWAS.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/XiaoyuLiang/HCLCFC",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-17T21:14:36.679151Z",
+    "license": "Not licensed",
+    "name": "HCLC-FC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/journal.pone.0276646",
+            "metadata": {
+                "abstract": "© 2022 Liang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association studies (PheWAS). In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from the UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at https://github.com/XiaoyuLiang/HCLCFC.",
+                "authors": [
+                    {
+                        "name": "Cao X."
+                    },
+                    {
+                        "name": "Liang X."
+                    },
+                    {
+                        "name": "Sha Q."
+                    },
+                    {
+                        "name": "Zhang S."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "HCLC-FC: A novel statistical method for phenome-wide association studies"
+            },
+            "pmcid": "PMC9645610",
+            "pmid": "36350801"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Phenomics",
+            "uri": "http://edamontology.org/topic_3298"
+        }
+    ]
+}
diff --git a/data/hcovdock/hcovdock.biotools.json b/data/hcovdock/hcovdock.biotools.json
new file mode 100644
index 0000000000000..b05f27a0b3939
--- /dev/null
+++ b/data/hcovdock/hcovdock.biotools.json
@@ -0,0 +1,102 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T09:37:08.714722Z",
+    "biotoolsCURIE": "biotools:hcovdock",
+    "biotoolsID": "hcovdock",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "huangsy@hust.edu.cn",
+            "name": "Sheng-You Huang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Qilong Wu"
+        }
+    ],
+    "description": "An efficient docking method for modeling covalent protein-ligand interactions.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Hydrogen bond calculation",
+                    "uri": "http://edamontology.org/operation_0394"
+                },
+                {
+                    "term": "Protein-ligand docking",
+                    "uri": "http://edamontology.org/operation_0482"
+                },
+                {
+                    "term": "Protein-protein docking",
+                    "uri": "http://edamontology.org/operation_3899"
+                },
+                {
+                    "term": "Side chain modelling",
+                    "uri": "http://edamontology.org/operation_0480"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://huanglab.phys.hust.edu.cn/hcovdock/",
+    "lastUpdate": "2023-02-26T09:37:08.717963Z",
+    "name": "HCovDock",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC559",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Covalent inhibitors have received extensive attentions in the past few decades because of their long residence time, high binding efficiency and strong selectivity. Therefore, it is valuable to develop computational tools like molecular docking for modeling of covalent protein-ligand interactions or screening of potential covalent drugs. Meeting the needs, we have proposed HCovDock, an efficient docking algorithm for covalent protein-ligand interactions by integrating a ligand sampling method of incremental construction and a scoring function with covalent bond-based energy. Tested on a benchmark containing 207 diverse protein-ligand complexes, HCovDock exhibits a significantly better performance than seven other state-of-the-art covalent docking programs (AutoDock, Cov_DOX, CovDock, FITTED, GOLD, ICM-Pro and MOE). With the criterion of ligand root-mean-squared distance < 2.0 Å, HCovDock obtains a high success rate of 70.5% and 93.2% in reproducing experimentally observed structures for top 1 and top 10 predictions. In addition, HCovDock is also validated in virtual screening against 10 receptors of three proteins. HCovDock is computationally efficient and the average running time for docking a ligand is only 5 min with as fast as 1 sec for ligands with one rotatable bond and about 18 min for ligands with 23 rotational bonds. HCovDock can be freely assessed at http://huanglab.phys.hust.edu.cn/hcovdock/.",
+                "authors": [
+                    {
+                        "name": "Huang S.-Y."
+                    },
+                    {
+                        "name": "Wu Q."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "HCovDock: an efficient docking method for modeling covalent protein-ligand interactions"
+            },
+            "pmid": "36573474"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/hdac1_predictor/hdac1_predictor.biotools.json b/data/hdac1_predictor/hdac1_predictor.biotools.json
new file mode 100644
index 0000000000000..808086031e544
--- /dev/null
+++ b/data/hdac1_predictor/hdac1_predictor.biotools.json
@@ -0,0 +1,123 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T09:32:11.581653Z",
+    "biotoolsCURIE": "biotools:hdac1_predictor",
+    "biotoolsID": "hdac1_predictor",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "L.D. Grigoreva",
+            "orcidid": "https://orcid.org/0000-0002-3854-0059"
+        },
+        {
+            "name": "O.V. Tinkov",
+            "orcidid": "https://orcid.org/0000-0003-4702-6825"
+        },
+        {
+            "name": "V.N. Osipov",
+            "orcidid": "https://orcid.org/0000-0001-7726-4467"
+        },
+        {
+            "name": "V.Y. Grigorev",
+            "orcidid": "https://orcid.org/0000-0002-5288-3242"
+        }
+    ],
+    "description": "A simple and transparent application for virtual screening of histone deacetylase 1 inhibitors.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/ovttiras/HDAC1-inhibitors/blob/main/manual.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "SMILES string",
+                        "uri": "http://edamontology.org/data_2301"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ovttiras-hdac1-inhibitors-hdac1-predictor-app-z3mrbr.streamlit.app",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T09:33:36.904742Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/ovttiras/HDAC1-inhibitors"
+        }
+    ],
+    "name": "HDAC1 PREDICTOR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1080/1062936X.2022.2147996",
+            "metadata": {
+                "abstract": "© 2022 Informa UK Limited, trading as Taylor & Francis Group.Histone deacetylases play an important role in regulating gene expression by modifying histones and changing chromatin conformation. HDAC dysregulation is involved in many diseases, such as cancer, autoimmune and neurodegenerative diseases. Histone deacetylase 1 (HDAC1) inhibitors represent an important class of drugs. Quantitative Structure-Activity Relationship (QSAR) classification models were developed using 2D RDKit molecular descriptors; ECPF4 (Extended Connectivity Fingerprint) circular fingerprints; and the Random Forest, Gradient Boosting, and Support Vector Machine methods. The developed models were integrated into the HDAC1 PREDICTOR application, which is freely available at the link https://ovttiras-hdac1-inhibitors-hdac1-predictor-app-z3mrbr.streamlitapp.com. The HDAC1 PREDICTOR web application allows one to reveal the compounds for which the predicted activity to inhibit HDAC1 is higher than that of the reference Vorinostat compound (IC50 = 11.08 nM). The algorithm implemented in HDAC1 PREDICTOR for determining the contributions of molecular fragments to the inhibitory activity can be used to find the molecule segments that increase or decrease the activity, enabling the researcher to conduct a rational molecular design of new highly active HDAC1 inhibitors. The developed QSAR models and the code for their construction in the Python programming language are freely available on the GitHub platform at https://github.com/ovttiras/HDAC1-inhibitors.",
+                "authors": [
+                    {
+                        "name": "Grigorev V.Y."
+                    },
+                    {
+                        "name": "Grigoreva L.D."
+                    },
+                    {
+                        "name": "Osipov V.N."
+                    },
+                    {
+                        "name": "Tinkov O.V."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "SAR and QSAR in Environmental Research",
+                "title": "HDAC1 PREDICTOR: a simple and transparent application for virtual screening of histone deacetylase 1 inhibitors"
+            },
+            "pmid": "36548122"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/health_gym/health_gym.biotools.json b/data/health_gym/health_gym.biotools.json
new file mode 100644
index 0000000000000..fa5a4b9594dba
--- /dev/null
+++ b/data/health_gym/health_gym.biotools.json
@@ -0,0 +1,98 @@
+{
+    "additionDate": "2023-01-28T14:49:32.845722Z",
+    "biotoolsCURIE": "biotools:health_gym",
+    "biotoolsID": "health_gym",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "n.kuo@unsw.edu.au",
+            "name": "Nicholas I-Hsien Kuo",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A growing collection of highly realistic synthetic medical datasets that can be freely accessed to prototype, evaluate, and compare machine learning algorithms, with a specific focus on reinforcement learning.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Nic5472K/ScientificData2021_HealthGym",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T14:49:32.848249Z",
+    "license": "MIT",
+    "name": "Health Gym",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41597-022-01784-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Clinical data are usually not openly available due to their confidential nature. This has hampered the development of reproducible and generalisable machine learning applications in health care. Here we introduce the Health Gym - a growing collection of highly realistic synthetic medical datasets that can be freely accessed to prototype, evaluate, and compare machine learning algorithms, with a specific focus on reinforcement learning. The three synthetic datasets described in this paper present patient cohorts with acute hypotension and sepsis in the intensive care unit, and people with human immunodeficiency virus (HIV) receiving antiretroviral therapy. The datasets were created using a novel generative adversarial network (GAN). The distributions of variables, and correlations between variables and trends in variables over time in the synthetic datasets mirror those in the real datasets. Furthermore, the risk of sensitive information disclosure associated with the public distribution of the synthetic datasets is estimated to be very low.",
+                "authors": [
+                    {
+                        "name": "Barbieri S."
+                    },
+                    {
+                        "name": "Bohm M."
+                    },
+                    {
+                        "name": "Finfer S."
+                    },
+                    {
+                        "name": "Garcia F."
+                    },
+                    {
+                        "name": "Jorm L."
+                    },
+                    {
+                        "name": "Kaiser R."
+                    },
+                    {
+                        "name": "Kuo N.I.-H."
+                    },
+                    {
+                        "name": "Polizzotto M.N."
+                    },
+                    {
+                        "name": "Sonnerborg A."
+                    },
+                    {
+                        "name": "Zazzi M."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Data",
+                "title": "The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms"
+            },
+            "pmcid": "PMC9652426",
+            "pmid": "36369205"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Critical care medicine",
+            "uri": "http://edamontology.org/topic_3403"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        }
+    ]
+}
diff --git a/data/helixer/helixer.biotools.json b/data/helixer/helixer.biotools.json
new file mode 100644
index 0000000000000..66c7013bd7f85
--- /dev/null
+++ b/data/helixer/helixer.biotools.json
@@ -0,0 +1,76 @@
+{
+    "additionDate": "2023-02-14T08:38:58.599215Z",
+    "biotoolsCURIE": "biotools:helixer",
+    "biotoolsID": "helixer",
+    "description": "Deep Learning to predict gene annotations",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene prediction",
+                    "uri": "http://edamontology.org/operation_2454"
+                },
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/weberlab-hhu/Helixer",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-14T08:38:58.602948Z",
+    "license": "GPL-3.0",
+    "name": "Helixer",
+    "owner": "alexcorm",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btaa1044",
+            "metadata": {
+                "abstract": "© 2020 The Author(s) 2020. Published by Oxford University Press.Motivation: Current state-of-the-art tools for the de novo annotation of genes in eukaryotic genomes have to be specifically fitted for each species and still often produce annotations that can be improved much further. The fundamental algorithmic architecture for these tools has remained largely unchanged for about two decades, limiting learning capabilities. Here, we set out to improve the cross-species annotation of genes from DNA sequence alone with the help of deep learning. The goal is to eliminate the dependency on a closely related gene model while also improving the predictive quality in general with a fundamentally new architecture. Results: We present Helixer, a framework for the development and usage of a cross-species deep learning model that improves significantly on performance and generalizability when compared to more traditional methods. We evaluate our approach by building a single vertebrate model for the base-wise annotation of 186 animal genomes and a separate land plant model for 51 plant genomes. Our predictions are shown to be much less sensitive to the length of the genome than those of a current state-of-the-art tool. We also present two novel post-processing techniques that each worked to further strengthen our annotations and show in-depth results of an RNA-Seq based comparison of our predictions. Our method does not yet produce comprehensive gene models but rather outputs base pair wise probabilities.",
+                "authors": [
+                    {
+                        "name": "Denton A.K."
+                    },
+                    {
+                        "name": "Dey D."
+                    },
+                    {
+                        "name": "Scholz S."
+                    },
+                    {
+                        "name": "Steinborn M."
+                    },
+                    {
+                        "name": "Stiehler F."
+                    },
+                    {
+                        "name": "Weber A.P.M."
+                    }
+                ],
+                "citationCount": 2,
+                "date": "2020-12-01T00:00:00Z",
+                "journal": "Bioinformatics",
+                "title": "Helixer: Cross-species gene annotation of large eukaryotic genomes using deep learning"
+            }
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
+}
diff --git a/data/hgca/hgca.biotools.json b/data/hgca/hgca.biotools.json
index 7c7ac29823c36..f4851f22add26 100644
--- a/data/hgca/hgca.biotools.json
+++ b/data/hgca/hgca.biotools.json
@@ -67,7 +67,7 @@
     "language": [
         "PHP"
     ],
-    "lastUpdate": "2021-01-25T11:30:33Z",
+    "lastUpdate": "2023-02-03T12:43:13.747682Z",
     "maturity": "Mature",
     "name": "Human Gene Correlation Analysis (HGCA)",
     "operatingSystem": [
@@ -104,13 +104,56 @@
                         "name": "Schneider R."
                     }
                 ],
-                "citationCount": 18,
+                "citationCount": 21,
                 "date": "2012-06-08T00:00:00Z",
                 "journal": "BMC Research Notes",
                 "title": "Human gene correlation analysis (HGCA): A tool for the identification of transcriptionally co-expressed genes"
             },
             "pmcid": "PMC3441226",
             "pmid": "22672625",
+            "type": [
+                "Other"
+            ]
+        },
+        {
+            "doi": "10.3390/biology11071019",
+            "metadata": {
+                "abstract": "© 2022 by the authors. Licensee MDPI, Basel, Switzerland.Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied ex-tensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensi-ble account of the steps required for performing a complete gene coexpression analysis in eukary-otic organisms. We comment on the use of RNA‐Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.",
+                "authors": [
+                    {
+                        "name": "Iconomidou V.A."
+                    },
+                    {
+                        "name": "Malatras A."
+                    },
+                    {
+                        "name": "Michalopoulos I."
+                    },
+                    {
+                        "name": "Papadopoulos K."
+                    },
+                    {
+                        "name": "Saxami G."
+                    },
+                    {
+                        "name": "Tsotra I."
+                    },
+                    {
+                        "name": "Zogopoulos V.L."
+                    }
+                ],
+                "date": "2022-07-01T00:00:00Z",
+                "journal": "Biology",
+                "title": "Approaches in Gene Coexpression Analysis in Eukaryotes"
+            },
+            "pmcid": "PMC9312353",
+            "pmid": "36101400",
+            "type": [
+                "Review"
+            ]
+        },
+        {
+            "doi": "10.3390/cells12030388",
             "type": [
                 "Primary"
             ]
diff --git a/data/hgd_db/hgd_db.biotools.json b/data/hgd_db/hgd_db.biotools.json
new file mode 100644
index 0000000000000..190295eccf936
--- /dev/null
+++ b/data/hgd_db/hgd_db.biotools.json
@@ -0,0 +1,148 @@
+{
+    "additionDate": "2023-01-28T14:55:18.577908Z",
+    "biotoolsCURIE": "biotools:hgd_db",
+    "biotoolsID": "hgd_db",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tangbx@big.ac.cn",
+            "name": "Bixia Tang",
+            "orcidid": "https://orcid.org/0000-0002-9357-4411",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhaowm@big.ac.cn",
+            "name": "Wenming Zhao",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An integrated homologous gene database across multiple species.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Gene functional annotation",
+                    "uri": "http://edamontology.org/operation_3672"
+                },
+                {
+                    "term": "Homology-based gene prediction",
+                    "uri": "http://edamontology.org/operation_3663"
+                },
+                {
+                    "term": "Phylogenetic tree reconciliation",
+                    "uri": "http://edamontology.org/operation_3947"
+                },
+                {
+                    "term": "Relation extraction",
+                    "uri": "http://edamontology.org/operation_3625"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ngdc.cncb.ac.cn/hgd",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T14:55:18.580556Z",
+    "license": "CC-BY-3.0",
+    "name": "HGD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC970",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Homology is fundamental to infer genes' evolutionary processes and relationships with shared ancestry. Existing homolog gene resources vary in terms of inferring methods, homologous relationship and identifiers, posing inevitable difficulties for choosing and mapping homology results from one to another. Here, we present HGD (Homologous Gene Database, https://ngdc.cncb.ac.cn/hgd), a comprehensive homologs resource integrating multi-species, multi-resources and multi-omics, as a complement to existing resources providing public and one-stop data service. Currently, HGD houses a total of 112 383 644 homologous pairs for 37 species, including 19 animals, 16 plants and 2 microorganisms. Meanwhile, HGD integrates various annotations from public resources, including 16 909 homologs with traits, 276 670 homologs with variants, 398 573 homologs with expression and 536 852 homologs with gene ontology (GO) annotations. HGD provides a wide range of omics gene function annotations to help users gain a deeper understanding of gene function.",
+                "authors": [
+                    {
+                        "name": "Bao Y."
+                    },
+                    {
+                        "name": "Chen X."
+                    },
+                    {
+                        "name": "Du Z."
+                    },
+                    {
+                        "name": "Duan G."
+                    },
+                    {
+                        "name": "Gao Y."
+                    },
+                    {
+                        "name": "Hao L."
+                    },
+                    {
+                        "name": "Li Z."
+                    },
+                    {
+                        "name": "Song S."
+                    },
+                    {
+                        "name": "Sun Y."
+                    },
+                    {
+                        "name": "Tang B."
+                    },
+                    {
+                        "name": "Tian D."
+                    },
+                    {
+                        "name": "Wu G."
+                    },
+                    {
+                        "name": "Xiao J."
+                    },
+                    {
+                        "name": "Zhang Z."
+                    },
+                    {
+                        "name": "Zhao W."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "HGD: an integrated homologous gene database across multiple species"
+            },
+            "pmcid": "PMC9825607",
+            "pmid": "36318261"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/hgtree/hgtree.biotools.json b/data/hgtree/hgtree.biotools.json
new file mode 100644
index 0000000000000..adaaa33ca0afc
--- /dev/null
+++ b/data/hgtree/hgtree.biotools.json
@@ -0,0 +1,108 @@
+{
+    "additionDate": "2023-01-28T14:58:17.905317Z",
+    "biotoolsCURIE": "biotools:hgtree",
+    "biotoolsID": "hgtree",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "heebal@snu.ac.kr",
+            "name": "Heebal Kim",
+            "orcidid": "https://orcid.org/0000-0003-3064-1303",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A comprehensive database update for horizontal gene transfer (HGT) events detected by the tree-reconciliation method.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene tree construction",
+                    "uri": "http://edamontology.org/operation_0553"
+                },
+                {
+                    "term": "Phylogenetic tree reconciliation",
+                    "uri": "http://edamontology.org/operation_3947"
+                },
+                {
+                    "term": "Species tree construction",
+                    "uri": "http://edamontology.org/operation_0544"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://hgtree2.snu.ac.kr",
+    "lastUpdate": "2023-01-28T14:58:17.907923Z",
+    "license": "Other",
+    "name": "HGTree",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC929",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.HGTree is a database that provides horizontal gene transfer (HGT) event information on 2472 prokaryote genomes using the tree-reconciliation method. HGTree was constructed in 2015, and a large number of prokaryotic genomes have been additionally published since then. To cope with the rapid rise of prokaryotic genome data, we present HGTree v2.0 (http://hgtree2.snu.ac.kr), a newly updated version of our HGT database with much more extensive data, including a total of 20 536 completely sequenced non-redundant prokaryotic genomes, and more reliable HGT information results curated with various steps. As a result, HGTree v2.0 has a set of expanded data results of 6 361 199 putative horizontally transferred genes integrated with additional functional information such as the KEGG pathway, virulence factors and antimicrobial resistance. Furthermore, various visualization tools in the HGTree v2.0 database website provide intuitive biological insights, allowing the users to investigate their genomes of interest.",
+                "authors": [
+                    {
+                        "name": "Ahn S."
+                    },
+                    {
+                        "name": "Cho S."
+                    },
+                    {
+                        "name": "Choi Y."
+                    },
+                    {
+                        "name": "Kim H."
+                    },
+                    {
+                        "name": "Lee S."
+                    },
+                    {
+                        "name": "Park M."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "HGTree v2.0: a comprehensive database update for horizontal gene transfer (HGT) events detected by the tree-reconciliation method"
+            },
+            "pmcid": "PMC9825516",
+            "pmid": "36350646"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Phylogeny",
+            "uri": "http://edamontology.org/topic_0084"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ],
+    "version": [
+        "2.0"
+    ]
+}
diff --git a/data/hi-c_aggregate/hi-c_aggregate.biotools.json b/data/hi-c_aggregate/hi-c_aggregate.biotools.json
new file mode 100644
index 0000000000000..8a1bfabfa5d74
--- /dev/null
+++ b/data/hi-c_aggregate/hi-c_aggregate.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T09:48:16.395663Z",
+    "biotoolsCURIE": "biotools:hi-c_aggregate",
+    "biotoolsID": "hi-c_aggregate",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jesse.gillis@utoronto.ca",
+            "name": "Jesse Gillis",
+            "orcidid": "https://orcid.org/0000-0002-0936-9774",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Nathan Fox"
+        },
+        {
+            "name": "Ruchi Lohia",
+            "orcidid": "http://orcid.org/0000-0002-3496-8197"
+        }
+    ],
+    "description": "A global high-density chromatin interaction network reveals functional long-range and trans-chromosomal relationships.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Gene expression QTL analysis",
+                    "uri": "http://edamontology.org/operation_3232"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://gillisweb.cshl.edu/HiC/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-08T09:48:16.398212Z",
+    "name": "Hi-C Aggregate",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13059-022-02790-Z",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Chromatin contacts are essential for gene-expression regulation; however, obtaining a high-resolution genome-wide chromatin contact map is still prohibitively expensive owing to large genome sizes and the quadratic scale of pairwise data. Chromosome conformation capture (3C)-based methods such as Hi-C have been extensively used to obtain chromatin contacts. However, since the sparsity of these maps increases with an increase in genomic distance between contacts, long-range or trans-chromatin contacts are especially challenging to sample. Results: Here, we create a high-density reference genome-wide chromatin contact map using a meta-analytic approach. We integrate 3600 human, 6700 mouse, and 500 fly Hi-C experiments to create species-specific meta-Hi-C chromatin contact maps with 304 billion, 193 billion, and 19 billion contacts in respective species. We validate that meta-Hi-C contact maps are uniquely powered to capture functional chromatin contacts in both cis and trans. We find that while individual dataset Hi-C networks are largely unable to predict any long-range coexpression (median 0.54 AUC), meta-Hi-C networks perform comparably in both cis and trans (0.65 AUC vs 0.64 AUC). Similarly, for long-range expression quantitative trait loci (eQTL), meta-Hi-C contacts outperform all individual Hi-C experiments, providing an improvement over the conventionally used linear genomic distance-based association. Assessing between species, we find patterns of chromatin contact conservation in both cis and trans and strong associations with coexpression even in species for which Hi-C data is lacking. Conclusions: We have generated an integrated chromatin interaction network which complements a large number of methodological and analytic approaches focused on improved specificity or interpretation. This high-depth “super-experiment” is surprisingly powerful in capturing long-range functional relationships of chromatin interactions, which are now able to predict coexpression, eQTLs, and cross-species relationships. The meta-Hi-C networks are available at https://labshare.cshl.edu/shares/gillislab/resource/HiC/.",
+                "authors": [
+                    {
+                        "name": "Fox N."
+                    },
+                    {
+                        "name": "Gillis J."
+                    },
+                    {
+                        "name": "Lohia R."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Genome Biology",
+                "title": "A global high-density chromatin interaction network reveals functional long-range and trans-chromosomal relationships"
+            },
+            "pmcid": "PMC9647974",
+            "pmid": "36352464"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "Chromosome conformation capture",
+            "uri": "http://edamontology.org/topic_3940"
+        },
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/hi-lasso/hi-lasso.biotools.json b/data/hi-lasso/hi-lasso.biotools.json
new file mode 100644
index 0000000000000..732345f8873e4
--- /dev/null
+++ b/data/hi-lasso/hi-lasso.biotools.json
@@ -0,0 +1,135 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-22T02:43:08.674587Z",
+    "biotoolsCURIE": "biotools:hi-lasso",
+    "biotoolsID": "hi-lasso",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mingon.kang@unlv.edu",
+            "name": "Mingon Kang",
+            "orcidid": "http://orcid.org/0000-0002-9565-9523",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "youngsoonkim@gnu.ac.kr",
+            "name": "Youngsoon Kim",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jongkwon Jo"
+        },
+        {
+            "name": "Joongyang Park"
+        },
+        {
+            "name": "Seungha Jung"
+        }
+    ],
+    "description": "High-performance python and apache spark packages for feature selection with high-dimensional data.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://hi-lasso.readthedocs.io/en/latest/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature selection",
+                    "uri": "http://edamontology.org/operation_3936"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/datax-lab/Hi-LASSO",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-22T02:43:08.677084Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://pypi.org/project/Hi-LASSO-spark"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://pypi.org/project/hi-lasso"
+        }
+    ],
+    "name": "Hi-LASSO",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/journal.pone.0278570",
+            "metadata": {
+                "abstract": "Copyright: © 2022 Jo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.High-dimensional LASSO (Hi-LASSO) is a powerful feature selection tool for high-dimensional data. Our previous study showed that Hi-LASSO outperformed the other state-of-the-art LASSO methods. However, the substantial cost of bootstrapping and the lack of experiments for a parametric statistical test for feature selection have impeded to apply Hi-LASSO for practical applications. In this paper, the Python package and its Spark library are efficiently designed in a parallel manner for practice with real-world problems, as well as providing the capability of the parametric statistical tests for feature selection on high-dimensional data. We demonstrate Hi-LASSO's outperformance with various intensive experiments in a practical manner. Hi-LASSO will be efficiently and easily performed by using the packages for feature selection. Hi-LASSO packages are publicly available at https://github.com/dataxlab/Hi-LASSO under the MIT license. The packages can be easily installed by Python PIP, and additional documentation is available at https://pypi.org/project/hi-lasso and https://pypi.org/project/Hi-LASSO-spark.",
+                "authors": [
+                    {
+                        "name": "Jo J."
+                    },
+                    {
+                        "name": "Jung S."
+                    },
+                    {
+                        "name": "Kang M."
+                    },
+                    {
+                        "name": "Kim Y."
+                    },
+                    {
+                        "name": "Park J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "Hi-LASSO: High-performance python and apache spark packages for feature selection with high-dimensional data"
+            },
+            "pmcid": "PMC9714948",
+            "pmid": "36455001"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Simulation experiment",
+            "uri": "http://edamontology.org/topic_3524"
+        }
+    ]
+}
diff --git a/data/hichipdb/hichipdb.biotools.json b/data/hichipdb/hichipdb.biotools.json
new file mode 100644
index 0000000000000..a2d1553a9c6f0
--- /dev/null
+++ b/data/hichipdb/hichipdb.biotools.json
@@ -0,0 +1,90 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T01:16:53.682048Z",
+    "biotoolsCURIE": "biotools:hichipdb",
+    "biotoolsID": "hichipdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ruijiang@tsinghua.edu.cn",
+            "name": "Rui Jiang",
+            "orcidid": "https://orcid.org/0000-0002-7533-3753",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "whwong@stanford.edu",
+            "name": "Wing Hung Wong",
+            "orcidid": "https://orcid.org/0000-0001-7466-2339",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Qiao Liu"
+        },
+        {
+            "name": "Wanwen Zeng"
+        }
+    ],
+    "description": "A comprehensive database of HiChIP regulatory interactions.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "SNP annotation",
+                    "uri": "http://edamontology.org/operation_3661"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://health.tsinghua.edu.cn/hichipdb/",
+    "lastUpdate": "2022-12-31T01:16:53.684605Z",
+    "name": "HiChIPdb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC859",
+            "pmid": "36215037"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-on-chip",
+            "uri": "http://edamontology.org/topic_3179"
+        },
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/hifens/hifens.biotools.json b/data/hifens/hifens.biotools.json
new file mode 100644
index 0000000000000..94b8c8d0241ff
--- /dev/null
+++ b/data/hifens/hifens.biotools.json
@@ -0,0 +1,91 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T01:11:51.277187Z",
+    "biotoolsCURIE": "biotools:hifens",
+    "biotoolsID": "hifens",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mistelit@mail.nih.gov",
+            "name": "Tom Misteli",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Asaf Shilo"
+        },
+        {
+            "name": "Gianluca Pegoraro"
+        }
+    ],
+    "description": "High-throughput FISH detection of endogenous pre-mRNA splicing isoforms.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Alternative splicing prediction",
+                    "uri": "http://edamontology.org/operation_0264"
+                },
+                {
+                    "term": "Exonic splicing enhancer prediction",
+                    "uri": "http://edamontology.org/operation_0446"
+                },
+                {
+                    "term": "Splice site prediction",
+                    "uri": "http://edamontology.org/operation_0433"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/CBIIT/mistelilab-hifens",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2022-12-31T01:11:51.280202Z",
+    "license": "GPL-3.0",
+    "name": "HiFENS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC869",
+            "pmid": "36243969"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Probes and primers",
+            "uri": "http://edamontology.org/topic_0632"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        }
+    ]
+}
diff --git a/data/hifiasm-meta/hifiasm-meta.biotools.json b/data/hifiasm-meta/hifiasm-meta.biotools.json
new file mode 100644
index 0000000000000..2108611662f54
--- /dev/null
+++ b/data/hifiasm-meta/hifiasm-meta.biotools.json
@@ -0,0 +1,41 @@
+{
+    "additionDate": "2023-01-30T14:05:47.447589Z",
+    "biotoolsCURIE": "biotools:hifiasm-meta",
+    "biotoolsID": "hifiasm-meta",
+    "description": "Hifiasm_meta - de novo metagenome assembler, based on hifiasm, a haplotype-resolved de novo assembler for PacBio Hifi reads.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/xfengnefx/hifiasm-meta",
+    "language": [
+        "C",
+        "C++"
+    ],
+    "lastUpdate": "2023-01-30T14:05:47.450198Z",
+    "license": "MIT",
+    "name": "Hifiasm-meta",
+    "owner": "alexcorm",
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/hignn/hignn.biotools.json b/data/hignn/hignn.biotools.json
new file mode 100644
index 0000000000000..e0f134275e8a4
--- /dev/null
+++ b/data/hignn/hignn.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-24T23:37:33.200075Z",
+    "biotoolsCURIE": "biotools:hignn",
+    "biotoolsID": "hignn",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Duancheng Zhao"
+        },
+        {
+            "name": "Jianrong Xu"
+        },
+        {
+            "name": "Weimin Zhu"
+        },
+        {
+            "name": "Yi Zhang"
+        },
+        {
+            "name": "Ling Wang",
+            "orcidid": "https://orcid.org/0000-0001-5116-7749"
+        }
+    ],
+    "description": "A Hierarchical Informative Graph Neural Network for Molecular Property Prediction Equipped with Feature-Wise Attention.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/idruglab/hignn",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-24T23:37:33.202673Z",
+    "license": "MIT",
+    "name": "HiGNN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCIM.2C01099",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society.Elucidating and accurately predicting the druggability and bioactivities of molecules plays a pivotal role in drug design and discovery and remains an open challenge. Recently, graph neural networks (GNNs) have made remarkable advancements in graph-based molecular property prediction. However, current graph-based deep learning methods neglect the hierarchical information of molecules and the relationships between feature channels. In this study, we propose a well-designed hierarchical informative graph neural network (termed HiGNN) framework for predicting molecular property by utilizing a corepresentation learning of molecular graphs and chemically synthesizable breaking of retrosynthetically interesting chemical substructure (BRICS) fragments. Furthermore, a plug-and-play feature-wise attention block is first designed in HiGNN architecture to adaptively recalibrate atomic features after the message passing phase. Extensive experiments demonstrate that HiGNN achieves state-of-the-art predictive performance on many challenging drug discovery-associated benchmark data sets. In addition, we devise a molecule-fragment similarity mechanism to comprehensively investigate the interpretability of the HiGNN model at the subgraph level, indicating that HiGNN as a powerful deep learning tool can help chemists and pharmacists identify the key components of molecules for designing better molecules with desired properties or functions. The source code is publicly available at https://github.com/idruglab/hignn.",
+                "authors": [
+                    {
+                        "name": "Wang L."
+                    },
+                    {
+                        "name": "Xu J."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zhao D."
+                    },
+                    {
+                        "name": "Zhu W."
+                    }
+                ],
+                "date": "2023-01-09T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "HiGNN: A Hierarchical Informative Graph Neural Network for Molecular Property Prediction Equipped with Feature-Wise Attention"
+            },
+            "pmid": "36519623"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        }
+    ]
+}
diff --git a/data/hipathia-gemomics/hipathia-gemomics.biotools.json b/data/hipathia-gemomics/hipathia-gemomics.biotools.json
index f4279e7c1ca68..eeaf6add9abc6 100644
--- a/data/hipathia-gemomics/hipathia-gemomics.biotools.json
+++ b/data/hipathia-gemomics/hipathia-gemomics.biotools.json
@@ -37,8 +37,8 @@
         }
     ],
     "homepage": "http://hipathia.babelomics.org",
-    "lastUpdate": "2022-03-14T12:49:13.937302Z",
-    "name": "Hipathia-gemomics",
+    "lastUpdate": "2023-02-17T10:51:17.316704Z",
+    "name": "Hipathia-genomics",
     "owner": "Niclaskn",
     "publication": [
         {
@@ -68,7 +68,7 @@
                         "name": "Rian K."
                     }
                 ],
-                "citationCount": 6,
+                "citationCount": 11,
                 "date": "2019-12-01T00:00:00Z",
                 "journal": "Scientific Reports",
                 "title": "Using mechanistic models for the clinical interpretation of complex genomic variation"
diff --git a/data/histofl/histofl.biotools.json b/data/histofl/histofl.biotools.json
new file mode 100644
index 0000000000000..c18bd0b60a1c5
--- /dev/null
+++ b/data/histofl/histofl.biotools.json
@@ -0,0 +1,127 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T00:28:09.591578Z",
+    "biotoolsCURIE": "biotools:histofl",
+    "biotoolsID": "histofl",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "faisalmahmood@bwh.harvard.edu",
+            "name": "Faisal Mahmood",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ming Y. Lu"
+        },
+        {
+            "name": "Richard J. Chen"
+        },
+        {
+            "name": "Tiffany Y. Chen"
+        }
+    ],
+    "description": "Federated learning for computational pathology on gigapixel whole slide images.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/mahmoodlab/HistoFL",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-27T00:28:09.594205Z",
+    "license": "GPL-3.0",
+    "name": "HistoFL",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.MEDIA.2021.102298",
+            "metadata": {
+                "abstract": "© 2021 The Author(s)Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morphological phenotypes to predicting non human-identifiable features from histology such as molecular alterations. However, the development of robust, adaptable and accurate deep learning-based models often rely on the collection and time-costly curation large high-quality annotated training data that should ideally come from diverse sources and patient populations to cater for the heterogeneity that exists in such datasets. Multi-centric and collaborative integration of medical data across multiple institutions can naturally help overcome this challenge and boost the model performance but is limited by privacy concerns among other difficulties that may arise in the complex data sharing process as models scale towards using hundreds of thousands of gigapixel whole slide images. In this paper, we introduce privacy-preserving federated learning for gigapixel whole slide images in computational pathology using weakly-supervised attention multiple instance learning and differential privacy. We evaluated our approach on two different diagnostic problems using thousands of histology whole slide images with only slide-level labels. Additionally, we present a weakly-supervised learning framework for survival prediction and patient stratification from whole slide images and demonstrate its effectiveness in a federated setting. Our results show that using federated learning, we can effectively develop accurate weakly-supervised deep learning models from distributed data silos without direct data sharing and its associated complexities, while also preserving differential privacy using randomized noise generation. We also make available an easy-to-use federated learning for computational pathology software package: http://github.com/mahmoodlab/HistoFL.",
+                "authors": [
+                    {
+                        "name": "Chen R.J."
+                    },
+                    {
+                        "name": "Chen T.Y."
+                    },
+                    {
+                        "name": "Kong D."
+                    },
+                    {
+                        "name": "Lipkova J."
+                    },
+                    {
+                        "name": "Lu M.Y."
+                    },
+                    {
+                        "name": "Mahmood F."
+                    },
+                    {
+                        "name": "Singh R."
+                    },
+                    {
+                        "name": "Williamson D.F.K."
+                    }
+                ],
+                "citationCount": 26,
+                "date": "2022-02-01T00:00:00Z",
+                "journal": "Medical Image Analysis",
+                "title": "Federated learning for computational pathology on gigapixel whole slide images"
+            },
+            "pmcid": "PMC9340569",
+            "pmid": "34911013"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Data security",
+            "uri": "http://edamontology.org/topic_3263"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/hn-ppisp/hn-ppisp.biotools.json b/data/hn-ppisp/hn-ppisp.biotools.json
new file mode 100644
index 0000000000000..bfde6a9ee8a32
--- /dev/null
+++ b/data/hn-ppisp/hn-ppisp.biotools.json
@@ -0,0 +1,75 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T15:00:48.743735Z",
+    "biotoolsCURIE": "biotools:hn-ppisp",
+    "biotoolsID": "hn-ppisp",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "pubin@hnu.edu.cn",
+            "name": "Bin Pu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A hybrid network based on MLP-Mixer for protein-protein interaction site prediction.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein feature detection",
+                    "uri": "http://edamontology.org/operation_3092"
+                },
+                {
+                    "term": "Protein interaction network prediction",
+                    "uri": "http://edamontology.org/operation_3094"
+                },
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ylxu05/HN-PPISP",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T15:00:48.746180Z",
+    "license": "Not licensed",
+    "name": "HN-PPISP",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC480",
+            "pmid": "36403092"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein interaction experiment",
+            "uri": "http://edamontology.org/topic_3957"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein structural motifs and surfaces",
+            "uri": "http://edamontology.org/topic_0166"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/hnc-predictor/hnc-predictor.biotools.json b/data/hnc-predictor/hnc-predictor.biotools.json
new file mode 100644
index 0000000000000..9f560fd0c6360
--- /dev/null
+++ b/data/hnc-predictor/hnc-predictor.biotools.json
@@ -0,0 +1,105 @@
+{
+    "additionDate": "2023-01-28T15:04:30.257808Z",
+    "biotoolsCURIE": "biotools:hnc-predictor",
+    "biotoolsID": "hnc-predictor",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Clifton D. Fuller"
+        }
+    ],
+    "description": "Development, international multi-institutional validation, and web implementation of clinic-ready model-based risk stratification for head and neck cancer', 'clinic-ready'",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://uic-evl.github.io/hnc-predictor/",
+    "lastUpdate": "2023-01-28T15:04:30.260262Z",
+    "license": "Not licensed",
+    "name": "HNC-PREDICTOR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.EJCA.2022.10.011",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsBackground: Personalised radiotherapy can improve treatment outcomes of patients with head and neck cancer (HNC), where currently a ‘one-dose-fits-all’ approach is the standard. The aim was to establish individualised outcome prediction based on multi-institutional international ‘big-data’ to facilitate risk-based stratification of patients with HNC. Methods: The data of 4611 HNC radiotherapy patients from three academic cancer centres were split into four cohorts: a training (n = 2241), independent test (n = 786), and external validation cohorts 1 (n = 1087) and 2 (n = 497). Tumour- and patient-related clinical variables were considered in a machine learning pipeline to predict overall survival (primary end-point) and local and regional tumour control (secondary end-points); serially, imaging features were considered for optional model improvement. Finally, patients were stratified into high-, intermediate-, and low-risk groups. Results: Performance score, AJCC8th stage, pack-years, and Age were identified as predictors for overall survival, demonstrating good performance in both the training cohort (c-index = 0.72 [95% CI, 0.66–0.77]) and in all three validation cohorts (c-indices: 0.76 [0.69–0.83], 0.73 [0.68–0.77], and 0.75 [0.68–0.80]). Excellent stratification of patients with HNC into high, intermediate, and low mortality risk was achieved; with 5-year overall survival rates of 17–46% for the high-risk group compared to 92–98% for the low-risk group. The addition of morphological image feature further improved the performance (c-index = 0.73 [0.64–0.81]). These models are integrated in a clinic-ready interactive web interface: https://uic-evl.github.io/hnc-predictor/ Conclusions: Robust model-based prediction was able to stratify patients with HNC in distinct high, intermediate, and low mortality risk groups. This can effectively be capitalised for personalised radiotherapy, e.g., for tumour radiation dose escalation/de-escalation.",
+                "authors": [
+                    {
+                        "name": "Ahmed S."
+                    },
+                    {
+                        "name": "Fuller C.D."
+                    },
+                    {
+                        "name": "Garden A.S."
+                    },
+                    {
+                        "name": "Gunn B."
+                    },
+                    {
+                        "name": "Hope A.J."
+                    },
+                    {
+                        "name": "Langendijk J.A."
+                    },
+                    {
+                        "name": "Marai G.E."
+                    },
+                    {
+                        "name": "Mohamed A.S."
+                    },
+                    {
+                        "name": "Moreno A."
+                    },
+                    {
+                        "name": "Nipu N."
+                    },
+                    {
+                        "name": "Sijtsema N.M."
+                    },
+                    {
+                        "name": "Wahid K."
+                    },
+                    {
+                        "name": "van Dijk L.V."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "European Journal of Cancer",
+                "title": "Head and neck cancer predictive risk estimator to determine control and therapeutic outcomes of radiotherapy (HNC-PREDICTOR): development, international multi-institutional validation, and web implementation of clinic-ready model-based risk stratification for head and neck cancer"
+            },
+            "pmid": "36442460"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/honto/honto.biotools.json b/data/honto/honto.biotools.json
new file mode 100644
index 0000000000000..fa6b0bd2e0d90
--- /dev/null
+++ b/data/honto/honto.biotools.json
@@ -0,0 +1,91 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T15:07:20.645438Z",
+    "biotoolsCURIE": "biotools:honto",
+    "biotoolsID": "honto",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "paolo.franciosa@uniroma1.it",
+            "name": "Paolo Giulio Franciosa",
+            "orcidid": "https://orcid.org/0000-0002-5464-4069",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A tool designed for assessing and measuring homophily in networks whose nodes have categorical attributes, namely when the nodes of networks come partitioned into classes.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/cumbof/honto",
+    "language": [
+        "C",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T15:07:20.648215Z",
+    "license": "MIT",
+    "name": "honto",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC763",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: It has been observed in different kinds of networks, such as social or biological ones, a typical behavior inspired by the general principle 'similarity breeds connections'. These networks are defined as homophilic as nodes belonging to the same class preferentially interact with each other. In this work, we present HONTO (HOmophily Network TOol), a user-friendly open-source Python3 package designed to evaluate and analyze homophily in complex networks. The tool takes in input from the network along with a partition of its nodes into classes and yields a matrix whose entries are the homophily/heterophily z-score values. To complement the analysis, the tool also provides z-score values of nodes that do not interact with any other node of the same class. Homophily/heterophily z-scores values are presented as a heatmap allowing a visual at-a-glance interpretation of results. AVAILABILITY AND IMPLEMENTATION: Tool's source code is available at https://github.com/cumbof/honto under the MIT license, installable as a package from PyPI (pip install honto) and conda-forge (conda install -c conda-forge honto), and has a wrapper for the Galaxy platform available on the official Galaxy ToolShed (Blankenberg et al., 2014) at https://toolshed.g2.bx.psu.edu/view/fabio/honto.",
+                "authors": [
+                    {
+                        "name": "Apollonio N."
+                    },
+                    {
+                        "name": "Blankenberg D."
+                    },
+                    {
+                        "name": "Cumbo F."
+                    },
+                    {
+                        "name": "Franciosa P.G."
+                    },
+                    {
+                        "name": "Santoni D."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Evaluating homophily in networks via HONTO (HOmophily Network TOol): a case study of chromosomal interactions in human PPI networks"
+            },
+            "pmcid": "PMC9805585",
+            "pmid": "36440918"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        }
+    ]
+}
diff --git a/data/hpipred/hpipred.biotools.json b/data/hpipred/hpipred.biotools.json
new file mode 100644
index 0000000000000..9d6c61704379f
--- /dev/null
+++ b/data/hpipred/hpipred.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-24T23:28:28.899417Z",
+    "biotoolsCURIE": "biotools:hpipred",
+    "biotoolsID": "hpipred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "marc.torrent@uab.cat",
+            "name": "Marc Torrent Burgas",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Javier Macho Rendón"
+        },
+        {
+            "name": "Rocio Rebollido-Ríos"
+        }
+    ],
+    "description": "Host-pathogen interactome prediction with phenotypic scoring.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                },
+                {
+                    "term": "Subcellular localisation prediction",
+                    "uri": "http://edamontology.org/operation_2489"
+                },
+                {
+                    "term": "Virulence prediction",
+                    "uri": "http://edamontology.org/operation_3461"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/SysBioUAB/hpi_predictor",
+    "language": [
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-24T23:28:28.901955Z",
+    "license": "Not licensed",
+    "name": "HPIPred",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.11.026",
+            "metadata": {
+                "abstract": "© 2022 The Author(s)Protein-protein interactions (PPIs) are involved in most cellular processes. Unfortunately, current knowledge of host-pathogen interactomes is still very limited. Experimental methods used to detect PPIs have several limitations, including increasing complexity and economic cost in large-scale screenings. Hence, computational methods are commonly used to support experimental data, although they generally suffer from high false-positive rates. To address this issue, we have created HPIPred, a host-pathogen PPI prediction tool based on numerical encoding of physicochemical properties. Unlike other available methods, HPIPred integrates phenotypic data to prioritize biologically meaningful results. We used HPIPred to screen the entire Homo sapiens and Pseudomonas aeruginosa PAO1 proteomes to generate a host-pathogen interactome with 763 interactions displaying a highly connected network topology. Our predictive model can be used to prioritize protein–protein interactions as potential targets for antibacterial drug development. Available at: https://github.com/SysBioUAB/hpi_predictor.",
+                "authors": [
+                    {
+                        "name": "Macho Rendon J."
+                    },
+                    {
+                        "name": "Rebollido-Rios R."
+                    },
+                    {
+                        "name": "Torrent Burgas M."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "HPIPred: Host–pathogen interactome prediction with phenotypic scoring"
+            },
+            "pmcid": "PMC9718936",
+            "pmid": "36514317"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Protein interaction experiment",
+            "uri": "http://edamontology.org/topic_3957"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/hproteome-bsite/hproteome-bsite.biotools.json b/data/hproteome-bsite/hproteome-bsite.biotools.json
new file mode 100644
index 0000000000000..6ad80f5e9ffb2
--- /dev/null
+++ b/data/hproteome-bsite/hproteome-bsite.biotools.json
@@ -0,0 +1,91 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T01:06:21.382484Z",
+    "biotoolsCURIE": "biotools:hproteome-bsite",
+    "biotoolsID": "hproteome-bsite",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chaok@snu.ac.kr",
+            "name": "Chaok Seok",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jiho Sim"
+        },
+        {
+            "name": "Sohee Kwon"
+        }
+    ],
+    "description": "An online database for ligand binding sites in human proteome.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Binding site prediction",
+                    "uri": "http://edamontology.org/operation_2575"
+                },
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                },
+                {
+                    "term": "Protein-ligand docking",
+                    "uri": "http://edamontology.org/operation_0482"
+                },
+                {
+                    "term": "Protein-protein docking",
+                    "uri": "http://edamontology.org/operation_3899"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://galaxy.seoklab.org/hproteome-bsite/database/domains/39056",
+    "lastUpdate": "2022-12-31T01:06:21.386008Z",
+    "name": "HProteome-BSite",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC873",
+            "pmid": "36243970"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
+}
diff --git a/data/hra/hra.biotools.json b/data/hra/hra.biotools.json
new file mode 100644
index 0000000000000..a397b3a8ab7e3
--- /dev/null
+++ b/data/hra/hra.biotools.json
@@ -0,0 +1,165 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-24T23:21:24.988187Z",
+    "biotoolsCURIE": "biotools:hra",
+    "biotoolsID": "hra",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "abueckle@iu.edu",
+            "name": "Andreas Bueckle",
+            "orcidid": "https://orcid.org/0000-0002-8977-498X",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "katy@indiana.edu",
+            "name": "Katy Börner",
+            "orcidid": "https://orcid.org/0000-0002-3321-6137",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Griffin M. Weber"
+        },
+        {
+            "name": "N. Heath Patterson",
+            "orcidid": "http://orcid.org/0000-0002-0064-1583"
+        }
+    ],
+    "description": "Tissue registration and exploration user interfaces in support of a human reference atlas.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cns-iu.github.io/HRA-supporting-information/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-24T23:21:24.990741Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://hubmapconsortium.github.io/hra-data-dashboard"
+        },
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://portal.hubmapconsortium.org/ccf-eui"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/cns-iu/HRA-supporting-information"
+        }
+    ],
+    "name": "HRA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S42003-022-03644-X",
+            "metadata": {
+                "abstract": "© 2022, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.Seventeen international consortia are collaborating on a human reference atlas (HRA), a comprehensive, high-resolution, three-dimensional atlas of all the cells in the healthy human body. Laboratories around the world are collecting tissue specimens from donors varying in sex, age, ethnicity, and body mass index. However, harmonizing tissue data across 25 organs and more than 15 bulk and spatial single-cell assay types poses challenges. Here, we present software tools and user interfaces developed to spatially and semantically annotate (“register”) and explore the tissue data and the evolving HRA. A key part of these tools is a common coordinate framework, providing standard terminologies and data structures for describing specimen, biological structure, and spatial data linked to existing ontologies. As of April 22, 2022, the “registration” user interface has been used to harmonize and publish data on 5,909 tissue blocks collected by the Human Biomolecular Atlas Program (HuBMAP), the Stimulating Peripheral Activity to Relieve Conditions program (SPARC), the Human Cell Atlas (HCA), the Kidney Precision Medicine Project (KPMP), and the Genotype Tissue Expression project (GTEx). Further, 5,856 tissue sections were derived from 506 HuBMAP tissue blocks. The second “exploration” user interface enables consortia to evaluate data quality, explore tissue data spatially within the context of the HRA, and guide data acquisition. A companion website is at https://cns-iu.github.io/HRA-supporting-information/.",
+                "authors": [
+                    {
+                        "name": "Borner K."
+                    },
+                    {
+                        "name": "Browne K.M."
+                    },
+                    {
+                        "name": "Bueckle A."
+                    },
+                    {
+                        "name": "Cross L.E."
+                    },
+                    {
+                        "name": "Herr B.W."
+                    },
+                    {
+                        "name": "Jain S."
+                    },
+                    {
+                        "name": "Jorgensen M.L."
+                    },
+                    {
+                        "name": "Ju Y."
+                    },
+                    {
+                        "name": "Patterson N.H."
+                    },
+                    {
+                        "name": "Quardokus E.M."
+                    },
+                    {
+                        "name": "Record E.G."
+                    },
+                    {
+                        "name": "Silverstein J.C."
+                    },
+                    {
+                        "name": "Spraggins J.M."
+                    },
+                    {
+                        "name": "Wasserfall C.H."
+                    },
+                    {
+                        "name": "Weber G.M."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Communications Biology",
+                "title": "Tissue registration and exploration user interfaces in support of a human reference atlas"
+            },
+            "pmcid": "PMC9747802",
+            "pmid": "36513738"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        }
+    ]
+}
diff --git a/data/hsdatabase/hsdatabase.biotools.json b/data/hsdatabase/hsdatabase.biotools.json
new file mode 100644
index 0000000000000..02c4107db9efb
--- /dev/null
+++ b/data/hsdatabase/hsdatabase.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T01:01:49.080128Z",
+    "biotoolsCURIE": "biotools:hsdatabase",
+    "biotoolsID": "hsdatabase",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dsmit242@uwo.ca",
+            "name": "David Roy Smith",
+            "orcidid": "https://orcid.org/0000-0001-9560-5210",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xi.zhang@dal.ca",
+            "name": "Xi Zhang",
+            "orcidid": "https://orcid.org/0000-0003-2821-9066",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Yining Hu"
+        }
+    ],
+    "description": "Database of highly similar duplicate genes from plants, animals, and algae.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Genome visualisation",
+                    "uri": "http://edamontology.org/operation_3208"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://hsdfinder.com/database/",
+    "lastUpdate": "2022-12-31T01:01:49.082939Z",
+    "name": "HSDatabase",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/DATABASE/BAAC086",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press.Gene duplication is an important evolutionary mechanism capable of providing new genetic material, which in some instances can help organisms adapt to various environmental conditions. Recent studies, for example, have indicated that highly similar duplicate genes (HSDs) are aiding adaptation to extreme conditions via gene dosage. However, for most eukaryotic genomes HSDs remain uncharacterized, partly because they can be hard to identify and categorize efficiently and effectively. Here, we collected and curated HSDs in nuclear genomes from various model animals, land plants and algae and indexed them in an online, open-access sequence repository called HSDatabase. Currently, this database contains 117 864 curated HSDs from 40 distinct genomes; it includes statistics on the total number of HSDs per genome as well as individual HSD copy numbers/lengths and provides sequence alignments of the duplicate gene copies. HSDatabase also allows users to download sequences of gene copies, access genome browsers, and link out to other databases, such as Pfam and Kyoto Encyclopedia of Genes and Genomes. What is more, a built-in Basic Local Alignment Search Tool option is available to conveniently explore potential homologous sequences of interest within and across species. HSDatabase has a user-friendly interface and provides easy access to the source data. It can be used on its own for comparative analyses of gene duplicates or in conjunction with HSDFinder, a newly developed bioinformatics tool for identifying, annotating, categorizing and visualizing HSDs. Database URL: http://hsdfinder.com/database/",
+                "authors": [
+                    {
+                        "name": "Hu Y."
+                    },
+                    {
+                        "name": "Smith D.R."
+                    },
+                    {
+                        "name": "Zhang X."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Database",
+                "title": "HSDatabase-A database of highly similar duplicate genes from plants, animals, and algae"
+            },
+            "pmcid": "PMC9547538",
+            "pmid": "36208223"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Carbohydrates",
+            "uri": "http://edamontology.org/topic_0152"
+        },
+        {
+            "term": "Data submission, annotation and curation",
+            "uri": "http://edamontology.org/topic_0219"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/hsdfinder/hsdfinder.biotools.json b/data/hsdfinder/hsdfinder.biotools.json
new file mode 100644
index 0000000000000..82365d74dd42c
--- /dev/null
+++ b/data/hsdfinder/hsdfinder.biotools.json
@@ -0,0 +1,86 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T00:55:58.680941Z",
+    "biotoolsCURIE": "biotools:hsdfinder",
+    "biotoolsID": "hsdfinder",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dsmit242@uwo.ca",
+            "name": "David Roy Smith",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xzha25@uwo.ca",
+            "name": "Xi Zhang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Yining Hu"
+        }
+    ],
+    "description": "An integrated tool for predicting highly similar duplicates (HSDs) in eukaryotic genomes.\nHSDFinder aims to become a useful platform for the identification and analysis of HSDs in the eukaryotic genomes, which deepen our insights into the gene duplication mechanisms driving the genome adaptation.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Duplication detection",
+                    "uri": "http://edamontology.org/operation_3963"
+                },
+                {
+                    "term": "Genome visualisation",
+                    "uri": "http://edamontology.org/operation_3208"
+                },
+                {
+                    "term": "Heat map generation",
+                    "uri": "http://edamontology.org/operation_0531"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://hsdfinder.com",
+    "lastUpdate": "2022-12-31T00:56:45.595468Z",
+    "name": "HSDFinder",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FBINF.2021.803176",
+            "pmcid": "PMC9580922",
+            "pmid": "36303740"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        }
+    ]
+}
diff --git a/data/hsnet/hsnet.biotools.json b/data/hsnet/hsnet.biotools.json
new file mode 100644
index 0000000000000..72043bb3f7e46
--- /dev/null
+++ b/data/hsnet/hsnet.biotools.json
@@ -0,0 +1,87 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T00:51:45.800347Z",
+    "biotoolsCURIE": "biotools:hsnet",
+    "biotoolsID": "hsnet",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "b.sham@auckland.ac.nz",
+            "name": "Chiu-Wing Sham",
+            "orcidid": "https://orcid.org/0000-0001-7007-6746"
+        },
+        {
+            "name": "Chong Fu"
+        },
+        {
+            "name": "Wenchao Zhang"
+        },
+        {
+            "name": "Yu Zheng",
+            "orcidid": "https://orcid.org/0000-0002-5816-4126"
+        }
+    ],
+    "description": "A hybrid semantic network for polyp segmentation.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/baiboat/HSNet",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-31T00:51:45.804041Z",
+    "license": "Not licensed",
+    "name": "HSNet",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106173",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdAutomatic polyp segmentation can help physicians to effectively locate polyps (a.k.a. region of interests) in clinical practice, in the way of screening colonoscopy images assisted by neural networks (NN). However, two significant bottlenecks hinder its effectiveness, disappointing physicians’ expectations. (1) Changeable polyps in different scaling, orientation, and illumination, bring difficulty in accurate segmentation. (2) Current works building on a dominant decoder–encoder network tend to overlook appearance details (e.g., textures) for a tiny polyp, degrading the accuracy to differentiate polyps. For alleviating the bottlenecks, we investigate a hybrid semantic network (HSNet) that adopts both advantages of Transformer and convolutional neural networks (CNN), aiming at improving polyp segmentation. Our HSNet contains a cross-semantic attention module (CSA), a hybrid semantic complementary module (HSC), and a multi-scale prediction module (MSP). Unlike previous works on segmenting polyps, we newly insert the CSA module, which can fill the gap between low-level and high-level features via an interactive mechanism that exchanges two types of semantics from different NN attentions. By a dual-branch structure of Transformer and CNN, we newly design an HSC module, for capturing both long-range dependencies and local details of appearance. Besides, the MSP module can learn weights for fusing stage-level prediction masks of a decoder. Experimentally, we compared our work with 10 state-of-the-art works, including both recent and classical works, showing improved accuracy (via 7 evaluative metrics) over 5 benchmark datasets, e.g., it achieves 0.926/0.877 mDic/mIoU on Kvasir-SEG, 0.948/0.905 mDic/mIoU on ClinicDB, 0.810/0.735 mDic/mIoU on ColonDB, 0.808/0.74 mDic/mIoU on ETIS, and 0.903/0.839 mDic/mIoU on Endoscene. The proposed model is available at (https://github.com/baiboat/HSNet).",
+                "authors": [
+                    {
+                        "name": "Fu C."
+                    },
+                    {
+                        "name": "Sham C.-W."
+                    },
+                    {
+                        "name": "Zhang F."
+                    },
+                    {
+                        "name": "Zhang W."
+                    },
+                    {
+                        "name": "Zhao Y."
+                    },
+                    {
+                        "name": "Zheng Y."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "HSNet: A hybrid semantic network for polyp segmentation"
+            },
+            "pmid": "36257278"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/htaadvar/htaadvar.biotools.json b/data/htaadvar/htaadvar.biotools.json
new file mode 100644
index 0000000000000..4fd92be5f2283
--- /dev/null
+++ b/data/htaadvar/htaadvar.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T00:42:15.233812Z",
+    "biotoolsCURIE": "biotools:htaadvar",
+    "biotoolsID": "htaadvar",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Guoyan Zhu"
+        },
+        {
+            "name": "Wei-Zhen Zhou"
+        },
+        {
+            "name": "Yujing Zhang"
+        }
+    ],
+    "description": "Aggregation and fully automated clinical interpretation of genetic variants in heritable thoracic aortic aneurysm and dissection.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene symbol",
+                        "uri": "http://edamontology.org/data_1026"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://htaadvar.fwgenetics.org",
+    "lastUpdate": "2022-12-31T00:42:15.236585Z",
+    "name": "HTAADVar",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.GIM.2022.08.024",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsPurpose: Early detection and pathogenicity interpretation of disease-associated variants are crucial but challenging in molecular diagnosis, especially for insidious and life-threatening diseases, such as heritable thoracic aortic aneurysm and dissection (HTAAD). In this study, we developed HTAADVar, an unbiased and fully automated system for the molecular diagnosis of HTAAD. Methods: We developed HTAADVar (http://htaadvar.fwgenetics.org) under the American College of Medical Genetics and Genomics/Association for Molecular Pathology framework, with optimizations based on disease- and gene-specific knowledge, expert panel recommendations, and variant observations. HTAADVar provides variant interpretation with a self-built database through the web server and the stand-alone programs. Results: We constructed an expert-reviewed database by integrating 4373 variants in HTAAD genes, with comprehensive metadata curated from 697 publications and an in-house study of 790 patients. We further developed an interpretation system to assess variants automatically. Notably, HTAADVar showed a multifold increase in performance compared with public tools, reaching a sensitivity of 92.64% and specificity of 70.83%. The molecular diagnostic yield of HTAADVar among 790 patients (42.03%) also matched the clinical data, independently demonstrating its good performance in clinical application. Conclusion: HTAADVar represents the first fully automated system for accurate variant interpretation for HTAAD. The framework of HTAADVar could also be generalized for the molecular diagnosis of other genetic diseases.",
+                "authors": [
+                    {
+                        "name": "Chen Q."
+                    },
+                    {
+                        "name": "Li W."
+                    },
+                    {
+                        "name": "Luo M."
+                    },
+                    {
+                        "name": "Shen H."
+                    },
+                    {
+                        "name": "Shu C."
+                    },
+                    {
+                        "name": "Yang H."
+                    },
+                    {
+                        "name": "Zeng Q."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zhou W.-Z."
+                    },
+                    {
+                        "name": "Zhou Z."
+                    },
+                    {
+                        "name": "Zhu G."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Genetics in Medicine",
+                "title": "HTAADVar: Aggregation and fully automated clinical interpretation of genetic variants in heritable thoracic aortic aneurysm and dissection"
+            },
+            "pmid": "36194209"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/htseq-clip/htseq-clip.biotools.json b/data/htseq-clip/htseq-clip.biotools.json
new file mode 100644
index 0000000000000..9b2b48a6465c6
--- /dev/null
+++ b/data/htseq-clip/htseq-clip.biotools.json
@@ -0,0 +1,116 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-28T15:11:28.535534Z",
+    "biotoolsCURIE": "biotools:htseq-clip",
+    "biotoolsID": "htseq-clip",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "schwarzl@embl.de",
+            "name": "Thomas Schwarzl",
+            "orcidid": "https://orcid.org/0000-0001-7697-7000",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "htseq-clip, a python package developed for preprocessing, extracting and summarizing crosslink site counts from i/eCLIP experimental data.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://htseq-clip.readthedocs.io/en/latest"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Filtering",
+                    "uri": "http://edamontology.org/operation_3695"
+                },
+                {
+                    "term": "RNA binding site prediction",
+                    "uri": "http://edamontology.org/operation_3902"
+                },
+                {
+                    "term": "RNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3901"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/EMBL-Hentze-group/htseq-clip",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-28T15:11:28.537995Z",
+    "license": "MIT",
+    "name": "htseq-clip",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC747",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: Transcriptome-wide detection of binding sites of RNA-binding proteins is achieved using Individual-nucleotide crosslinking and immunoprecipitation (iCLIP) and its derivative enhanced CLIP (eCLIP) sequencing methods. Here, we introduce htseq-clip, a python package developed for preprocessing, extracting and summarizing crosslink site counts from i/eCLIP experimental data. The package delivers crosslink site count matrices along with other metrics, which can be directly used for filtering and downstream analyses such as the identification of differential binding sites. AVAILABILITY AND IMPLEMENTATION: The Python package htseq-clip is available via pypi (python package index), bioconda and the Galaxy Tool Shed under the open source MIT License. The code is hosted at https://github.com/EMBL-Hentze-group/htseq-clip and documentation is available under https://htseq-clip.readthedocs.io/en/latest.",
+                "authors": [
+                    {
+                        "name": "Ashaf N."
+                    },
+                    {
+                        "name": "Fritz M."
+                    },
+                    {
+                        "name": "Hentze M.W."
+                    },
+                    {
+                        "name": "Huber W."
+                    },
+                    {
+                        "name": "Sahadevan S."
+                    },
+                    {
+                        "name": "Schwarzl T."
+                    },
+                    {
+                        "name": "Sekaran T."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "htseq-clip: a toolset for the preprocessing of eCLIP/iCLIP datasets"
+            },
+            "pmcid": "PMC9825771",
+            "pmid": "36394253"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Protein binding sites",
+            "uri": "http://edamontology.org/topic_3534"
+        },
+        {
+            "term": "RNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3794"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/htslib/htslib.biotools.json b/data/htslib/htslib.biotools.json
index aa2f3bc9a0124..ef500229d9acd 100644
--- a/data/htslib/htslib.biotools.json
+++ b/data/htslib/htslib.biotools.json
@@ -155,7 +155,7 @@
     "language": [
         "C"
     ],
-    "lastUpdate": "2022-08-18T14:22:47.096862Z",
+    "lastUpdate": "2023-02-21T14:51:50.878047Z",
     "license": "MIT",
     "link": [
         {
@@ -189,7 +189,7 @@
         {
             "doi": "10.1093/gigascience/giab007",
             "metadata": {
-                "abstract": "© The Author(s) 2021. Published by Oxford University Press GigaScience.BACKGROUND: Since the original publication of the VCF and SAM formats, an explosion of software tools have been created to process these data files. To facilitate this a library was produced out of the original SAMtools implementation, with a focus on performance and robustness. The file formats themselves have become international standards under the jurisdiction of the Global Alliance for Genomics and Health. FINDINGS: We present a software library for providing programmatic access to sequencing alignment and variant formats. It was born out of the widely used SAMtools and BCFtools applications. Considerable improvements have been made to the original code plus many new features including newer access protocols, the addition of the CRAM file format, better indexing and iterators, and better use of threading. CONCLUSION: Since the original Samtools release, performance has been considerably improved, with a BAM read-write loop running 5 times faster and BAM to SAM conversion 13 times faster (both using 16 threads, compared to Samtools 0.1.19). Widespread adoption has seen HTSlib downloaded >1 million times from GitHub and conda. The C library has been used directly by an estimated 900 GitHub projects and has been incorporated into Perl, Python, Rust, and R, significantly expanding the number of uses via other languages. HTSlib is open source and is freely available from htslib.org under MIT/BSD license.",
+                "abstract": "© 2021 The Author(s). Published by Oxford University Press GigaScience.Background: Since the original publication of the VCF and SAM formats, an explosion of software tools have been created to process these data files. To facilitate this a library was produced out of the original SAMtools implementation, with a focus on performance and robustness. The file formats themselves have become international standards under the jurisdiction of the Global Alliance for Genomics and Health. Findings: We present a software library for providing programmatic access to sequencing alignment and variant formats. It was born out of the widely used SAMtools and BCFtools applications. Considerable improvements have been made to the original code plus many new features including newer access protocols, the addition of the CRAM file format, better indexing and iterators, and better use of threading. Conclusion: Since the original Samtools release, performance has been considerably improved, with a BAM read-write loop running 5 times faster and BAM to SAM conversion 13 times faster (both using 16 threads, compared to Samtools 0.1.19). Widespread adoption has seen HTSlib downloaded >1 million times from GitHub and conda. The C library has been used directly by an estimated 900 GitHub projects and has been incorporated into Perl, Python, Rust, and R, significantly expanding the number of uses via other languages. HTSlib is open source and is freely available from htslib.org under MIT/BSD license.",
                 "authors": [
                     {
                         "name": "Bonfield J.K."
@@ -197,9 +197,6 @@
                     {
                         "name": "Danecek P."
                     },
-                    {
-                        "name": "Davies R.M."
-                    },
                     {
                         "name": "Keane T."
                     },
@@ -216,10 +213,10 @@
                         "name": "Whitwham A."
                     }
                 ],
-                "citationCount": 26,
-                "date": "2021-02-16T00:00:00Z",
+                "citationCount": 44,
+                "date": "2021-02-01T00:00:00Z",
                 "journal": "GigaScience",
-                "title": "HTSlib: C library for reading/writing high-throughput sequencing data"
+                "title": "HTSlib: C library for reading/writing high-Throughput sequencing data"
             },
             "note": "HTSlib: C library for reading/writing high-throughput sequencing data.",
             "pmcid": "PMC7931820",
@@ -262,6 +259,7 @@
         "1.15",
         "1.15.1",
         "1.16",
+        "1.17",
         "1.2",
         "1.2.1",
         "1.3",
diff --git a/data/husch/husch.biotools.json b/data/husch/husch.biotools.json
new file mode 100644
index 0000000000000..23cb18e7a641f
--- /dev/null
+++ b/data/husch/husch.biotools.json
@@ -0,0 +1,147 @@
+{
+    "additionDate": "2023-02-06T07:18:27.088837Z",
+    "biotoolsCURIE": "biotools:husch",
+    "biotoolsID": "husch",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "08chenfeiwang@tongji.edu.cn",
+            "name": "Chenfei Wang",
+            "orcidid": "https://orcid.org/0000-0001-7573-3768",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "litaiwen@scu.edu.cn",
+            "name": "Taiwen Li",
+            "orcidid": "https://orcid.org/0000-0001-7940-8196",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "http://husch.comp-genomics.org/#/documentation"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Expression data visualisation",
+                    "uri": "http://edamontology.org/operation_0571"
+                },
+                {
+                    "term": "Expression profile clustering",
+                    "uri": "http://edamontology.org/operation_0313"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://husch.comp-genomics.org",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-06T07:18:27.092298Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/wanglabtongji/HUSCH"
+        }
+    ],
+    "name": "HUSCH",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1001",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Understanding gene expression patterns across different human cell types is crucial for investigating mechanisms of cell type differentiation, disease occurrence and progression. The recent development of single-cell RNA-seq (scRNA-seq) technologies significantly boosted the characterization of cell type heterogeneities in different human tissues. However, the huge number of datasets in the public domain also posed challenges in data integration and reuse. We present Human Universal Single Cell Hub (HUSCH, http://husch.comp-genomics.org), an atlas-scale curated database that integrates single-cell transcriptomic profiles of nearly 3 million cells from 185 high-quality human scRNA-seq datasets from 45 different tissues. All the data in HUSCH were uniformly processed and annotated with a standard workflow. In the single dataset module, HUSCH provides interactive gene expression visualization, differentially expressed genes, functional analyses, transcription regulators and cell-cell interaction analyses for each cell type cluster. Besides, HUSCH integrated different datasets in the single tissue module and performs data integration, batch correction, and cell type harmonization. This allows a comprehensive visualization and analysis of gene expression within each tissue based on single-cell datasets from multiple sources and platforms. HUSCH is a flexible and comprehensive data portal that enables searching, visualizing, analyzing, and downloading single-cell gene expression for the human tissue atlas.",
+                "authors": [
+                    {
+                        "name": "Ding X."
+                    },
+                    {
+                        "name": "Dong X."
+                    },
+                    {
+                        "name": "Li T."
+                    },
+                    {
+                        "name": "Ren P."
+                    },
+                    {
+                        "name": "Shi X."
+                    },
+                    {
+                        "name": "Song J."
+                    },
+                    {
+                        "name": "Wang C."
+                    },
+                    {
+                        "name": "Yu Z."
+                    },
+                    {
+                        "name": "Zhang J."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses"
+            },
+            "pmcid": "PMC9825509",
+            "pmid": "36318258"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/hypacadd/hypacadd.biotools.json b/data/hypacadd/hypacadd.biotools.json
new file mode 100644
index 0000000000000..6663f9387cbaf
--- /dev/null
+++ b/data/hypacadd/hypacadd.biotools.json
@@ -0,0 +1,129 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-24T23:08:19.492631Z",
+    "biotoolsCURIE": "biotools:hypacadd",
+    "biotoolsID": "hypacadd",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hugo.lam@hypahub.com",
+            "name": "Hugo Y K Lam",
+            "orcidid": "https://orcid.org/0000-0002-0564-6105",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "mark@gersteinlab.org",
+            "name": "Mark B Gerstein",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Bayo Lau"
+        },
+        {
+            "name": "Prashant S Emani"
+        }
+    ],
+    "description": "Insights from incorporating quantum computing into drug design workflows.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular docking",
+                    "uri": "http://edamontology.org/operation_0478"
+                },
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.github.com/hypahub/hypacadd_notebook",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-24T23:08:19.495959Z",
+    "license": "CC-BY-NC-SA-4.0",
+    "name": "HypaCADD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC789",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. RESULTS: We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the coronavirus (SARS-CoV-2) protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. AVAILABILITY AND IMPLEMENTATION: Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Chapman J."
+                    },
+                    {
+                        "name": "Emani P.S."
+                    },
+                    {
+                        "name": "Gerstein M.B."
+                    },
+                    {
+                        "name": "Lam H.Y.K."
+                    },
+                    {
+                        "name": "Lam T."
+                    },
+                    {
+                        "name": "Lau B."
+                    },
+                    {
+                        "name": "Merrill P."
+                    },
+                    {
+                        "name": "Warrell J."
+                    },
+                    {
+                        "name": "Yao L."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Insights from incorporating quantum computing into drug design workflows"
+            },
+            "pmcid": "PMC9825754",
+            "pmid": "36477833"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Molecular dynamics",
+            "uri": "http://edamontology.org/topic_0176"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/hyperhmm/hyperhmm.biotools.json b/data/hyperhmm/hyperhmm.biotools.json
new file mode 100644
index 0000000000000..3d9729f1dded2
--- /dev/null
+++ b/data/hyperhmm/hyperhmm.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-24T23:04:00.996442Z",
+    "biotoolsCURIE": "biotools:hyperhmm",
+    "biotoolsID": "hyperhmm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "iain.johnston@uib.no",
+            "name": "Iain G Johnston",
+            "orcidid": "https://orcid.org/0000-0001-8559-3519",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Marcus T Moen"
+        }
+    ],
+    "description": "Efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Ancestral reconstruction",
+                    "uri": "http://edamontology.org/operation_3745"
+                },
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/StochasticBiology/hypercube-hmm",
+    "language": [
+        "C",
+        "C++",
+        "R"
+    ],
+    "lastUpdate": "2023-02-24T23:04:00.999611Z",
+    "license": "Not licensed",
+    "name": "HyperHMM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC803",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The evolution of bacterial drug resistance and other features in biology, the progression of cancer and other diseases and a wide range of broader questions can often be viewed as the sequential stochastic acquisition of binary traits (e.g. genetic changes, symptoms or characters). Using potentially noisy or incomplete data to learn the sequences by which such traits are acquired is a problem of general interest. The problem is complicated for large numbers of traits, which may, individually or synergistically, influence the probability of further acquisitions both positively and negatively. Hypercubic inference approaches, based on hidden Markov models on a hypercubic transition network, address these complications, but previous Bayesian instances can consume substantial time for converged results, limiting their practical use. RESULTS: Here, we introduce HyperHMM, an adapted Baum-Welch (expectation-maximization) algorithm for hypercubic inference with resampling to quantify uncertainty, and show that it allows orders-of-magnitude faster inference while making few practical sacrifices compared to previous hypercubic inference approaches. We show that HyperHMM allows any combination of traits to exert arbitrary positive or negative influence on the acquisition of other traits, relaxing a common limitation of only independent trait influences. We apply this approach to synthetic and biological datasets and discuss its more general application in learning evolutionary and progressive pathways. AVAILABILITY AND IMPLEMENTATION: Code for inference and visualization, and data for example cases, is freely available at https://github.com/StochasticBiology/hypercube-hmm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Johnston I.G."
+                    },
+                    {
+                        "name": "Moen M.T."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "HyperHMM: efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs"
+            },
+            "pmcid": "PMC9848056",
+            "pmid": "36511587"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/iamap-scm/iamap-scm.biotools.json b/data/iamap-scm/iamap-scm.biotools.json
new file mode 100644
index 0000000000000..d8fd3f6e6729d
--- /dev/null
+++ b/data/iamap-scm/iamap-scm.biotools.json
@@ -0,0 +1,102 @@
+{
+    "additionDate": "2023-02-06T07:23:04.525538Z",
+    "biotoolsCURIE": "biotools:iamap-scm",
+    "biotoolsID": "iamap-scm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "watshara.sho@mahidol.ac.th",
+            "name": "Watshara Shoombuatong",
+            "orcidid": "https://orcid.org/0000-0002-3394-8709",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "pramote.c@ku.th",
+            "name": "Pramote Chumnanpuen",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides.\n\nWelcome to the Home Page of iAMAP-SCM.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular docking",
+                    "uri": "http://edamontology.org/operation_0478"
+                },
+                {
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                },
+                {
+                    "term": "Protein structure validation",
+                    "uri": "http://edamontology.org/operation_0321"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://pmlabstack.pythonanywhere.com/iAMAP-SCM",
+    "lastUpdate": "2023-02-06T07:23:04.528164Z",
+    "license": "Other",
+    "name": "iAMAP-SCM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1021/ACSOMEGA.2C04465",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society. All rights reserved.Antimalarial peptides (AMAPs) varying in length, amino acid composition, charge, conformational structure, hydrophobicity, and amphipathicity reflect their diversity in antimalarial mechanisms. Due to the worldwide major health problem concerning antimicrobial resistance, these peptides possess great therapeutic value owing to their low incidences of drug resistance as compared to conventional antibiotics. Although well-known experimental methods are able to precisely determine the antimalarial activity of peptides, these methods are still time-consuming and costly. Thus, machine learning (ML)-based methods that are capable of identifying AMAPs rapidly by using only sequence information would be beneficial for the high-throughput identification of AMAPs. In this study, we propose the first computational model (termed iAMAP-SCM) for the large-scale identification and characterization of peptides with antimalarial activity by using only sequence information. Specifically, we employed an interpretable scoring card method (SCM) to develop iAMAP-SCM and estimate propensities of 20 amino acids and 400 dipeptides to be AMAPs in a supervised manner. Experimental results showed that iAMAP-SCM could achieve a maximum accuracy and Matthew's coefficient correlation of 0.957 and 0.834, respectively, on the independent test dataset. In addition, SCM-derived propensities of 20 amino acids and selected physicochemical properties were used to provide an understanding of the functional mechanisms of AMAPs. Finally, a user-friendly online computational platform of iAMAP-SCM is publicly available at http://pmlabstack.pythonanywhere.com/iAMAP-SCM. The iAMAP-SCM predictor is anticipated to assist experimental scientists in the high-throughput identification of potential AMAP candidates for the treatment of malaria and other clinical applications.",
+                "authors": [
+                    {
+                        "name": "Charoenkwan P."
+                    },
+                    {
+                        "name": "Chumnanpuen P."
+                    },
+                    {
+                        "name": "Lio P."
+                    },
+                    {
+                        "name": "Moni M.A."
+                    },
+                    {
+                        "name": "Schaduangrat N."
+                    },
+                    {
+                        "name": "Shoombuatong W."
+                    }
+                ],
+                "date": "2022-11-15T00:00:00Z",
+                "journal": "ACS Omega",
+                "title": "iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides"
+            },
+            "pmcid": "PMC9670693",
+            "pmid": "36406571"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/ianimal/ianimal.biotools.json b/data/ianimal/ianimal.biotools.json
new file mode 100644
index 0000000000000..64cb6fd83328e
--- /dev/null
+++ b/data/ianimal/ianimal.biotools.json
@@ -0,0 +1,102 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T00:35:03.606366Z",
+    "biotoolsCURIE": "biotools:ianimal",
+    "biotoolsID": "ianimal",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "shzhao@mail.hzau.edu.cn",
+            "name": "Shuhong Zhao",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xiaoleiliu@mail.hzau.edu.cn",
+            "name": "Xiaolei Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Hong Liu"
+        },
+        {
+            "name": "Yuhua Fu"
+        }
+    ],
+    "description": "A cross-species omics knowledgebase for animals.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "GO concept ID",
+                        "uri": "http://edamontology.org/data_1176"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Identifier",
+                        "uri": "http://edamontology.org/data_0842"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Gene regulatory network analysis",
+                    "uri": "http://edamontology.org/operation_1781"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ianimal.pro/",
+    "lastUpdate": "2022-12-31T00:35:03.609061Z",
+    "name": "IAnimal",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC936",
+            "pmid": "36300629"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Phenomics",
+            "uri": "http://edamontology.org/topic_3298"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/iantisplodge/iantisplodge.biotools.json b/data/iantisplodge/iantisplodge.biotools.json
new file mode 100644
index 0000000000000..a45a2978a73b4
--- /dev/null
+++ b/data/iantisplodge/iantisplodge.biotools.json
@@ -0,0 +1,100 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T00:25:06.067069Z",
+    "biotoolsCURIE": "biotools:iantisplodge",
+    "biotoolsID": "iantisplodge",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "christoph.Lippert@hpi.de",
+            "name": "Christoph Lippert",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Eric L Lindberg"
+        },
+        {
+            "name": "Norbert Hübner"
+        },
+        {
+            "name": "Jesper B Lund",
+            "orcidid": "https://orcid.org/0000-0001-9483-1603"
+        }
+    ],
+    "description": "A neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deisotoping",
+                    "uri": "http://edamontology.org/operation_3629"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/HealthML/AntiSplodge/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-31T00:28:49.301672Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://pypi.org/project/antisplodge/"
+        }
+    ],
+    "name": "iAntiSplodge",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NARGAB/LQAC073",
+            "pmcid": "PMC9549785",
+            "pmid": "36225530"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/ibm_cloud_data_pak/ibm_cloud_data_pak.biotools.json b/data/ibm_cloud_data_pak/ibm_cloud_data_pak.biotools.json
new file mode 100644
index 0000000000000..119896cc59640
--- /dev/null
+++ b/data/ibm_cloud_data_pak/ibm_cloud_data_pak.biotools.json
@@ -0,0 +1,48 @@
+{
+    "additionDate": "2023-01-31T07:33:21.392222Z",
+    "biotoolsCURIE": "biotools:ibm_cloud_data_pak",
+    "biotoolsID": "ibm_cloud_data_pak",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "url": "https://www.ibm.com/training/"
+        }
+    ],
+    "description": "Predict outcomes faster using a platform built with data fabric architecture. Collect, organize and analyze data, no matter where it resides.",
+    "documentation": [
+        {
+            "type": [
+                "Training material"
+            ],
+            "url": "https://www.ibm.com/training/"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.ibm.com/products/cloud-pak-for-data",
+    "lastUpdate": "2023-02-01T12:49:42.589102Z",
+    "license": "Proprietary",
+    "link": [
+        {
+            "type": [
+                "Software catalogue"
+            ],
+            "url": "https://www.ibm.com/products/cloud-pak-for-data"
+        }
+    ],
+    "name": "IBM Cloud Data Pak",
+    "owner": "iacs-biocomputacion"
+}
diff --git a/data/icam-reg/icam-reg.biotools.json b/data/icam-reg/icam-reg.biotools.json
new file mode 100644
index 0000000000000..f6310d451867e
--- /dev/null
+++ b/data/icam-reg/icam-reg.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-06T07:27:38.648745Z",
+    "biotoolsCURIE": "biotools:icam-reg",
+    "biotoolsID": "icam-reg",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Cher Bass"
+        }
+    ],
+    "description": "Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/CherBass/ICAM",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-06T07:27:38.651218Z",
+    "license": "MIT",
+    "name": "ICAM-reg",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1109/TMI.2022.3221890",
+            "metadata": {
+                "abstract": "AuthorAn important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN (variational autoencoder - general adversarial network) for translation called ICAM, to explicitly disentangle class relevant features, from background confounds, for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer&#x2019;s Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on GitHub https://github.com/CherBass/ICAM.",
+                "authors": [
+                    {
+                        "name": "Alfaro-Almagro F."
+                    },
+                    {
+                        "name": "Bass C."
+                    },
+                    {
+                        "name": "Da Silva M."
+                    },
+                    {
+                        "name": "Fitzgibbon S.P."
+                    },
+                    {
+                        "name": "Glasser M.F."
+                    },
+                    {
+                        "name": "Robinson E.C."
+                    },
+                    {
+                        "name": "Smith S.M."
+                    },
+                    {
+                        "name": "Sousa H.S."
+                    },
+                    {
+                        "name": "Sudre C."
+                    },
+                    {
+                        "name": "Tudosiu P."
+                    },
+                    {
+                        "name": "Williams L.Z.J."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "IEEE Transactions on Medical Imaging",
+                "title": "ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans"
+            },
+            "pmid": "36374873"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        },
+        {
+            "term": "Neurology",
+            "uri": "http://edamontology.org/topic_3334"
+        }
+    ]
+}
diff --git a/data/ican/ican.biotools.json b/data/ican/ican.biotools.json
new file mode 100644
index 0000000000000..4ff83c66b6a1a
--- /dev/null
+++ b/data/ican/ican.biotools.json
@@ -0,0 +1,96 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T00:20:04.996941Z",
+    "biotoolsCURIE": "biotools:ican",
+    "biotoolsID": "ican",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "kurata@bio.kyutech.ac.jp",
+            "name": "Hiroyuki Kurata",
+            "orcidid": "https://orcid.org/0000-0003-4254-2214",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Sho Tsukiyama"
+        }
+    ],
+    "description": "Interpretable cross-attention network for identifying drug and target protein interactions.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein interaction network analysis",
+                    "uri": "http://edamontology.org/operation_0276"
+                },
+                {
+                    "term": "Protein interaction network prediction",
+                    "uri": "http://edamontology.org/operation_3094"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/kuratahiroyuki/ICAN",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-31T00:20:04.999830Z",
+    "license": "MIT",
+    "name": "ICAN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PONE.0276609",
+            "metadata": {
+                "abstract": "© 2022 Kurata, Tsukiyama. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Drug–target protein interaction (DTI) identification is fundamental for drug discovery and drug repositioning, because therapeutic drugs act on disease-causing proteins. However, the DTI identification process often requires expensive and time-consuming tasks, including biological experiments involving large numbers of candidate compounds. Thus, a variety of computation approaches have been developed. Of the many approaches available, chemo-genomics feature-based methods have attracted considerable attention. These methods compute the feature descriptors of drugs and proteins as the input data to train machine and deep learning models to enable accurate prediction of unknown DTIs. In addition, attention-based learning methods have been proposed to identify and interpret DTI mechanisms. However, improvements are needed for enhancing prediction performance and DTI mechanism elucidation. To address these problems, we developed an attention-based method designated the interpretable cross-attention network (ICAN), which predicts DTIs using the Simplified Molecular Input Line Entry System of drugs and amino acid sequences of target proteins. We optimized the attention mechanism architecture by exploring the cross-attention or self-attention, attention layer depth, and selection of the context matrixes from the attention mechanism. We found that a plain attention mechanism that decodes drug-related protein context features without any protein-related drug context features effectively achieved high performance. The ICAN outperformed state-of-the-art methods in several metrics on the DAVIS dataset and first revealed with statistical significance that some weighted sites in the cross-attention weight matrix represent experimental binding sites, thus demonstrating the high interpretability of the results. The program is freely available at https://github.com/kuratahiroyuki/ICAN.",
+                "authors": [
+                    {
+                        "name": "Kurata H."
+                    },
+                    {
+                        "name": "Tsukiyama S."
+                    }
+                ],
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "ICAN: Interpretable cross-attention network for identifying drug and target protein interactions"
+            },
+            "pmcid": "PMC9591068",
+            "pmid": "36279284"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/icardiotoxcsm/icardiotoxcsm.biotools.json b/data/icardiotoxcsm/icardiotoxcsm.biotools.json
new file mode 100644
index 0000000000000..4963e044b782a
--- /dev/null
+++ b/data/icardiotoxcsm/icardiotoxcsm.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T00:15:05.074311Z",
+    "biotoolsCURIE": "biotools:icardiotoxcsm",
+    "biotoolsID": "icardiotoxcsm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Alex G. C. de Sá"
+        },
+        {
+            "name": "Saba Iftkhar"
+        },
+        {
+            "name": "David B. Ascher",
+            "orcidid": "https://orcid.org/0000-0003-2948-2413"
+        },
+        {
+            "name": "Douglas E. V. Pires",
+            "orcidid": "https://orcid.org/0000-0002-3004-2119"
+        }
+    ],
+    "description": "A Web Server for Predicting Cardiotoxicity of Small Molecules.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "SMILES string",
+                        "uri": "http://edamontology.org/data_2301"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://biosig.lab.uq.edu.au/cardiotoxcsm",
+    "lastUpdate": "2022-12-31T00:15:05.077046Z",
+    "name": "icardioToxCSM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCIM.2C00822",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society.The design of novel, safe, and effective drugs to treat human diseases is a challenging venture, with toxicity being one of the main sources of attrition at later stages of development. Failure due to toxicity incurs a significant increase in costs and time to market, with multiple drugs being withdrawn from the market due to their adverse effects. Cardiotoxicity, for instance, was responsible for the failure of drugs such as fenspiride, propoxyphene, and valdecoxib. While significant effort has been dedicated to mitigate this issue by developing computational approaches that aim to identify molecules likely to be toxic, including quantitative structure-activity relationship models and machine learning methods, current approaches present limited performance and interpretability. To overcome these, we propose a new web-based computational method, cardioToxCSM, which can predict six types of cardiac toxicity outcomes, including arrhythmia, cardiac failure, heart block, hERG toxicity, hypertension, and myocardial infarction, efficiently and accurately. cardioToxCSM was developed using the concept of graph-based signatures, molecular descriptors, toxicophore matchings, and molecular fingerprints, leveraging explainable machine learning, and was validated internally via different cross validation schemes and externally via low-redundancy blind sets. The models presented robust performances with areas under ROC curves of up to 0.898 on 5-fold cross-validation, consistent with metrics on blind tests. Additionally, our models provide interpretation of the predictions by identifying whether substructures that are commonly enriched in toxic compounds were present. We believe cardioToxCSM will provide valuable insight into the potential cardiotoxicity of small molecules early on drug screening efforts. The method is made freely available as a web server at https://biosig.lab.uq.edu.au/cardiotoxcsm.",
+                "authors": [
+                    {
+                        "name": "Aljarf R."
+                    },
+                    {
+                        "name": "Ascher D.B."
+                    },
+                    {
+                        "name": "De Sa A.G.C."
+                    },
+                    {
+                        "name": "Iftkhar S."
+                    },
+                    {
+                        "name": "Pires D.E.V."
+                    },
+                    {
+                        "name": "Velloso J.P.L."
+                    }
+                ],
+                "date": "2022-10-24T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "CardioToxCSM: A Web Server for Predicting Cardiotoxicity of Small Molecules"
+            },
+            "pmid": "36219164"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/icescreen/icescreen.biotools.json b/data/icescreen/icescreen.biotools.json
new file mode 100644
index 0000000000000..5e6893e5a1529
--- /dev/null
+++ b/data/icescreen/icescreen.biotools.json
@@ -0,0 +1,97 @@
+{
+    "additionDate": "2022-12-31T00:10:13.950087Z",
+    "biotoolsCURIE": "biotools:icescreen",
+    "biotoolsID": "icescreen",
+    "confidence_flag": "high",
+    "credit": [
+        {
+            "email": "helene.chiapello@inrae.fr",
+            "name": "Hélène Chiapello",
+            "orcidid": "https://orcid.org/0000-0001-5102-0632",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "nathalie.leblond@univ-lorraine.fr",
+            "name": "Nathalie Leblond-Bourget",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Thomas Lacroix"
+        },
+        {
+            "name": "Julie Lao",
+            "orcidid": "https://orcid.org/0000-0002-5290-3098"
+        }
+    ],
+    "description": "A tool to detect Firmicute ICEs and IMEs, isolated or enclosed in composite structures.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Protein feature detection",
+                    "uri": "http://edamontology.org/operation_3092"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://icescreen.migale.inrae.fr",
+    "lastUpdate": "2022-12-31T00:10:13.952610Z",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://anaconda.org/search?q=icescreen"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://forgemia.inra.fr/ices_imes_analysis/icescreen"
+        }
+    ],
+    "name": "ICEscreen",
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NARGAB/LQAC079",
+            "pmcid": "PMC9585547",
+            "pmid": "36285285"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Mobile genetic elements",
+            "uri": "http://edamontology.org/topic_0798"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Structural variation",
+            "uri": "http://edamontology.org/topic_3175"
+        }
+    ],
+    "version": [
+        "1.1.0"
+    ]
+}
diff --git a/data/idjexpress/idjexpress.biotools.json b/data/idjexpress/idjexpress.biotools.json
new file mode 100644
index 0000000000000..9d2ffb0c02ac2
--- /dev/null
+++ b/data/idjexpress/idjexpress.biotools.json
@@ -0,0 +1,95 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-31T00:00:08.724956Z",
+    "biotoolsCURIE": "biotools:idjexpress",
+    "biotoolsID": "idjexpress",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jan.mauer@gmail.com",
+            "name": "Jan Mauer",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "linhiel@gmail.com",
+            "name": "Lina Marcela Gallego-Paez",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An Integrated Application for Differential Splicing Analysis and Visualization.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Alternative splicing prediction",
+                    "uri": "http://edamontology.org/operation_0264"
+                },
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Weighted correlation network analysis",
+                    "uri": "http://edamontology.org/operation_3766"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MauerLab/DJExpress",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2022-12-31T00:00:08.728069Z",
+    "license": "MIT",
+    "name": "iDJExpress",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FBINF.2022.786898",
+            "pmcid": "PMC9580925",
+            "pmid": "36304260"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/idmet/idmet.biotools.json b/data/idmet/idmet.biotools.json
new file mode 100644
index 0000000000000..051f8ce9311ef
--- /dev/null
+++ b/data/idmet/idmet.biotools.json
@@ -0,0 +1,97 @@
+{
+    "additionDate": "2023-02-06T09:20:48.126138Z",
+    "biotoolsCURIE": "biotools:idmet",
+    "biotoolsID": "idmet",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "h.yama2396@gmail.com",
+            "name": "Hiroyuki Yamamoto",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Network-based approach for integrating differential analysis of cancer metabolomics.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Network visualisation",
+                    "uri": "http://edamontology.org/operation_3925"
+                },
+                {
+                    "term": "Sorting",
+                    "uri": "http://edamontology.org/operation_3802"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/riramatsuta/iDMET",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-06T09:20:48.128909Z",
+    "license": "Not licensed",
+    "name": "iDMET",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05068-0",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study. Results: We developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered. Conclusions: We believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena.",
+                "authors": [
+                    {
+                        "name": "Matsuta R."
+                    },
+                    {
+                        "name": "Saito R."
+                    },
+                    {
+                        "name": "Tomita M."
+                    },
+                    {
+                        "name": "Yamamoto H."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "iDMET: network-based approach for integrating differential analysis of cancer metabolomics"
+            },
+            "pmcid": "PMC9706903",
+            "pmid": "36443658"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/idna-abf/idna-abf.biotools.json b/data/idna-abf/idna-abf.biotools.json
new file mode 100644
index 0000000000000..f7206096be234
--- /dev/null
+++ b/data/idna-abf/idna-abf.biotools.json
@@ -0,0 +1,161 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-30T23:56:23.080805Z",
+    "biotoolsCURIE": "biotools:idna-abf",
+    "biotoolsID": "idna-abf",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "knakai@ims.u-tokyo.ac.jp",
+            "name": "Kenta Nakai",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "weileyi@sdu.edu.cn",
+            "name": "Leyi Wei",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Junru Jin"
+        },
+        {
+            "name": "Yingying Yu"
+        }
+    ],
+    "description": "Multi-scale deep biological language learning model for the interpretable prediction of DNA methylations.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "DNA sequence",
+                        "uri": "http://edamontology.org/data_3494"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                },
+                {
+                    "term": "Whole genome methylation analysis",
+                    "uri": "http://edamontology.org/operation_3206"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://inner.wei-group.net/idnaabf/#/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-30T23:57:22.473995Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/FakeEnd/iDNA_ABF"
+        }
+    ],
+    "name": "iDNA-ABF",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13059-022-02780-1",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.",
+                "authors": [
+                    {
+                        "name": "Dai Y."
+                    },
+                    {
+                        "name": "Jiang Y."
+                    },
+                    {
+                        "name": "Jin J."
+                    },
+                    {
+                        "name": "Li Z."
+                    },
+                    {
+                        "name": "Nakai K."
+                    },
+                    {
+                        "name": "Pang C."
+                    },
+                    {
+                        "name": "Su R."
+                    },
+                    {
+                        "name": "Wang R."
+                    },
+                    {
+                        "name": "Wei L."
+                    },
+                    {
+                        "name": "Yu Y."
+                    },
+                    {
+                        "name": "Zeng X."
+                    },
+                    {
+                        "name": "Zou Q."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Genome Biology",
+                "title": "iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations"
+            },
+            "pmcid": "PMC9575223",
+            "pmid": "36253864"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Methylated DNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3674"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        }
+    ]
+}
diff --git a/data/idpconformergenerator/idpconformergenerator.biotools.json b/data/idpconformergenerator/idpconformergenerator.biotools.json
new file mode 100644
index 0000000000000..9f6100eaf2cab
--- /dev/null
+++ b/data/idpconformergenerator/idpconformergenerator.biotools.json
@@ -0,0 +1,150 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-22T02:54:07.414857Z",
+    "biotoolsCURIE": "biotools:idpconformergenerator",
+    "biotoolsID": "idpconformergenerator",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Zi Hao Liu"
+        },
+        {
+            "name": "João M.C. Teixeira",
+            "orcidid": "http://orcid.org/0000-0002-9113-0622"
+        },
+        {
+            "name": "Julie D. Forman-Kay",
+            "orcidid": "http://orcid.org/0000-0001-8265-972X"
+        },
+        {
+            "name": "Teresa Head-Gordon",
+            "orcidid": "http://orcid.org/0000-0003-0025-8987"
+        }
+    ],
+    "description": "A Flexible Software Suite for Sampling Conformational Space of Disordered Protein States.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://idpconformergenerator.readthedocs.io/en/latest/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Protein disorder prediction",
+                    "uri": "http://edamontology.org/operation_3904"
+                },
+                {
+                    "term": "Protein secondary structure comparison",
+                    "uri": "http://edamontology.org/operation_2488"
+                },
+                {
+                    "term": "Protein secondary structure prediction (coils)",
+                    "uri": "http://edamontology.org/operation_0470"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/julie-forman-kay-lab/IDPConformerGenerator",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-22T02:54:07.417386Z",
+    "license": "Apache-2.0",
+    "name": "IDPConformerGenerator",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/acs.jpca.2c03726",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Published by American Chemical Society.The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.",
+                "authors": [
+                    {
+                        "name": "Forman-Kay J.D."
+                    },
+                    {
+                        "name": "Haghighatlari M."
+                    },
+                    {
+                        "name": "Head-Gordon T."
+                    },
+                    {
+                        "name": "Krzeminski M."
+                    },
+                    {
+                        "name": "Li J."
+                    },
+                    {
+                        "name": "Liu Z.H."
+                    },
+                    {
+                        "name": "Namini A."
+                    },
+                    {
+                        "name": "Shamandy A.A."
+                    },
+                    {
+                        "name": "Teixeira J.M.C."
+                    },
+                    {
+                        "name": "Vernon R.M."
+                    },
+                    {
+                        "name": "Yu L."
+                    },
+                    {
+                        "name": "Zhang O."
+                    }
+                ],
+                "citationCount": 2,
+                "date": "2022-09-08T00:00:00Z",
+                "journal": "Journal of Physical Chemistry A",
+                "title": "IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States"
+            },
+            "pmcid": "PMC9465686",
+            "pmid": "36030416"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular dynamics",
+            "uri": "http://edamontology.org/topic_0176"
+        },
+        {
+            "term": "Protein disordered structure",
+            "uri": "http://edamontology.org/topic_3538"
+        },
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        },
+        {
+            "term": "Protein secondary structure",
+            "uri": "http://edamontology.org/topic_3542"
+        }
+    ]
+}
diff --git a/data/idvip/idvip.biotools.json b/data/idvip/idvip.biotools.json
new file mode 100644
index 0000000000000..de8684f5ebe06
--- /dev/null
+++ b/data/idvip/idvip.biotools.json
@@ -0,0 +1,103 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-30T23:52:02.823170Z",
+    "biotoolsCURIE": "biotools:idvip",
+    "biotoolsID": "idvip",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Hui-Ju Kao"
+        },
+        {
+            "name": "Shun-Long Weng"
+        },
+        {
+            "name": "Kai-Yao Huang",
+            "orcidid": "https://orcid.org/0000-0001-9855-1035"
+        }
+    ],
+    "description": "iDVIP is a web server for identifying Viral integrase inhibitory peptides (VINIPs).",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://mer.hc.mmh.org.tw/iDVIP/",
+    "lastUpdate": "2022-12-30T23:52:02.825676Z",
+    "name": "iDVIP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC406",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Antiretroviral peptides are a kind of bioactive peptides that present inhibitory activity against retroviruses through various mechanisms. Among them, viral integrase inhibitory peptides (VINIPs) are a class of antiretroviral peptides that have the ability to block the action of integrase proteins, which is essential for retroviral replication. As the number of experimentally verified bioactive peptides has increased significantly, the lack of in silico machine learning approaches can effectively predict the peptides with the integrase inhibitory activity. Here, we have developed the first prediction model for identifying the novel VINIPs using the sequence characteristics, and the hybrid feature set was considered to improve the predictive ability. The performance was evaluated by 5-fold cross-validation based on the training dataset, and the result indicates the proposed model is capable of predicting the VINIPs, with a sensitivity of 85.82%, a specificity of 88.81%, an accuracy of 88.37%, a balanced accuracy of 87.32% and a Matthews correlation coefficient value of 0.64. Most importantly, the model also consistently provides effective performance in independent testing. To sum up, we propose the first computational approach for identifying and characterizing the VINIPs, which can be considered novel antiretroviral therapy agents. Ultimately, to facilitate further research and development, iDVIP, an automatic computational tool that predicts the VINIPs has been developed, which is now freely available at http://mer.hc.mmh.org.tw/iDVIP/.",
+                "authors": [
+                    {
+                        "name": "Chen C.-H."
+                    },
+                    {
+                        "name": "Huang K.-Y."
+                    },
+                    {
+                        "name": "Kao H.-J."
+                    },
+                    {
+                        "name": "Weng S.-L."
+                    },
+                    {
+                        "name": "Weng T.-H."
+                    }
+                ],
+                "date": "2022-11-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "iDVIP: identification and characterization of viral integrase inhibitory peptides"
+            },
+            "pmid": "36215051"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/ienhancer-dcla/ienhancer-dcla.biotools.json b/data/ienhancer-dcla/ienhancer-dcla.biotools.json
new file mode 100644
index 0000000000000..bdccacf51675f
--- /dev/null
+++ b/data/ienhancer-dcla/ienhancer-dcla.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-06T09:25:23.949462Z",
+    "biotoolsCURIE": "biotools:ienhancer-dcla",
+    "biotoolsID": "ienhancer-dcla",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhaojianping@126.com",
+            "name": "Jian-ping Zhao",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhengch99@126.com",
+            "name": "Chun-Hou Zheng",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A prediction method based on deep learning is proposed to identify enhancers and enhancer strength, called iEnhancer-DCLA",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "DNA transcription",
+                    "uri": "http://edamontology.org/operation_0372"
+                },
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Transcription factor binding site prediction",
+                    "uri": "http://edamontology.org/operation_0445"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/WamesM/iEnhancer-DCLA",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-06T09:25:23.951905Z",
+    "license": "Not licensed",
+    "name": "iEnhancer-DCLA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05033-X",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Enhancers are small regions of DNA that bind to proteins, which enhance the transcription of genes. The enhancer may be located upstream or downstream of the gene. It is not necessarily close to the gene to be acted on, because the entanglement structure of chromatin allows the positions far apart in the sequence to have the opportunity to contact each other. Therefore, identifying enhancers and their strength is a complex and challenging task. In this article, a new prediction method based on deep learning is proposed to identify enhancers and enhancer strength, called iEnhancer-DCLA. Firstly, we use word2vec to convert k-mers into number vectors to construct an input matrix. Secondly, we use convolutional neural network and bidirectional long short-term memory network to extract sequence features, and finally use the attention mechanism to extract relatively important features. In the task of predicting enhancers and their strengths, this method has improved to a certain extent in most evaluation indexes. In summary, we believe that this method provides new ideas in the analysis of enhancers.",
+                "authors": [
+                    {
+                        "name": "Liao M."
+                    },
+                    {
+                        "name": "Tian J."
+                    },
+                    {
+                        "name": "Zhao J.-P."
+                    },
+                    {
+                        "name": "Zheng C.-H."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework"
+            },
+            "pmcid": "PMC9664816",
+            "pmid": "36376800"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/iepicas-dl/iepicas-dl.biotools.json b/data/iepicas-dl/iepicas-dl.biotools.json
new file mode 100644
index 0000000000000..31ffdf99719df
--- /dev/null
+++ b/data/iepicas-dl/iepicas-dl.biotools.json
@@ -0,0 +1,142 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-24T22:50:49.392176Z",
+    "biotoolsCURIE": "biotools:iepicas-dl",
+    "biotoolsID": "iepicas-dl",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ydsun@ion.ac.cn",
+            "name": "Yidi Sun",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zuoerwei@caas.cn",
+            "name": "Erwei Zuo",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Juan Meng"
+        },
+        {
+            "name": "Lei Ma"
+        },
+        {
+            "name": "Leilei Wu"
+        },
+        {
+            "name": "Qianqian Yang"
+        }
+    ],
+    "description": "Predicting sgRNA activity for CRISPR-mediated epigenome editing by deep learning.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence",
+                        "uri": "http://edamontology.org/data_2044"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.sunlab.fun:3838/EpiCas-DL",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-24T22:50:49.394720Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/yangqianq/EpiCas-DL"
+        }
+    ],
+    "name": "iEpiCas-DL",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.11.034",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsCRISPR-mediated epigenome editing enables gene expression regulation without changing the underlying DNA sequence, and thus has vast potential for basic research and gene therapy. Effective selection of a single guide RNA (sgRNA) with high on-target efficiency and specificity would facilitate the application of epigenome editing tools. Here we performed an extensive analysis of CRISPR-mediated epigenome editing tools on thousands of experimentally examined on-target sites and established EpiCas-DL, a deep learning framework to optimize sgRNA design for gene silencing or activation. EpiCas-DL achieves high accuracy in sgRNA activity prediction for targeted gene silencing or activation and outperforms other available in silico methods. In addition, EpiCas-DL also identifies both epigenetic and sequence features that affect sgRNA efficacy in gene silencing and activation, facilitating the application of epigenome editing for research and therapy. EpiCas-DL is available at http://www.sunlab.fun:3838/EpiCas-DL.",
+                "authors": [
+                    {
+                        "name": "Ma L."
+                    },
+                    {
+                        "name": "Meng J."
+                    },
+                    {
+                        "name": "Sun Y."
+                    },
+                    {
+                        "name": "Wu L."
+                    },
+                    {
+                        "name": "Yang Q."
+                    },
+                    {
+                        "name": "Zuo E."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "EpiCas-DL: Predicting sgRNA activity for CRISPR-mediated epigenome editing by deep learning"
+            },
+            "pmcid": "PMC9763632",
+            "pmid": "36582444"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Methylated DNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3674"
+        }
+    ]
+}
diff --git a/data/iexcerno/iexcerno.biotools.json b/data/iexcerno/iexcerno.biotools.json
new file mode 100644
index 0000000000000..3c1819a866c77
--- /dev/null
+++ b/data/iexcerno/iexcerno.biotools.json
@@ -0,0 +1,78 @@
+{
+    "additionDate": "2023-02-06T09:30:14.389622Z",
+    "biotoolsCURIE": "biotools:iexcerno",
+    "biotoolsID": "iexcerno",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "davila3@stolaf.edu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A package used as a classifier for determining the origin of a mutation, specifically for samples that have been preserved using formalin-fixation paraffin-embedding (FFPE).",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Filtering",
+                    "uri": "http://edamontology.org/operation_3695"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "Variant filtering",
+                    "uri": "http://edamontology.org/operation_3675"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/jdavilal/excerno",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-06T09:30:14.392057Z",
+    "license": "Not licensed",
+    "name": "iexcerno",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1089/CMB.2022.0394",
+            "pmid": "36322906"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/iflnc/iflnc.biotools.json b/data/iflnc/iflnc.biotools.json
new file mode 100644
index 0000000000000..946acb1543e5d
--- /dev/null
+++ b/data/iflnc/iflnc.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-30T23:48:09.265568Z",
+    "biotoolsCURIE": "biotools:iflnc",
+    "biotoolsID": "iflnc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chan.zhou@umassmed.edu",
+            "name": "Chan Zhou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Peng Zhou"
+        },
+        {
+            "name": "Zixiu Li"
+        },
+        {
+            "name": "Zhiping Weng",
+            "orcidid": "https://orcid.org/0000-0002-3032-7966"
+        }
+    ],
+    "description": "Flnc is software that can accurately identify full-length long noncoding RNAs (lncRNAs) from human RNA-seq data. lncRNAs are linear transcripts of more than 200 nucleotides that do not encode proteins.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Peak calling",
+                    "uri": "http://edamontology.org/operation_3222"
+                },
+                {
+                    "term": "Promoter prediction",
+                    "uri": "http://edamontology.org/operation_0440"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/CZhouLab/Flnc",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-30T23:48:09.269599Z",
+    "license": "Not licensed",
+    "name": "iFlnc",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/NCRNA8050070",
+            "metadata": {
+                "abstract": "© 2022 by the authors.Long noncoding RNAs (lncRNAs) play critical regulatory roles in human development and disease. Although there are over 100,000 samples with available RNA sequencing (RNA-seq) data, many lncRNAs have yet to be annotated. The conventional approach to identifying novel lncRNAs from RNA-seq data is to find transcripts without coding potential but this approach has a false discovery rate of 30–75%. Other existing methods either identify only multi-exon lncRNAs, missing single-exon lncRNAs, or require transcriptional initiation profiling data (such as H3K4me3 ChIP-seq data), which is unavailable for many samples with RNA-seq data. Because of these limitations, current methods cannot accurately identify novel lncRNAs from existing RNA-seq data. To address this problem, we have developed software, Flnc, to accurately identify both novel and annotated full-length lncRNAs, including single-exon lncRNAs, directly from RNA-seq data without requiring transcriptional initiation profiles. Flnc integrates machine learning models built by incorporating four types of features: transcript length, promoter signature, multiple exons, and genomic location. Flnc achieves state-of-the-art prediction power with an AUROC score over 0.92. Flnc significantly improves the prediction accuracy from less than 50% using the conventional approach to over 85%. Flnc is available via GitHub platform.",
+                "authors": [
+                    {
+                        "name": "Fitzgerald K.A."
+                    },
+                    {
+                        "name": "Kwon E."
+                    },
+                    {
+                        "name": "Li Z."
+                    },
+                    {
+                        "name": "Weng Z."
+                    },
+                    {
+                        "name": "Zhou C."
+                    },
+                    {
+                        "name": "Zhou P."
+                    }
+                ],
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "Non-coding RNA",
+                "title": "Flnc: Machine Learning Improves the Identification of Novel Long Noncoding RNAs from Stand-Alone RNA-Seq Data"
+            },
+            "pmcid": "PMC9607125",
+            "pmid": "36287122"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/igneous/igneous.biotools.json b/data/igneous/igneous.biotools.json
new file mode 100644
index 0000000000000..ba98029e3aacb
--- /dev/null
+++ b/data/igneous/igneous.biotools.json
@@ -0,0 +1,134 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-24T22:27:25.226440Z",
+    "biotoolsCURIE": "biotools:igneous",
+    "biotoolsID": "igneous",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ws9@princeton.edu",
+            "name": "William Silversmith",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Aleksandar Zlateski"
+        },
+        {
+            "name": "H Sebastian Seung"
+        },
+        {
+            "name": "Jingpeng Wu"
+        }
+    ],
+    "description": "Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling.",
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://hub.docker.com/repository/docker/seunglab/igneous"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pypi.org/project/igneous-pipeline/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-24T22:27:25.228862Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/seung-lab/igneous"
+        }
+    ],
+    "name": "Igneous",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FNCIR.2022.977700",
+            "metadata": {
+                "abstract": "Copyright © 2022 Silversmith, Zlateski, Bae, Tartavull, Kemnitz, Wu and Seung.Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multi-resolution meshes, image hierarchies (for both modalities) for visualization and analysis, and tools to manage the large amount of data. However, open tools for large-scale meshing, skeletonization, and data management have been missing. Igneous is a Python-based distributed computing framework that enables economical meshing, skeletonization, image hierarchy creation, and data management using cloud or cluster computing that has been proven to scale horizontally. We sketch Igneous's computing framework, show how to use it, and characterize its performance and data storage.",
+                "authors": [
+                    {
+                        "name": "Bae J.A."
+                    },
+                    {
+                        "name": "Kemnitz N."
+                    },
+                    {
+                        "name": "Seung H.S."
+                    },
+                    {
+                        "name": "Silversmith W."
+                    },
+                    {
+                        "name": "Tartavull I."
+                    },
+                    {
+                        "name": "Wu J."
+                    },
+                    {
+                        "name": "Zlateski A."
+                    }
+                ],
+                "date": "2022-11-25T00:00:00Z",
+                "journal": "Frontiers in Neural Circuits",
+                "title": "Igneous: Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling"
+            },
+            "pmcid": "PMC9732676",
+            "pmid": "36506593"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Computer science",
+            "uri": "http://edamontology.org/topic_3316"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        }
+    ],
+    "version": [
+        "4.12.0"
+    ]
+}
diff --git a/data/iguana/iguana.biotools.json b/data/iguana/iguana.biotools.json
new file mode 100644
index 0000000000000..aa595c77d2e90
--- /dev/null
+++ b/data/iguana/iguana.biotools.json
@@ -0,0 +1,102 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T19:39:59.409644Z",
+    "biotoolsCURIE": "biotools:iguana",
+    "biotoolsID": "iguana",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "n.m.rajpoot@warwick.ac.uk",
+            "name": "Nasir M. Rajpoot",
+            "orcidid": "http://orcid.org/0000-0002-4706-1308",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "David Snead",
+            "orcidid": "http://orcid.org/0000-0002-0766-9650"
+        },
+        {
+            "name": "Fayyaz Minhas",
+            "orcidid": "http://orcid.org/0000-0001-9129-1189"
+        },
+        {
+            "name": "Simon Graham",
+            "orcidid": "http://orcid.org/0000-0002-2214-8212"
+        }
+    ],
+    "description": "IGUANA is a graph neural network built for colon biopsy screening. IGUANA represents a whole-slide image (WSI) as a graph built with nodes on top of glands in the tissue, each node associated with a set of interpretable features. The output of the pipeline is explainable, indicating glands and features that contribute to a WSI being predicted as abnormal.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://iguana.dcs.warwick.ac.uk/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-29T19:39:59.413770Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/TissueImageAnalytics/iguana"
+        }
+    ],
+    "name": "IGUANA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1101/2022.10.17.22279804"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Gastroenterology",
+            "uri": "http://edamontology.org/topic_3409"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/igv/igv.biotools.json b/data/igv/igv.biotools.json
index 517a92ff13009..4f2a4c1717763 100644
--- a/data/igv/igv.biotools.json
+++ b/data/igv/igv.biotools.json
@@ -1,10 +1,13 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2017-01-13T13:15:04Z",
     "biotoolsCURIE": "biotools:igv",
     "biotoolsID": "igv",
     "collectionID": [
         "Animal and Crop Genomics"
     ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "credit": [
         {
             "email": "igv-team@broadinstitute.org",
@@ -68,8 +71,34 @@
     "language": [
         "Java"
     ],
-    "lastUpdate": "2019-01-29T12:09:23Z",
+    "lastUpdate": "2023-02-24T22:17:09.685457Z",
     "license": "LGPL-2.1",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://igv.org/app"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/igvteam/igv-webapp"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/igvteam/igv.js"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://www.npmjs.com/package/igv"
+        }
+    ],
     "maturity": "Mature",
     "name": "IGV",
     "operatingSystem": [
@@ -79,6 +108,31 @@
     ],
     "owner": "seqwiki_import",
     "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC830",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: igv.js is an embeddable JavaScript implementation of the Integrative Genomics Viewer (IGV). It can be easily dropped into any web page with a single line of code and has no external dependencies. The viewer runs completely in the web browser, with no backend server and no data pre-processing required. AVAILABILITY AND IMPLEMENTATION: The igv.js JavaScript component can be installed from NPM at https://www.npmjs.com/package/igv. The source code is available at https://github.com/igvteam/igv.js under the MIT open-source license. IGV-Web, the end-user application built around igv.js, is available at https://igv.org/app. The source code is available at https://github.com/igvteam/igv-webapp under the MIT open-source license. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Mesirov J.P."
+                    },
+                    {
+                        "name": "Robinson J.T."
+                    },
+                    {
+                        "name": "Thorvaldsdottir H."
+                    },
+                    {
+                        "name": "Turner D."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "igv.js: an embeddable JavaScript implementation of the Integrative Genomics Viewer (IGV)"
+            },
+            "pmcid": "PMC9825295",
+            "pmid": "36562559"
+        },
         {
             "doi": "10.1093/BIB/BBS017",
             "metadata": {
@@ -94,7 +148,7 @@
                         "name": "Thorvaldsdottir H."
                     }
                 ],
-                "citationCount": 3988,
+                "citationCount": 4895,
                 "date": "2013-03-01T00:00:00Z",
                 "journal": "Briefings in Bioinformatics",
                 "title": "Integrative Genomics Viewer (IGV): High-performance genomics data visualization and exploration"
@@ -129,7 +183,7 @@
                         "name": "Winckler W."
                     }
                 ],
-                "citationCount": 5938,
+                "citationCount": 7611,
                 "date": "2011-01-01T00:00:00Z",
                 "journal": "Nature Biotechnology",
                 "title": "Integrative genomics viewer"
@@ -141,7 +195,9 @@
         }
     ],
     "toolType": [
-        "Desktop application"
+        "Desktop application",
+        "Library",
+        "Web application"
     ],
     "topic": [
         {
diff --git a/data/ikaraj/ikaraj.biotools.json b/data/ikaraj/ikaraj.biotools.json
new file mode 100644
index 0000000000000..533abc57ba739
--- /dev/null
+++ b/data/ikaraj/ikaraj.biotools.json
@@ -0,0 +1,96 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-06T09:35:36.050317Z",
+    "biotoolsCURIE": "biotools:ikaraj",
+    "biotoolsID": "ikaraj",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ali.afrasiabi@wimr.org.au",
+            "name": "Ali Afrasiabi",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Linux command-line tool to automate the end-to-end process of querying and downloading a wide range of file formats containing genomic and transcriptomic sequence data.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/GTP-programmers/KARAJ",
+    "language": [
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-06T09:35:36.052974Z",
+    "license": "MIT",
+    "name": "iKARAJ",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3390/IJMS232214418",
+            "metadata": {
+                "abstract": "© 2022 by the authors.Here we developed KARAJ, a fast and flexible Linux command-line tool to automate the end-to-end process of querying and downloading a wide range of genomic and transcriptomic sequence data types. The input to KARAJ is a list of PMCIDs or publication URLs or various types of accession numbers to automate four tasks as follows; firstly, it provides a summary list of accessible datasets generated by or used in these scientific articles, enabling users to select appropriate datasets; secondly, KARAJ calculates the size of files that users want to download and confirms the availability of adequate space on the local disk; thirdly, it generates a metadata table containing sample information and the experimental design of the corresponding study; and lastly, it enables users to download supplementary data tables attached to publications. Further, KARAJ provides a parallel downloading framework powered by Aspera connect which reduces the downloading time significantly.",
+                "authors": [
+                    {
+                        "name": "Afrasiabi A."
+                    },
+                    {
+                        "name": "Alinejad-Rokny H."
+                    },
+                    {
+                        "name": "Beheshti A."
+                    },
+                    {
+                        "name": "Labani M."
+                    },
+                    {
+                        "name": "Lovell N.H."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "International Journal of Molecular Sciences",
+                "title": "KARAJ: An Efficient Adaptive Multi-Processor Tool to Streamline Genomic and Transcriptomic Sequence Data Acquisition"
+            },
+            "pmcid": "PMC9694301",
+            "pmid": "36430895"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Data acquisition",
+            "uri": "http://edamontology.org/topic_3077"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/imagej/imagej.biotools.json b/data/imagej/imagej.biotools.json
index 228d7c8a6f662..456aa1c1d8c5e 100644
--- a/data/imagej/imagej.biotools.json
+++ b/data/imagej/imagej.biotools.json
@@ -117,7 +117,7 @@
     "language": [
         "Java"
     ],
-    "lastUpdate": "2022-09-17T12:17:33.939600Z",
+    "lastUpdate": "2023-02-20T14:38:20.238309Z",
     "link": [
         {
             "type": [
@@ -148,7 +148,7 @@
                         "name": "Schneider C.A."
                     }
                 ],
-                "citationCount": 32651,
+                "citationCount": 35011,
                 "date": "2012-07-01T00:00:00Z",
                 "journal": "Nature Methods",
                 "title": "NIH Image to ImageJ: 25 years of image analysis"
@@ -159,6 +159,10 @@
         }
     ],
     "relation": [
+        {
+            "biotoolsID": "gift_imagej",
+            "type": "usedBy"
+        },
         {
             "biotoolsID": "irimage",
             "type": "usedBy"
@@ -167,6 +171,10 @@
             "biotoolsID": "liplacet",
             "type": "usedBy"
         },
+        {
+            "biotoolsID": "mssr",
+            "type": "usedBy"
+        },
         {
             "biotoolsID": "omero_image",
             "type": "usedBy"
diff --git a/data/img_vr/img_vr.biotools.json b/data/img_vr/img_vr.biotools.json
new file mode 100644
index 0000000000000..c03d103abf18a
--- /dev/null
+++ b/data/img_vr/img_vr.biotools.json
@@ -0,0 +1,167 @@
+{
+    "additionDate": "2023-02-06T09:46:25.706496Z",
+    "biotoolsCURIE": "biotools:img_vr",
+    "biotoolsID": "img_vr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "antoniop.camargo@lbl.gov",
+            "name": "Antonio Pedro Camargo",
+            "orcidid": "https://orcid.org/0000-0003-3913-2484",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "sroux@lbl.gov",
+            "name": "Simon Roux",
+            "orcidid": "https://orcid.org/0000-0002-5831-5895",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "NCKyrpides@lbl.gov",
+            "name": "Nikos C Kyrpides",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An expanded database of uncultivated virus genomes within a framework of extensive functional, taxonomic, and ecological metadata.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                },
+                {
+                    "term": "Taxonomic classification",
+                    "uri": "http://edamontology.org/operation_3460"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://img.jgi.doe.gov/vr",
+    "lastUpdate": "2023-02-06T09:46:25.708819Z",
+    "license": "Other",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://portal.nersc.gov/cfs/m342/imgvr_stats/"
+        }
+    ],
+    "name": "IMG_VR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1037",
+            "metadata": {
+                "abstract": "© Published by Oxford University Press on behalf of Nucleic Acids Research 2022.Viruses are widely recognized as critical members of all microbiomes. Metagenomics enables large-scale exploration of the global virosphere, progressively revealing the extensive genomic diversity of viruses on Earth and highlighting the myriad of ways by which viruses impact biological processes. IMG/VR provides access to the largest collection of viral sequences obtained from (meta)genomes, along with functional annotation and rich metadata. A web interface enables users to efficiently browse and search viruses based on genome features and/or sequence similarity. Here, we present the fourth version of IMG/VR, composed of >15 million virus genomes and genome fragments, a ≈6-fold increase in size compared to the previous version. These clustered into 8.7 million viral operational taxonomic units, including 231 408 with at least one high-quality representative. Viral sequences in IMG/VR are now systematically identified from genomes, metagenomes, and metatranscriptomes using a new detection approach (geNomad), and IMG standard annotation are complemented with genome quality estimation using CheckV, taxonomic classification reflecting the latest taxonomic standards, and microbial host taxonomy prediction. IMG/VR v4 is available at https://img.jgi.doe.gov/vr, and the underlying data are available to download at https://genome.jgi.doe.gov/portal/IMG_VR.",
+                "authors": [
+                    {
+                        "name": "Call L."
+                    },
+                    {
+                        "name": "Camargo A.P."
+                    },
+                    {
+                        "name": "Chen I.-M.A."
+                    },
+                    {
+                        "name": "Chu K."
+                    },
+                    {
+                        "name": "Eloe-Fadrosh E.A."
+                    },
+                    {
+                        "name": "Ivanova N.N."
+                    },
+                    {
+                        "name": "Kyrpides N.C."
+                    },
+                    {
+                        "name": "Mukherjee S."
+                    },
+                    {
+                        "name": "Nayfach S."
+                    },
+                    {
+                        "name": "Neches R.Y."
+                    },
+                    {
+                        "name": "Palaniappan K."
+                    },
+                    {
+                        "name": "Ratner A."
+                    },
+                    {
+                        "name": "Reddy T.B.K."
+                    },
+                    {
+                        "name": "Ritter S.J."
+                    },
+                    {
+                        "name": "Roux S."
+                    },
+                    {
+                        "name": "Schulz F."
+                    },
+                    {
+                        "name": "Woyke T."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "IMG/VR v4: an expanded database of uncultivated virus genomes within a framework of extensive functional, taxonomic, and ecological metadata"
+            },
+            "pmcid": "PMC9825611",
+            "pmid": "36399502"
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "img",
+            "type": "includedIn"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Metatranscriptomics",
+            "uri": "http://edamontology.org/topic_3941"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Taxonomy",
+            "uri": "http://edamontology.org/topic_0637"
+        }
+    ],
+    "version": [
+        "4.0"
+    ]
+}
diff --git a/data/immerge/immerge.biotools.json b/data/immerge/immerge.biotools.json
new file mode 100644
index 0000000000000..ece4f499ed59e
--- /dev/null
+++ b/data/immerge/immerge.biotools.json
@@ -0,0 +1,115 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-06T09:50:15.358800Z",
+    "biotoolsCURIE": "biotools:immerge",
+    "biotoolsID": "immerge",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "heather.highland@unc.edu",
+            "name": "Heather M Highland",
+            "orcidid": "https://orcid.org/0000-0002-3583-8239",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "jennifer.e.below@vumc.org",
+            "name": "Jennifer E Below",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Tool to merge VCF genotype files at scale",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                },
+                {
+                    "term": "Sorting",
+                    "uri": "http://edamontology.org/operation_3802"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/belowlab/IMMerge",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-06T09:50:15.361219Z",
+    "license": "MIT",
+    "name": "IMMerge",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC750",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: Genomic data are often processed in batches and analyzed together to save time. However, it is challenging to combine multiple large VCFs and properly handle imputation quality and missing variants due to the limitations of available tools. To address these concerns, we developed IMMerge, a Python-based tool that takes advantage of multiprocessing to reduce running time. For the first time in a publicly available tool, imputation quality scores are correctly combined with Fisher's z transformation. AVAILABILITY AND IMPLEMENTATION: IMMerge is an open-source project under MIT license. Source code and user manual are available at https://github.com/belowlab/IMMerge.",
+                "authors": [
+                    {
+                        "name": "Below J.E."
+                    },
+                    {
+                        "name": "Chen H.-H."
+                    },
+                    {
+                        "name": "Gamazon E.R."
+                    },
+                    {
+                        "name": "Highland H.M."
+                    },
+                    {
+                        "name": "Petty A.S."
+                    },
+                    {
+                        "name": "Petty L.E."
+                    },
+                    {
+                        "name": "Polikowsky H.G."
+                    },
+                    {
+                        "name": "Zhu W."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "IMMerge: merging imputation data at scale"
+            },
+            "pmcid": "PMC9805583",
+            "pmid": "36413071"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        }
+    ]
+}
diff --git a/data/improve-dd/improve-dd.biotools.json b/data/improve-dd/improve-dd.biotools.json
new file mode 100644
index 0000000000000..72ab80dd4cb35
--- /dev/null
+++ b/data/improve-dd/improve-dd.biotools.json
@@ -0,0 +1,125 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-23T08:52:08.315104Z",
+    "biotoolsCURIE": "biotools:improve-dd",
+    "biotoolsID": "improve-dd",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "s.aitken@ed.ac.uk",
+            "name": "Stuart Aitken",
+            "orcidid": "http://orcid.org/0000-0003-4867-4568",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Caroline F Wright",
+            "orcidid": "http://orcid.org/0000-0003-2958-5076"
+        },
+        {
+            "name": "Colin A. Semple",
+            "orcidid": "http://orcid.org/0000-0003-1765-4118"
+        },
+        {
+            "name": "David R FitzPatrick",
+            "orcidid": "http://orcid.org/0000-0003-4861-969X"
+        },
+        {
+            "name": "Helen V Firth",
+            "orcidid": "http://orcid.org/0000-0002-6410-0882"
+        },
+        {
+            "name": "Matthew E Hurles",
+            "orcidid": "http://orcid.org/0000-0002-2333-7015"
+        }
+    ],
+    "description": "Integrating Multiple Phenotype Resources Optimises Variant Evaluation in genetically determined Developmental Disorders.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Variant prioritisation",
+                    "uri": "http://edamontology.org/operation_3226"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Stuart-Aitken/IMPROVE-DD",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-23T08:52:08.318739Z",
+    "license": "GPL-3.0",
+    "name": "IMPROVE-DD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.xhgg.2022.100162",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsDiagnosing rare developmental disorders using genome-wide sequencing data commonly necessitates review of multiple plausible candidate variants, often using ontologies of categorical clinical terms. We show that Integrating Multiple Phenotype Resources Optimizes Variant Evaluation in Developmental Disorders (IMPROVE-DD) by incorporating additional classes of data commonly available to clinicians and recorded in health records. In doing so, we quantify the distinct contributions of sex, growth, and development in addition to Human Phenotype Ontology (HPO) terms and demonstrate added value from these readily available information sources. We use likelihood ratios for nominal and quantitative data and propose a classifier for HPO terms in this framework. This Bayesian framework results in more robust diagnoses. Using data systematically collected in the Deciphering Developmental Disorders study, we considered 77 genes with pathogenic/likely pathogenic variants in ≥10 individuals. All genes showed at least a satisfactory prediction by receiver operating characteristic when testing on training data (AUC ≥ 0.6), and HPO terms were the best predictor for the majority of genes, though a minority (13/77) of genes were better predicted by other phenotypic data types. Overall, classifiers based upon multiple integrated phenotypic data sources performed better than those based upon any individual source, and importantly, integrated models produced notably fewer false positives. Finally, we show that IMPROVE-DD models with good predictive performance on cross-validation can be constructed from relatively few individuals. This suggests new strategies for candidate gene prioritization and highlights the value of systematic clinical data collection to support diagnostic programs.",
+                "authors": [
+                    {
+                        "name": "Aitken S."
+                    },
+                    {
+                        "name": "Firth H.V."
+                    },
+                    {
+                        "name": "FitzPatrick D.R."
+                    },
+                    {
+                        "name": "Hurles M.E."
+                    },
+                    {
+                        "name": "Semple C.A."
+                    },
+                    {
+                        "name": "Wright C.F."
+                    }
+                ],
+                "date": "2023-01-12T00:00:00Z",
+                "journal": "Human Genetics and Genomics Advances",
+                "title": "IMPROVE-DD: Integrating multiple phenotype resources optimizes variant evaluation in genetically determined developmental disorders"
+            },
+            "pmcid": "PMC9763511",
+            "pmid": "36561149"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Neurology",
+            "uri": "http://edamontology.org/topic_3334"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        }
+    ]
+}
diff --git a/data/indelgt/indelgt.biotools.json b/data/indelgt/indelgt.biotools.json
new file mode 100644
index 0000000000000..a49b9e4f1c7ee
--- /dev/null
+++ b/data/indelgt/indelgt.biotools.json
@@ -0,0 +1,123 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-24T21:59:37.874920Z",
+    "biotoolsCURIE": "biotools:indelgt",
+    "biotoolsID": "indelgt",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tongchf@njfu.edu.cn",
+            "name": "Chunfa Tong",
+            "orcidid": "https://orcid.org/0000-0001-9795-211X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jinpeng Zhang",
+            "orcidid": "https://orcid.org/0000-0002-3007-1139"
+        },
+        {
+            "name": "Shengjun Bai",
+            "orcidid": "https://orcid.org/0000-0003-1343-8944"
+        },
+        {
+            "name": "Zhiliang Pan",
+            "orcidid": "https://orcid.org/0000-0002-7973-0588"
+        },
+        {
+            "name": "Zhiting Li",
+            "orcidid": "https://orcid.org/0000-0002-0854-9538"
+        }
+    ],
+    "description": "An integrated pipeline for extracting indel genotypes for genetic mapping in a hybrid population using next-generation sequencing data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genetic mapping",
+                    "uri": "http://edamontology.org/operation_0282"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Indel detection",
+                    "uri": "http://edamontology.org/operation_0452"
+                },
+                {
+                    "term": "PCR primer design",
+                    "uri": "http://edamontology.org/operation_0308"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/tongchf/InDelGT",
+    "lastUpdate": "2023-02-24T21:59:37.877538Z",
+    "license": "Not licensed",
+    "name": "InDelGT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/APS3.11499",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Applications in Plant Sciences published by Wiley Periodicals LLC on behalf of Botanical Society of America.Premise: Although several software packages are available for genotyping insertion/deletion (indel) polymorphisms in genomes using next-generation sequencing data, simultaneously calling indel genotypes across many individuals for use in genetic mapping remains challenging. Methods and Results: We present an integrated pipeline, InDelGT, for the extraction of indel genotypes from a segregating population such as backcross or F2 lines, or from an F1 cross between outbred species. The InDelGT algorithm is implemented in three steps: generating an indel catalog, calling indel genotypes, and analyzing indel segregation. We demonstrated the use of the pipeline with an example data set from an F1 hybrid population of Populus and successfully constructed the two parental genetic linkage maps. Conclusions: InDelGT is a practical tool that can quickly genotype a large number of indel markers within a population following Mendelian segregation. The InDelGT pipeline is freely available on GitHub (https://github.com/tongchf/InDelGT).",
+                "authors": [
+                    {
+                        "name": "Bai S."
+                    },
+                    {
+                        "name": "Li Z."
+                    },
+                    {
+                        "name": "Pan Z."
+                    },
+                    {
+                        "name": "Tong C."
+                    },
+                    {
+                        "name": "Zhang J."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Applications in Plant Sciences",
+                "title": "InDelGT: An integrated pipeline for extracting indel genotypes for genetic mapping in a hybrid population using next-generation sequencing data"
+            },
+            "pmcid": "PMC9742820",
+            "pmid": "36518944"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/indelsrnamute/indelsrnamute.biotools.json b/data/indelsrnamute/indelsrnamute.biotools.json
new file mode 100644
index 0000000000000..1af30d9fd5cab
--- /dev/null
+++ b/data/indelsrnamute/indelsrnamute.biotools.json
@@ -0,0 +1,96 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T19:28:10.472421Z",
+    "biotoolsCURIE": "biotools:indelsrnamute",
+    "biotoolsID": "indelsrnamute",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "alexach3@sce.ac.il",
+            "name": "Alexander Churkin",
+            "orcidid": "https://orcid.org/0000-0003-4275-257X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Danny Barash"
+        },
+        {
+            "name": "Yann Ponty"
+        }
+    ],
+    "description": "Predicting deleterious multiple point substitutions and indels mutations.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "RNA secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0278"
+                },
+                {
+                    "term": "RNA structure prediction",
+                    "uri": "http://edamontology.org/operation_2441"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.cs.bgu.ac.il/~dbarash/Churkin/SCE/IndelsRNAmute/",
+    "lastUpdate": "2022-12-29T19:28:10.476914Z",
+    "name": "IndelsRNAmute",
+    "operatingSystem": [
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-04943-0",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: RNA deleterious point mutation prediction was previously addressed with programs such as RNAmute and MultiRNAmute. The purpose of these programs is to predict a global conformational rearrangement of the secondary structure of a functional RNA molecule, thereby disrupting its function. RNAmute was designed to deal with only single point mutations in a brute force manner, while in MultiRNAmute an efficient approach to deal with multiple point mutations was developed. The approach used in MultiRNAmute is based on the stabilization of the suboptimal RNA folding prediction solutions and/or destabilization of the optimal folding prediction solution of the wild type RNA molecule. The MultiRNAmute algorithm is significantly more efficient than the brute force approach in RNAmute, but in the case of long sequences and large m-point mutation sets the MultiRNAmute becomes exponential in examining all possible stabilizing and destabilizing mutations. Results: An inherent limitation in the RNAmute and MultiRNAmute programs is their ability to predict only substitution mutations, as these programs were not designed to work with deletion or insertion mutations. To address this limitation we herein develop a very fast algorithm, based on suboptimal folding solutions, to predict a predefined number of multiple point deleterious mutations as specified by the user. Depending on the user’s choice, each such set of mutations may contain combinations of deletions, insertions and substitution mutations. Additionally, we prove the hardness of predicting the most deleterious set of point mutations in structural RNAs. Conclusions: We developed a method that extends our previous MultiRNAmute method to predict insertion and deletion mutations in addition to substitutions. The additional advantage of the new method is its efficiency to find a predefined number of deleterious mutations. Our new method may be exploited by biologists and virologists prior to site-directed mutagenesis experiments, which involve indel mutations along with substitutions. For example, our method may help to investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary structure.",
+                "authors": [
+                    {
+                        "name": "Barash D."
+                    },
+                    {
+                        "name": "Churkin A."
+                    },
+                    {
+                        "name": "Ponty Y."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "IndelsRNAmute: predicting deleterious multiple point substitutions and indels mutations"
+            },
+            "pmcid": "PMC9569039",
+            "pmid": "36241988"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Nucleic acid structure analysis",
+            "uri": "http://edamontology.org/topic_0097"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/inflect_cluster/inflect_cluster.biotools.json b/data/inflect_cluster/inflect_cluster.biotools.json
new file mode 100644
index 0000000000000..a23d1c49e8d44
--- /dev/null
+++ b/data/inflect_cluster/inflect_cluster.biotools.json
@@ -0,0 +1,90 @@
+{
+    "additionDate": "2023-02-06T09:57:40.510170Z",
+    "biotoolsCURIE": "biotools:inflect_cluster",
+    "biotoolsID": "inflect_cluster",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "jj.garciavallejo@amsterdamumc.nl",
+            "name": "Juan J. Garcia-Vallejo",
+            "orcidid": "https://orcid.org/0000-0001-6238-7069",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "R-package designed to give insight in clustering results and provide an optimal number of clusters",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/jnverhoeff/GarciaVallejoLab",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-06T09:57:40.512744Z",
+    "license": "GPL-3.0",
+    "name": "INFLECT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05018-W",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Current methods of high-dimensional unsupervised clustering of mass cytometry data lack means to monitor and evaluate clustering results. Whether unsupervised clustering is correct is typically evaluated by agreement with dimensionality reduction techniques or based on benchmarking with manually classified cells. The ambiguity and lack of reproducibility of sequential gating has been replaced with ambiguity in interpretation of clustering results. On the other hand, spurious overclustering of data leads to loss of statistical power. We have developed INFLECT, an R-package designed to give insight in clustering results and provide an optimal number of clusters. In our approach, a mass cytometry dataset is overclustered intentionally to ensure the smallest phenotypically different subsets are captured using FlowSOM. A range of metacluster number endpoints are generated and evaluated using marker interquartile range and distribution unimodality checks. The fraction of marker distributions that pass these checks is taken as a measure of clustering success. The fraction of unimodal distributions within metaclusters is plotted against the number of generated metaclusters and reaches a plateau of diminishing returns. The inflection point at which this occurs gives an optimal point of capturing cellular heterogeneity versus statistical power. Results: We applied INFLECT to four publically available mass cytometry datasets of different size and number of markers. The unimodality score consistently reached a plateau, with an inflection point dependent on dataset size and number of dimensions. We tested both ConsenusClusterPlus metaclustering and hierarchical clustering. While hierarchical clustering is less computationally expensive and thus faster, it achieved similar results to ConsensusClusterPlus. The four datasets consisted of labeled data and we compared INFLECT metaclustering to published results. INFLECT identified a higher optimal number of metaclusters for all datasets. We illustrated the underlying heterogeneity within labels, showing that these labels encompass distinct types of cells. Conclusion: INFLECT addresses a knowledge gap in high-dimensional cytometry analysis, namely assessing clustering results. This is done through monitoring marker distributions for interquartile range and unimodality across a range of metacluster numbers. The inflection point is the optimal trade-off between cellular heterogeneity and statistical power, applied in this work for FlowSOM clustering on mass cytometry datasets.",
+                "authors": [
+                    {
+                        "name": "Abeln S."
+                    },
+                    {
+                        "name": "Garcia-Vallejo J.J."
+                    },
+                    {
+                        "name": "Verhoeff J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "INFLECT: an R-package for cytometry cluster evaluation using marker modality"
+            },
+            "pmcid": "PMC9670405",
+            "pmid": "36384426"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Cytometry",
+            "uri": "http://edamontology.org/topic_3934"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        }
+    ]
+}
diff --git a/data/inga/inga.biotools.json b/data/inga/inga.biotools.json
index 9a6c5c0e658b3..9b5acf339ce3b 100644
--- a/data/inga/inga.biotools.json
+++ b/data/inga/inga.biotools.json
@@ -101,8 +101,8 @@
             ]
         }
     ],
-    "homepage": "http://protein.bio.unipd.it/inga",
-    "lastUpdate": "2019-01-11T00:54:18Z",
+    "homepage": "https://inga.bio.unipd.it/",
+    "lastUpdate": "2023-02-27T13:55:57.791654Z",
     "name": "INGA",
     "operatingSystem": [
         "Linux",
@@ -132,7 +132,7 @@
                         "name": "Tosatto S.C.E."
                     }
                 ],
-                "citationCount": 48,
+                "citationCount": 59,
                 "date": "2015-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "INGA: Protein function prediction combining interaction networks, domain assignments and sequence similarity"
@@ -156,6 +156,6 @@
     ],
     "validated": 1,
     "version": [
-        "1.1"
+        "2"
     ]
 }
diff --git a/data/inpactor2/inpactor2.biotools.json b/data/inpactor2/inpactor2.biotools.json
new file mode 100644
index 0000000000000..a7597c50f2993
--- /dev/null
+++ b/data/inpactor2/inpactor2.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-24T21:52:15.910197Z",
+    "biotoolsCURIE": "biotools:inpactor2",
+    "biotoolsID": "inpactor2",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "gustavo.isaza@ucaldas.edu.co",
+            "name": "Gustavo Isaza",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "paschoal@utfpr.edu.br",
+            "name": "Alexandre Rossi Paschoal",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "romain.guyot@ird.fr",
+            "name": "Romain Guyot",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "simon.orozco.arias@gmail.com",
+            "name": "Simon Orozco-Arias",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A software based on deep learning to identify and classify LTR-retrotransposons in plant genomes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "DNA sequence",
+                        "uri": "http://edamontology.org/data_3494"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Filtering",
+                    "uri": "http://edamontology.org/operation_3695"
+                },
+                {
+                    "term": "Genome assembly",
+                    "uri": "http://edamontology.org/operation_0525"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/simonorozcoarias/Inpactor2",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-24T21:52:15.912627Z",
+    "license": "GPL-3.0",
+    "name": "Inpactor2",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC511",
+            "pmcid": "PMC9851300",
+            "pmid": "36502372"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biodiversity",
+            "uri": "http://edamontology.org/topic_3050"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Phylogeny",
+            "uri": "http://edamontology.org/topic_0084"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/insistc/insistc.biotools.json b/data/insistc/insistc.biotools.json
new file mode 100644
index 0000000000000..599119a5a40cf
--- /dev/null
+++ b/data/insistc/insistc.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-23T08:59:25.624524Z",
+    "biotoolsCURIE": "biotools:insistc",
+    "biotoolsID": "insistc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Haiyan Hu"
+        },
+        {
+            "name": "Hansi Zheng"
+        },
+        {
+            "name": "Saidi Wang"
+        },
+        {
+            "name": "Xiaoman Li"
+        }
+    ],
+    "description": "Incorporating Network Structure Information for Single-Cell Type Classification.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "http://hulab.ucf.edu/research/projects/INSISTC/INSISTC_Manual.txt"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene regulatory network analysis",
+                    "uri": "http://edamontology.org/operation_1781"
+                },
+                {
+                    "term": "Gene regulatory network prediction",
+                    "uri": "http://edamontology.org/operation_2437"
+                },
+                {
+                    "term": "Structure classification",
+                    "uri": "http://edamontology.org/operation_2996"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://hulab.ucf.edu/research/projects/INSISTC/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-23T08:59:25.627189Z",
+    "license": "Not licensed",
+    "name": "INSISTC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.ygeno.2022.110480",
+            "metadata": {
+                "abstract": "© 2022Uncovering gene regulatory mechanisms in individual cells can provide insight into cell heterogeneity and function. Recent accumulated Single-Cell RNA-Seq data have made it possible to analyze gene regulation at single-cell resolution. Understanding cell-type-specific gene regulation can assist in more accurate cell type and state identification. Computational approaches utilizing such relationships are under development. Methods pioneering in integrating gene regulatory mechanism discovery with cell-type classification encounter challenges such as determine gene regulatory relationships and incorporate gene regulatory network structure. To fill this gap, we developed INSISTC, a computational method to incorporate gene regulatory network structure information for single-cell type classification. INSISTC is capable of identifying cell-type-specific gene regulatory mechanisms while performing single-cell type classification. INSISTC demonstrated its accuracy in cell type classification and its potential for providing insight into molecular mechanisms specific to individual cells. In comparison with the alternative methods, INSISTC demonstrated its complementary performance for gene regulation interpretation.",
+                "authors": [
+                    {
+                        "name": "Hu H."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Zheng H."
+                    }
+                ],
+                "date": "2022-09-01T00:00:00Z",
+                "journal": "Genomics",
+                "title": "INSISTC: Incorporating network structure information for single-cell type classification"
+            },
+            "pmid": "36075505"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Gene regulation",
+            "uri": "http://edamontology.org/topic_0204"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        }
+    ]
+}
diff --git a/data/inspire/inspire.biotools.json b/data/inspire/inspire.biotools.json
new file mode 100644
index 0000000000000..b96436f9f9add
--- /dev/null
+++ b/data/inspire/inspire.biotools.json
@@ -0,0 +1,122 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-24T12:00:04.315773Z",
+    "biotoolsCURIE": "biotools:inspire",
+    "biotoolsID": "inspire",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jliepe@mpinat.mpg.de",
+            "name": "Juliane Liepe",
+            "orcidid": "https://orcid.org/0000-0003-2515-9707",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "michele.mishto@kcl.ac.uk",
+            "name": "Michele Mishto",
+            "orcidid": "https://orcid.org/0000-0003-3042-2792",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "John A. Cormican",
+            "orcidid": "https://orcid.org/0000-0001-5339-7177"
+        },
+        {
+            "name": "Wai Tuck Soh",
+            "orcidid": "https://orcid.org/0000-0003-0082-7983"
+        },
+        {
+            "name": "Yehor Horokhovskyi",
+            "orcidid": "https://orcid.org/0000-0002-6553-9619"
+        }
+    ],
+    "description": "An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Spectral library search",
+                    "uri": "http://edamontology.org/operation_3801"
+                },
+                {
+                    "term": "Tag-based peptide identification",
+                    "uri": "http://edamontology.org/operation_3643"
+                },
+                {
+                    "term": "Target-Decoy",
+                    "uri": "http://edamontology.org/operation_3649"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://figshare.com/articles/software/inSPIRE_Models/20368035",
+    "lastUpdate": "2023-02-24T12:00:04.318283Z",
+    "license": "Apache-2.0",
+    "name": "inSPIRE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.MCPRO.2022.100432",
+            "metadata": {
+                "abstract": "© 2022 THE AUTHORS.Rescoring of mass spectrometry (MS) search results using spectral predictors can strongly increase peptide spectrum match (PSM) identification rates. This approach is particularly effective when aiming to search MS data against large databases, for example, when dealing with nonspecific cleavage in immunopeptidomics or inflation of the reference database for noncanonical peptide identification. Here, we present inSPIRE (in silico Spectral Predictor Informed REscoring), a flexible and performant open-source rescoring pipeline built on Prosit MS spectral prediction, which is compatible with common database search engines. inSPIRE allows large-scale rescoring with data from multiple MS search files, increases sensitivity to minor differences in amino acid residue position, and can be applied to various MS sample types, including tryptic proteome digestions and immunopeptidomes. inSPIRE boosts PSM identification rates in immunopeptidomics, leading to better performance than the original Prosit rescoring pipeline, as confirmed by benchmarking of inSPIRE performance on ground truth datasets. The integration of various features in the inSPIRE backbone further boosts the PSM identification in immunopeptidomics, with a potential benefit for the identification of noncanonical peptides.",
+                "authors": [
+                    {
+                        "name": "Cormican J.A."
+                    },
+                    {
+                        "name": "Horokhovskyi Y."
+                    },
+                    {
+                        "name": "Liepe J."
+                    },
+                    {
+                        "name": "Mishto M."
+                    },
+                    {
+                        "name": "Soh W.T."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Molecular and Cellular Proteomics",
+                "title": "inSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction"
+            },
+            "pmcid": "PMC9720494",
+            "pmid": "36280141"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Proteogenomics",
+            "uri": "http://edamontology.org/topic_3922"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/introverse/introverse.biotools.json b/data/introverse/introverse.biotools.json
new file mode 100644
index 0000000000000..f528cc8401177
--- /dev/null
+++ b/data/introverse/introverse.biotools.json
@@ -0,0 +1,127 @@
+{
+    "additionDate": "2023-02-06T10:04:04.346670Z",
+    "biotoolsCURIE": "biotools:introverse",
+    "biotoolsID": "introverse",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mina.ryten@ucl.ac.uk",
+            "name": "Mina Ryten",
+            "orcidid": "https://orcid.org/0000-0001-9520-6957",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A comprehensive database of introns across human tissues.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Sequence merging",
+                    "uri": "http://edamontology.org/operation_0232"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://rytenlab.com/browser/app/introverse",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-06T10:04:04.349060Z",
+    "license": "Other",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://hub.docker.com/r/soniaruiz/introverse"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://zenodo.org/record/6869186"
+        }
+    ],
+    "name": "IntroVerse",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1056",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Dysregulation of RNA splicing contributes to both rare and complex diseases. RNA-sequencing data from human tissues has shown that this process can be inaccurate, resulting in the presence of novel introns detected at low frequency across samples and within an individual. To enable the full spectrum of intron use to be explored, we have developed IntroVerse, which offers an extensive catalogue on the splicing of 332,571 annotated introns and a linked set of 4,679,474 novel junctions covering 32,669 different genes. This dataset has been generated through the analysis of 17,510 human control RNA samples from 54 tissues provided by the Genotype-Tissue Expression Consortium. IntroVerse has two unique features: (i) it provides a complete catalogue of novel junctions and (ii) each novel junction has been assigned to a specific annotated intron. This unique, hierarchical structure offers multiple uses, including the identification of novel transcripts from known genes and their tissue-specific usage, and the assessment of background splicing noise for introns thought to be mis-spliced in disease states. IntroVerse provides a user-friendly web interface and is freely available at https://rytenlab.com/browser/app/introverse.",
+                "authors": [
+                    {
+                        "name": "Botia J.A."
+                    },
+                    {
+                        "name": "Chen Z."
+                    },
+                    {
+                        "name": "Collado-Torres L."
+                    },
+                    {
+                        "name": "Fairbrother-Browne A."
+                    },
+                    {
+                        "name": "Garcia-Ruiz S."
+                    },
+                    {
+                        "name": "Gil-Martinez A.L."
+                    },
+                    {
+                        "name": "Gustavsson E.K."
+                    },
+                    {
+                        "name": "Reynolds R.H."
+                    },
+                    {
+                        "name": "Ryten M."
+                    },
+                    {
+                        "name": "Zhang D."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "IntroVerse: a comprehensive database of introns across human tissues"
+            },
+            "pmcid": "PMC9825543",
+            "pmid": "36399497"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Rare diseases",
+            "uri": "http://edamontology.org/topic_3325"
+        }
+    ]
+}
diff --git a/data/iofs-sa/iofs-sa.biotools.json b/data/iofs-sa/iofs-sa.biotools.json
new file mode 100644
index 0000000000000..02db726d5e84b
--- /dev/null
+++ b/data/iofs-sa/iofs-sa.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T19:22:56.204141Z",
+    "biotoolsCURIE": "biotools:iofs-sa",
+    "biotoolsID": "iofs-sa",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Guohua Wang"
+        },
+        {
+            "name": "Tong Liu"
+        },
+        {
+            "name": "Youlin Wu"
+        },
+        {
+            "name": "Xudong Zhao",
+            "orcidid": "https://orcid.org/0000-0003-2272-6278"
+        },
+        {
+            "name": "Yuanyuan He",
+            "orcidid": "https://orcid.org/0000-0002-7305-5120"
+        }
+    ],
+    "description": "An interactive online feature selection tool for survival analysis.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature selection",
+                    "uri": "http://edamontology.org/operation_3936"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://bioinfor.nefu.edu.cn/IOFS-SA/",
+    "language": [
+        "JavaScript",
+        "Python"
+    ],
+    "lastUpdate": "2022-12-29T19:22:56.208147Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/Yuan-23/IOFS-SA-ecp-data-main"
+        }
+    ],
+    "name": "IOFS-SA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106121",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdBackground: Survival analysis is a primary problem before clinical treatments to cancer patients after their operations. In order to make this kind of analysis simple, many corresponding tools have been proposed. Though these tools are easy to use, there exist still two fatal flaws. One is that sample grouping is commonly empirical and wrongly based on original gene expressions or survival time. The other is that their feature selection methods mostly depend univariate semi-supervised regression or the multivariate one without considering the small sample size compared with the high dimension. Objective: In order to solve the two problems, we design an automatic feature selection web tool which can also satisfy interactive sample grouping. Methods: An automatic feature selection is performed on user-defined data or TCGA data. users can also perform manual feature selection. Then, hierarchical clustering is used and an automatic re-clustering strategy is proposed after interactive risk score split. Kaplan–Meier survival curve and log-rank test are utilized as the measurement. Results: Experimental results on 53 datasets from TCGA demonstrate the effectiveness of our method. The tree view, heat map and scatter map can intuitively display the result of the selected genes to the doctors for further research. Conclusions: This method is suitable for survival analysis of high-dimensional small sample data sets. At the same time, it also provides a platform for researchers to analyze custom data. It solves the problems of the existing web tools and provides an effective feature selection method for survival analysis. Availability: The full code package is freely available and can be downloaded at https://github.com/Yuan-23/IOFS-SA-ecp-data-main, and the online version at https://bioinfor.nefu.edu.cn/IOFS-SA/ is ready for use freely.",
+                "authors": [
+                    {
+                        "name": "He Y."
+                    },
+                    {
+                        "name": "Liu T."
+                    },
+                    {
+                        "name": "Wang G."
+                    },
+                    {
+                        "name": "Wu Y."
+                    },
+                    {
+                        "name": "Zhao X."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "IOFS-SA: An interactive online feature selection tool for survival analysis"
+            },
+            "pmid": "36201885"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/iorbase/iorbase.biotools.json b/data/iorbase/iorbase.biotools.json
new file mode 100644
index 0000000000000..168fc1b33305c
--- /dev/null
+++ b/data/iorbase/iorbase.biotools.json
@@ -0,0 +1,133 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-24T11:49:11.915109Z",
+    "biotoolsCURIE": "biotools:iorbase",
+    "biotoolsID": "iorbase",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Gang Li"
+        },
+        {
+            "name": "Qian Li"
+        },
+        {
+            "name": "Yi-Feng Zhang"
+        },
+        {
+            "name": "Hui-Meng Lu",
+            "orcidid": "https://orcid.org/0000-0002-5334-1402"
+        }
+    ],
+    "description": "A database for the prediction of the structures and functions of insect olfactory receptors.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                },
+                {
+                    "term": "Protein structure prediction",
+                    "uri": "http://edamontology.org/operation_0474"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.iorbase.com",
+    "lastUpdate": "2023-02-24T11:49:11.917669Z",
+    "name": "iORbase",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1111/1744-7917.13162",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Insect Science published by John Wiley & Sons Australia, Ltd on behalf of Institute of Zoology, Chinese Academy of Sciences.Insect olfactory receptors (iORs) with atypical 7-transmembrane domains, unlike Chordata olfactory receptors, are not in the GPCR protein family. iORs selectively bind to volatile ligands in the environment and affect essential insect behaviors. In this study, we constructed a new platform (iORbase, https://www.iorbase.com) for the structural and functional analysis of iORs based on a combined algorithm for gene annotation and protein structure prediction. Moreover, it provides the option to calculate the binding affinities and binding residues between iORs and pheromone molecules by virtual screening of docking. Furthermore, iORbase supports the automatic structural and functional prediction of user-submitted iORs or pheromones. iORbase contains the well-analyzed results of approximately 6 000 iORs and their 3D protein structures identified from 59 insect species and 2 077 insect pheromones from the literature, as well as approximately 12 million pairs of simulated interactions between functional iORs and pheromones. We also built 4 online modules, iORPDB, iInteraction, iModelTM, and iOdorTool to easily retrieve and visualize the 3D structures and interactions. iORbase can help greatly improve the experimental efficiency and success rate, identify new insecticide targets, or develop electronic nose technology. This study will shed light on the olfactory recognition mechanism and evolutionary characteristics from the perspectives of omics and macroevolution.",
+                "authors": [
+                    {
+                        "name": "Huang Y."
+                    },
+                    {
+                        "name": "Li G."
+                    },
+                    {
+                        "name": "Li Q."
+                    },
+                    {
+                        "name": "Lu H.-M."
+                    },
+                    {
+                        "name": "Wan J.-H."
+                    },
+                    {
+                        "name": "Xu C."
+                    },
+                    {
+                        "name": "Yang H."
+                    },
+                    {
+                        "name": "Zhang T.-M."
+                    },
+                    {
+                        "name": "Zhang Y.-D."
+                    },
+                    {
+                        "name": "Zhang Y.-F."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Insect Science",
+                "title": "iORbase: A database for the prediction of the structures and functions of insect olfactory receptors"
+            },
+            "pmid": "36519267"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Gene and protein families",
+            "uri": "http://edamontology.org/topic_0623"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Protein structure analysis",
+            "uri": "http://edamontology.org/topic_2814"
+        }
+    ]
+}
diff --git a/data/ipida-gcn/ipida-gcn.biotools.json b/data/ipida-gcn/ipida-gcn.biotools.json
new file mode 100644
index 0000000000000..81baa76a87c0b
--- /dev/null
+++ b/data/ipida-gcn/ipida-gcn.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T19:16:01.854806Z",
+    "biotoolsCURIE": "biotools:ipida-gcn",
+    "biotoolsID": "ipida-gcn",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "bliu@bliulab.net",
+            "name": "Bin Liu",
+            "orcidid": "https://orcid.org/0000-0002-8520-8374",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Hang Wei"
+        },
+        {
+            "name": "Jialu Hou"
+        }
+    ],
+    "description": "Identification of piRNA-disease associations based on Graph Convolutional Network.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bliulab.net/iPiDA-GCN/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-29T19:16:01.857590Z",
+    "license": "Not licensed",
+    "name": "iPiDA-GCN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PCBI.1010671",
+            "metadata": {
+                "abstract": "Copyright: © 2022 Hou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Motivation Piwi-interacting RNAs (piRNAs) play a critical role in the progression of various diseases. Accurately identifying the associations between piRNAs and diseases is important for diagnosing and prognosticating diseases. Although some computational methods have been proposed to detect piRNA-disease associations, it is challenging for these methods to effectively capture nonlinear and complex relationships between piRNAs and diseases because of the limited training data and insufficient association representation. Results With the growth of piRNA-disease association data, it is possible to design a more complex machine learning method to solve this problem. In this study, we propose a computational method called iPiDA-GCN for piRNA-disease association identification based on graph convolutional networks (GCNs). The iPiDA-GCN predictor constructs the graphs based on piRNA sequence information, disease semantic information and known piRNA-disease associations. Two GCNs (Asso-GCN and Sim-GCN) are used to extract the features of both piRNAs and diseases by capturing the association patterns from piRNA-disease interaction network and two similarity networks. GCNs can capture complex network structure information from these networks, and learn discriminative features. Finally, the full connection networks and inner production are utilized as the output module to predict piRNA-disease association scores. Experimental results demonstrate that iPiDA-GCN achieves better performance than the other state-of-the-art methods, benefitted from the discriminative features extracted by Asso-GCN and Sim-GCN. The iPiDA-GCN predictor is able to detect new piRNA-disease associations to reveal the potential pathogenesis at the RNA level. The data and source code are available at http://bliulab.net/iPiDA-GCN/.",
+                "authors": [
+                    {
+                        "name": "Hou J."
+                    },
+                    {
+                        "name": "Liu B."
+                    },
+                    {
+                        "name": "Wei H."
+                    }
+                ],
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network"
+            },
+            "pmcid": "PMC9662734",
+            "pmid": "36301998"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Cardiology",
+            "uri": "http://edamontology.org/topic_3335"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        }
+    ]
+}
diff --git a/data/ippf_fe/ippf_fe.biotools.json b/data/ippf_fe/ippf_fe.biotools.json
new file mode 100644
index 0000000000000..3b5540f7b739a
--- /dev/null
+++ b/data/ippf_fe/ippf_fe.biotools.json
@@ -0,0 +1,75 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T14:22:19.350023Z",
+    "biotoolsCURIE": "biotools:ippf_fe",
+    "biotoolsID": "ippf_fe",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Xiaozhou Luo"
+        }
+    ],
+    "description": "An integrated peptide and protein function prediction framework based on fused features and ensemble models.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein feature detection",
+                    "uri": "http://edamontology.org/operation_3092"
+                },
+                {
+                    "term": "Protein function prediction",
+                    "uri": "http://edamontology.org/operation_1777"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Luo-SynBioLab/IPPF-FE",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-08T14:22:19.352899Z",
+    "license": "Not licensed",
+    "name": "IPPF-FE",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC476",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.The prediction of peptide and protein function is important for research and industrial applications, and many machine learning methods have been developed for this purpose. The existing models have encountered many challenges, including the lack of effective and comprehensive features and the limited applicability of each model. Here, we introduce an Integrated Peptide and Protein function prediction Framework based on Fused features and Ensemble models (IPPF-FE), which can accurately capture the relationship between features and labels. The results indicated that IPPF-FE outperformed existing state-of-the-art (SOTA) models on more than 8 different categories of peptide and protein tasks. In addition, t-distributed Stochastic Neighbour Embedding demonstrated the advantages of IPPF-FE. We anticipate that our method will become a versatile tool for peptide and protein prediction tasks and shed light on the future development of related models. The model is open source and available in the GitHub repository https://github.com/Luo-SynBioLab/IPPF-FE.",
+                "authors": [
+                    {
+                        "name": "Luo X."
+                    },
+                    {
+                        "name": "Yu H."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "IPPF-FE: an integrated peptide and protein function prediction framework based on fused features and ensemble models"
+            },
+            "pmid": "36403184"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Function analysis",
+            "uri": "http://edamontology.org/topic_1775"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/iprom_phage/iprom_phage.biotools.json b/data/iprom_phage/iprom_phage.biotools.json
new file mode 100644
index 0000000000000..54b650b60736e
--- /dev/null
+++ b/data/iprom_phage/iprom_phage.biotools.json
@@ -0,0 +1,96 @@
+{
+    "additionDate": "2023-02-08T14:25:13.210188Z",
+    "biotoolsCURIE": "biotools:iprom_phage",
+    "biotoolsID": "iprom_phage",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hilaltayara@jbnu.ac.kr",
+            "name": "Hilal Tayara",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "kitchong@jbnu.ac.kr",
+            "name": "Kil To Chong",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A two-layer model to identify phage promoters and their types using a convolutional neural network.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Promoter prediction",
+                    "uri": "http://edamontology.org/operation_0440"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://nsclbio.jbnu.ac.kr/tools/iProm-phage/",
+    "lastUpdate": "2023-02-23T15:55:25.458695Z",
+    "license": "Other",
+    "name": "iProm-phage",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FMICB.2022.1061122",
+            "metadata": {
+                "abstract": "Copyright © 2022 Shujaat, Jin, Tayara and Chong.The increased interest in phages as antibacterial agents has resulted in a rise in the number of sequenced phage genomes, necessitating the development of user-friendly bioinformatics tools for genome annotation. A promoter is a DNA sequence that is used in the annotation of phage genomes. In this study we proposed a two layer model called “iProm-phage” for the prediction and classification of phage promoters. Model first layer identify query sequence as promoter or non-promoter and if the query sequence is predicted as promoter then model second layer classify it as phage or host promoter. Furthermore, rather than using non-coding regions of the genome as a negative set, we created a more challenging negative dataset using promoter sequences. The presented approach improves discrimination while decreasing the frequency of erroneous positive predictions. For feature selection, we investigated 10 distinct feature encoding approaches and utilized them with several machine-learning algorithms and a 1-D convolutional neural network model. We discovered that the one-hot encoding approach and the CNN model outperformed based on performance metrics. Based on the results of the 5-fold cross validation, the proposed predictor has a high potential. Furthermore, to make it easier for other experimental scientists to obtain the results they require, we set up a freely accessible and user-friendly web server at http://nsclbio.jbnu.ac.kr/tools/iProm-phage/.",
+                "authors": [
+                    {
+                        "name": "Chong K.T."
+                    },
+                    {
+                        "name": "Jin J.S."
+                    },
+                    {
+                        "name": "Shujaat M."
+                    },
+                    {
+                        "name": "Tayara H."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-11-04T00:00:00Z",
+                "journal": "Frontiers in Microbiology",
+                "title": "iProm-phage: A two-layer model to identify phage promoters and their types using a convolutional neural network"
+            },
+            "pmcid": "PMC9672459",
+            "pmid": "36406389"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/ipromoter-seqvec/ipromoter-seqvec.biotools.json b/data/ipromoter-seqvec/ipromoter-seqvec.biotools.json
new file mode 100644
index 0000000000000..af1c73fef87e1
--- /dev/null
+++ b/data/ipromoter-seqvec/ipromoter-seqvec.biotools.json
@@ -0,0 +1,142 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T19:09:12.090902Z",
+    "biotoolsCURIE": "biotools:ipromoter-seqvec",
+    "biotoolsID": "ipromoter-seqvec",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "binh.p.nguyen@vuw.ac.nz",
+            "name": "Binh P. Nguyen",
+            "orcidid": "https://orcid.org/0000-0001-6203-6664",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "susantorahardja@ieee.org",
+            "name": "Susanto Rahardja",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Quang H. Trinh"
+        },
+        {
+            "name": "Thanh-Hoang Nguyen-Vo"
+        }
+    ],
+    "description": "Identifying promoters using bidirectional long short-term memory and sequence-embedded features.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "DNA sequence",
+                        "uri": "http://edamontology.org/data_3494"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Promoter prediction",
+                    "uri": "http://edamontology.org/operation_0440"
+                },
+                {
+                    "term": "cis-regulatory element prediction",
+                    "uri": "http://edamontology.org/operation_0441"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://124.197.54.240:5001",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-29T19:09:12.094633Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/mldlproject/2022-iPromoter-Seqvec"
+        }
+    ],
+    "name": "iPromoter-Seqvec",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12864-022-08829-6",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Promoters, non-coding DNA sequences located at upstream regions of the transcription start site of genes/gene clusters, are essential regulatory elements for the initiation and regulation of transcriptional processes. Furthermore, identifying promoters in DNA sequences and genomes significantly contributes to discovering entire structures of genes of interest. Therefore, exploration of promoter regions is one of the most imperative topics in molecular genetics and biology. Besides experimental techniques, computational methods have been developed to predict promoters. In this study, we propose iPromoter-Seqvec – an efficient computational model to predict TATA and non-TATA promoters in human and mouse genomes using bidirectional long short-term memory neural networks in combination with sequence-embedded features extracted from input sequences. The promoter and non-promoter sequences were retrieved from the Eukaryotic Promoter database and then were refined to create four benchmark datasets. Results: The area under the receiver operating characteristic curve (AUCROC) and the area under the precision-recall curve (AUCPR) were used as two key metrics to evaluate model performance. Results on independent test sets showed that iPromoter-Seqvec outperformed other state-of-the-art methods with AUCROC values ranging from 0.85 to 0.99 and AUCPR values ranging from 0.86 to 0.99. Models predicting TATA promoters in both species had slightly higher predictive power compared to those predicting non-TATA promoters. With a novel idea of constructing artificial non-promoter sequences based on promoter sequences, our models were able to learn highly specific characteristics discriminating promoters from non-promoters to improve predictive efficiency. Conclusions: iPromoter-Seqvec is a stable and robust model for predicting both TATA and non-TATA promoters in human and mouse genomes. Our proposed method was also deployed as an online web server with a user-friendly interface to support research communities. Links to our source codes and web server are available at https://github.com/mldlproject/2022-iPromoter-Seqvec.",
+                "authors": [
+                    {
+                        "name": "Nguyen B.P."
+                    },
+                    {
+                        "name": "Nguyen L."
+                    },
+                    {
+                        "name": "Nguyen-Hoang P.-U."
+                    },
+                    {
+                        "name": "Nguyen-Vo T.-H."
+                    },
+                    {
+                        "name": "Rahardja S."
+                    },
+                    {
+                        "name": "Trinh Q.H."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Genomics",
+                "title": "iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features"
+            },
+            "pmcid": "PMC9531353",
+            "pmid": "36192696"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/irna-ac4c/irna-ac4c.biotools.json b/data/irna-ac4c/irna-ac4c.biotools.json
new file mode 100644
index 0000000000000..32a99030f6cd2
--- /dev/null
+++ b/data/irna-ac4c/irna-ac4c.biotools.json
@@ -0,0 +1,124 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-24T00:01:59.554575Z",
+    "biotoolsCURIE": "biotools:irna-ac4c",
+    "biotoolsID": "irna-ac4c",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Hao Lin"
+        },
+        {
+            "name": "Wei Su"
+        },
+        {
+            "name": "Xiao-Long Yu"
+        },
+        {
+            "name": "Yan-Wen Li"
+        }
+    ],
+    "description": "A novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "RNA sequence",
+                        "uri": "http://edamontology.org/data_3495"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Feature selection",
+                    "uri": "http://edamontology.org/operation_3936"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://lin-group.cn/server/iRNA-ac4C/",
+    "lastUpdate": "2023-02-24T00:01:59.556996Z",
+    "name": "iRNA-ac4C",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.IJBIOMAC.2022.11.299",
+            "metadata": {
+                "abstract": "© 2022 Elsevier B.V.RNA N4-acetylcytidine (ac4C) is the acetylation of cytidine at the nitrogen-4 position, which is a highly conserved RNA modification and involves a variety of biological processes. Hence, accurate identification of genome-wide ac4C sites is vital for understanding regulation mechanism of gene expression. In this work, a novel predictor, named iRNA-ac4C, was established to identify ac4C sites in human mRNA based on three feature extraction methods, including nucleotide composition, nucleotide chemical property, and accumulated nucleotide frequency. Subsequently, minimum-Redundancy-Maximum-Relevance combined with incremental feature selection strategies was utilized to select the optimal feature subset. According to the optimal feature subset, the best ac4C classification model was trained by gradient boosting decision tree with 10-fold cross-validation. The results of independent testing set indicated that our proposed method could produce encouraging generalization capabilities. For the convenience of other researchers, we established a user-friendly web server which is freely available at http://lin-group.cn/server/iRNA-ac4C/. We hope that the tool could provide guide for wet-experimental scholars.",
+                "authors": [
+                    {
+                        "name": "Gao D."
+                    },
+                    {
+                        "name": "Li Y.-W."
+                    },
+                    {
+                        "name": "Lin H."
+                    },
+                    {
+                        "name": "Liu X.-W."
+                    },
+                    {
+                        "name": "Ma C.-Y."
+                    },
+                    {
+                        "name": "Su W."
+                    },
+                    {
+                        "name": "Xie X.-Q."
+                    },
+                    {
+                        "name": "Yang H."
+                    },
+                    {
+                        "name": "Yu X.-L."
+                    },
+                    {
+                        "name": "Zulfiqar H."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "International Journal of Biological Macromolecules",
+                "title": "iRNA-ac4C: A novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA"
+            },
+            "pmid": "36470433"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/isnat/isnat.biotools.json b/data/isnat/isnat.biotools.json
new file mode 100644
index 0000000000000..d786d82db5724
--- /dev/null
+++ b/data/isnat/isnat.biotools.json
@@ -0,0 +1,166 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-23T23:55:35.125257Z",
+    "biotoolsCURIE": "biotools:isnat",
+    "biotoolsID": "isnat",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "rgiger@umich.edu",
+            "name": "Roman J Giger",
+            "orcidid": "https://orcid.org/0000-0002-2926-3336",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Daniel H Geschwind",
+            "orcidid": "https://orcid.org/0000-0003-2896-3450"
+        },
+        {
+            "name": "Gabriel Corfas",
+            "orcidid": "https://orcid.org/0000-0001-5412-9473"
+        },
+        {
+            "name": "Xiao-Feng Zhao",
+            "orcidid": "https://orcid.org/0000-0002-7574-7163"
+        }
+    ],
+    "description": "The injured sciatic nerve atlas (iSNAT), insights into the cellular and molecular basis of neural tissue degeneration and regeneration.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Demultiplexing",
+                    "uri": "http://edamontology.org/operation_3933"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cdb-rshiny.med.umich.edu/Giger_iSNAT/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-23T23:55:35.127817Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/GigerLab/iSNAT"
+        }
+    ],
+    "name": "iSNAT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.7554/ELIFE.80881",
+            "metadata": {
+                "abstract": "© Zhao, Huffman, Hafner et al.Upon trauma, the adult murine peripheral nervous system (PNS) displays a remarkable degree of spontaneous anatomical and functional regeneration. To explore extrinsic mechanisms of neural repair, we carried out single-cell analysis of naïve mouse sciatic nerve, peripheral blood mono-nuclear cells, and crushed sciatic nerves at 1 day, 3 days, and 7 days following injury. During the first week, monocytes and macrophages (Mo/Mac) rapidly accumulate in the injured nerve and undergo extensive metabolic reprogramming. Proinflammatory Mo/Mac with a high glycolytic flux dominate the early injury response and rapidly give way to inflammation resolving Mac, programmed toward oxidative phosphorylation. Nerve crush injury causes partial leakiness of the blood–nerve barrier, proliferation of endoneurial and perineurial stromal cells, and entry of opsonizing serum proteins. Micro-dissection of the nerve injury site and distal nerve, followed by single-cell RNA-sequencing, identified distinct immune compartments, triggered by mechanical nerve wounding and Wallerian degeneration, respectively. This finding was independently confirmed with Sarm1-/- mice, in which Wallerian degeneration is greatly delayed. Experiments with chimeric mice showed that wildtype immune cells readily enter the injury site in Sarm1-/- mice, but are sparse in the distal nerve, except for Mo. We used CellChat to explore intercellular communications in the naïve and injured PNS and report on hundreds of ligand–receptor interactions. Our longitudinal analysis represents a new resource for neural tissue regeneration, reveals location-specific immune microenvironments, and reports on large intercellular communication networks. To facilitate mining of scRNAseq datasets, we generated the injured sciatic nerve atlas (iSNAT): https://cdb-rshiny.med.umich.edu/Giger_iSNAT/.",
+                "authors": [
+                    {
+                        "name": "Athaiya M."
+                    },
+                    {
+                        "name": "Corfas G."
+                    },
+                    {
+                        "name": "Finneran M.C."
+                    },
+                    {
+                        "name": "Flynn C."
+                    },
+                    {
+                        "name": "Geschwind D.H."
+                    },
+                    {
+                        "name": "Giger R.J."
+                    },
+                    {
+                        "name": "Hafner H."
+                    },
+                    {
+                        "name": "Huffman L.D."
+                    },
+                    {
+                        "name": "Johnson C.N."
+                    },
+                    {
+                        "name": "Kalinski A.L."
+                    },
+                    {
+                        "name": "Kawaguchi R."
+                    },
+                    {
+                        "name": "Kohen R."
+                    },
+                    {
+                        "name": "Kohrman D."
+                    },
+                    {
+                        "name": "Passino R."
+                    },
+                    {
+                        "name": "Twiss J.L."
+                    },
+                    {
+                        "name": "Yang L.J.S."
+                    },
+                    {
+                        "name": "Zhao X.-F."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "eLife",
+                "title": "The injured sciatic nerve atlas (iSNAT), insights into the cellular and molecular basis of neural tissue degeneration and regeneration"
+            },
+            "pmcid": "PMC9829412",
+            "pmid": "36515985"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Immunology",
+            "uri": "http://edamontology.org/topic_0804"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/isnodi-lsgt/isnodi-lsgt.biotools.json b/data/isnodi-lsgt/isnodi-lsgt.biotools.json
new file mode 100644
index 0000000000000..0a58f471ef502
--- /dev/null
+++ b/data/isnodi-lsgt/isnodi-lsgt.biotools.json
@@ -0,0 +1,66 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T19:01:42.367195Z",
+    "biotoolsCURIE": "biotools:isnodi-lsgt",
+    "biotoolsID": "isnodi-lsgt",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Bin Liu"
+        },
+        {
+            "name": "Wenxiang Zhang"
+        }
+    ],
+    "description": "identifying snoRNA-disease associations based on local similarity constraint and global topological constraint.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "http://bliulab.net/iSnoDi-LSGT/",
+    "lastUpdate": "2022-12-29T19:01:42.369928Z",
+    "name": "iSnoDi-LSGT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1261/RNA.079325.122",
+            "metadata": {
+                "abstract": "© 2022 Zhang and Liu; Published by Cold Spring Harbor Laboratory Press for the RNA Society.Growing evidence proves that small nucleolar RNAs (snoRNAs) have important functions in various biological processes, the malfunction of which leads to the emergence and development of complex diseases. However, identifying snoRNA-disease associations is an ongoing challenging task due to the considerable time- and money-consuming biological experiments. Therefore, it is urgent to design efficient and economical methods for the identification of snoRNA-disease associations. In this regard, we propose a computational method named iSnoDi-LSGT, which utilizes snoRNA sequence similarity and disease similarity as local similarity constraints. The iSnoDi-LSGT predictor further employs network embedding technology to extract topological features of snoRNAs and diseases, based on which snoRNA topological similarity and disease topological similarity are calculated as global topological constraints. To the best of our knowledge, the iSnoDi-LSGT is the first computational method for snoRNA-disease association identification. The experimental results indicate that the iSnoDi-LSGT predictor can effectively predict unknown snoRNA-disease associations. The web server of the iSnoDi-LSGT predictor is freely available at http://bliulab.net/iSnoDi-LSGT.",
+                "authors": [
+                    {
+                        "name": "Liu B."
+                    },
+                    {
+                        "name": "Zhang W."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "RNA (New York, N.Y.)",
+                "title": "iSnoDi-LSGT: identifying snoRNA-disease associations based on local similarity constraints and global topological constraints"
+            },
+            "pmid": "36192132"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Laboratory techniques",
+            "uri": "http://edamontology.org/topic_3361"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/isomirdb/isomirdb.biotools.json b/data/isomirdb/isomirdb.biotools.json
new file mode 100644
index 0000000000000..b875b3fdf46ad
--- /dev/null
+++ b/data/isomirdb/isomirdb.biotools.json
@@ -0,0 +1,91 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T18:55:24.036795Z",
+    "biotoolsCURIE": "biotools:isomirdb",
+    "biotoolsID": "isomirdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "andreas.keller@ccb.uni-saarland.de",
+            "name": "Andreas Keller",
+            "orcidid": "https://orcid.org/0000-0002-5361-0895",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "ernesto.aparicio@ccb.uni-saarland.de",
+            "name": "Ernesto Aparicio-Puerta",
+            "orcidid": "https://orcid.org/0000-0002-3470-1425",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Pascal Hirsch"
+        },
+        {
+            "name": "Georges P Schmartz",
+            "orcidid": "https://orcid.org/0000-0002-9627-9223"
+        }
+    ],
+    "description": "A miRNA expression database with isoform resolution.\nisomiRdb stores miRNA and isomiR expression values for 42499 miRNA-seq samples collected from miRMaster, The Cancer Genome Atlas and Sequence Read Archive and uniformly processed from raw reads using sRNAbench .",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                },
+                {
+                    "term": "miRNA expression analysis",
+                    "uri": "http://edamontology.org/operation_3792"
+                },
+                {
+                    "term": "miRNA target prediction",
+                    "uri": "http://edamontology.org/operation_0463"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.ccb.uni-saarland.de/isomirdb",
+    "lastUpdate": "2022-12-29T18:55:24.039381Z",
+    "name": "isomiRdb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC884",
+            "pmid": "36243964"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/isomirtar/isomirtar.biotools.json b/data/isomirtar/isomirtar.biotools.json
new file mode 100644
index 0000000000000..fd7320b0fddd1
--- /dev/null
+++ b/data/isomirtar/isomirtar.biotools.json
@@ -0,0 +1,141 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T18:51:11.528332Z",
+    "biotoolsCURIE": "biotools:isomirtar",
+    "biotoolsID": "isomirtar",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "snersisyan@hse.ru",
+            "name": "Stepan Nersisyan",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Aleksandra Gorbonos"
+        },
+        {
+            "name": "Alexander Tonevitsky"
+        },
+        {
+            "name": "Maxim Shkurnikov"
+        }
+    ],
+    "description": "A comprehensive portal of pan-cancer 5'-isomiR targeting.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Disease name",
+                        "uri": "http://edamontology.org/data_3668"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Gene name",
+                        "uri": "http://edamontology.org/data_2299"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "miRNA target prediction",
+                    "uri": "http://edamontology.org/operation_0463"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://isomirtar.hse.ru",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2022-12-29T18:51:11.531183Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/s-a-nersisyan/isomiRTar"
+        }
+    ],
+    "name": "isomiRTar",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.7717/PEERJ.14205",
+            "metadata": {
+                "abstract": "Copyright 2022 Nersisyan et al.Inaccurate cleavage of pri- and pre-miRNA hairpins by Drosha and Dicer results in the generation of miRNA isoforms known as isomiRs. isomiRs with 50-end variations (50-isomiRs) create a new dimension in miRNA research since they have different seed regions and distinct targetomes. We developed isomiRTar (https://isomirtar.hse.ru)—a comprehensive portal that allows one to analyze expression profiles and targeting activity of 50-isomiRs in cancer. Using the Cancer Genome Atlas sequencing data, we compiled the list of 1022 50-isomiRs expressed in 9282 tumor samples across 31 cancer types. Sequences of these isomiRs were used to predict target genes with miRDB and TargetScan. The putative interactions were then subjected to the co-expression analysis in each cancer type to identify isomiR-target pairs supported by significant negative correlations. Downstream analysis of the data deposited in isomiRTar revealed both cancer-specific and cancer-conserved 50-isomiR expression landscapes. Pairs of isomiRs differing in one nucleotide shift from 50-end had poorly overlapping targetomes with the median Jaccard index of 0.06. The analysis of colorectal cancer 50-isomiR-mediated regulatory networks revealed promising candidate tumor suppressor isomiRs: hsamiR-203a-3p|+1, hsa-miR-192-5p|+1 and hsa-miR-148a-3p|0. In summary, we believe that isomiRTar will help researchers find novel mechanisms of isomiR-mediated gene silencing in different types of cancer.",
+                "authors": [
+                    {
+                        "name": "Gorbonos A."
+                    },
+                    {
+                        "name": "Makhonin A."
+                    },
+                    {
+                        "name": "Nersisyan S."
+                    },
+                    {
+                        "name": "Shkurnikov M."
+                    },
+                    {
+                        "name": "Tonevitsky A."
+                    },
+                    {
+                        "name": "Zhiyanov A."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-10-17T00:00:00Z",
+                "journal": "PeerJ",
+                "title": "isomiRTar: a comprehensive portal of pan-cancer 50-isomiR targeting"
+            },
+            "pmcid": "PMC9583861",
+            "pmid": "36275459"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/iup_bert/iup_bert.biotools.json b/data/iup_bert/iup_bert.biotools.json
new file mode 100644
index 0000000000000..019142f3937d2
--- /dev/null
+++ b/data/iup_bert/iup_bert.biotools.json
@@ -0,0 +1,120 @@
+{
+    "additionDate": "2023-02-08T14:33:02.562098Z",
+    "biotoolsCURIE": "biotools:iup_bert",
+    "biotoolsID": "iup_bert",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "lvzhibin@pku.edu.cn",
+            "name": "Zhibin Lv",
+            "orcidid": "https://orcid.org/0000-0001-5390-7616",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "iUP-BERT is a user-friendly web server. It can directly identify whether a polypeptide is an umami peptide only from the sequence.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.aibiochem.net/servers/iUP-BERT/iUP-BERT.html",
+    "lastUpdate": "2023-02-08T14:33:02.565026Z",
+    "license": "Other",
+    "name": "iUP-BERT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3390/FOODS11223742",
+            "metadata": {
+                "abstract": "© 2022 by the authors.Umami is an important widely-used taste component of food seasoning. Umami peptides are specific structural peptides endowing foods with a favorable umami taste. Laboratory approaches used to identify umami peptides are time-consuming and labor-intensive, which are not feasible for rapid screening. Here, we developed a novel peptide sequence-based umami peptide predictor, namely iUP-BERT, which was based on the deep learning pretrained neural network feature extraction method. After optimization, a single deep representation learning feature encoding method (BERT: bidirectional encoder representations from transformer) in conjugation with the synthetic minority over-sampling technique (SMOTE) and support vector machine (SVM) methods was adopted for model creation to generate predicted probabilistic scores of potential umami peptides. Further extensive empirical experiments on cross-validation and an independent test showed that iUP-BERT outperformed the existing methods with improvements, highlighting its effectiveness and robustness. Finally, an open-access iUP-BERT web server was built. To our knowledge, this is the first efficient sequence-based umami predictor created based on a single deep-learning pretrained neural network feature extraction method. By predicting umami peptides, iUP-BERT can help in further research to improve the palatability of dietary supplements in the future.",
+                "authors": [
+                    {
+                        "name": "Jiang J."
+                    },
+                    {
+                        "name": "Jiang L."
+                    },
+                    {
+                        "name": "Liu C."
+                    },
+                    {
+                        "name": "Liu S."
+                    },
+                    {
+                        "name": "Lv Z."
+                    },
+                    {
+                        "name": "Wan Y."
+                    },
+                    {
+                        "name": "Wang X."
+                    },
+                    {
+                        "name": "Xiang D."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zheng B."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Foods",
+                "title": "IUP-BERT: Identification of Umami Peptides Based on BERT Features"
+            },
+            "pmcid": "PMC9689418",
+            "pmid": "36429332"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/jalview/jalview.biotools.json b/data/jalview/jalview.biotools.json
index a7c64e9044875..c0c4b45423db5 100644
--- a/data/jalview/jalview.biotools.json
+++ b/data/jalview/jalview.biotools.json
@@ -23,11 +23,11 @@
     "description": "Jalview is a free program for multiple sequence alignment editing, visualisation and analysis. Use it to view and edit sequence alignments, analyse them with phylogenetic trees and principal components analysis (PCA) plots and explore molecular structures and annotation.",
     "documentation": [
         {
-            "note": "Jalview training videos",
+            "note": "Hands-on exercises, Training courses and Training videos",
             "type": [
                 "Training material"
             ],
-            "url": "https://www.jalview.org/Help/Getting-Started"
+            "url": "https://www.jalview.org/training/"
         },
         {
             "type": [
@@ -39,28 +39,37 @@
             "type": [
                 "FAQ"
             ],
-            "url": "https://www.jalview.org/faq"
+            "url": "https://www.jalview.org/help/faq"
         },
         {
             "type": [
                 "User manual"
             ],
-            "url": "https://www.jalview.org/about/documentation"
+            "url": "https://www.jalview.org/help/documentation/"
         }
     ],
     "download": [
         {
             "note": "Binaries for all platforms",
             "type": "Binaries",
-            "url": "https://www.jalview.org/getdown/release/?osChoice=all"
+            "url": "https://www.jalview.org/download/?os=all"
+        },
+        {
+            "note": "Executable JAR file",
+            "type": "Binaries",
+            "url": "https://www.jalview.org/download/other/jar/"
         },
         {
             "type": "Downloads page",
             "url": "https://www.jalview.org/download"
         },
+        {
+            "type": "Icon",
+            "url": "https://www.jalview.org/favicon.svg"
+        },
         {
             "type": "Source code",
-            "url": "https://www.jalview.org/source/"
+            "url": "https://www.jalview.org/download/source/"
         }
     ],
     "editPermission": {
@@ -259,7 +268,7 @@
         }
     ],
     "homepage": "https://www.jalview.org/",
-    "lastUpdate": "2022-07-11T17:26:57.927086Z",
+    "lastUpdate": "2023-01-25T12:01:14.360386Z",
     "license": "GPL-3.0",
     "link": [
         {
@@ -267,19 +276,33 @@
             "type": [
                 "Other"
             ],
-            "url": "https://builds.jalview.org/browse/JB-GPC/latest/artifact"
+            "url": "https://www.jalview.org/development/jalview_develop/"
         },
         {
+            "note": "Twitter feed",
             "type": [
-                "Issue tracker"
+                "Social media"
             ],
-            "url": "https://issues.jalview.org/"
+            "url": "https://twitter.com/Jalview"
         },
         {
+            "note": "YouTube training videos",
             "type": [
-                "Mailing list"
+                "Social media"
             ],
-            "url": "https://www.jalview.org/mailman/listinfo/jalview-discuss"
+            "url": "https://www.youtube.com/channel/UCIjpnvZB770yz7ftbrJ0tfw"
+        },
+        {
+            "type": [
+                "Discussion forum"
+            ],
+            "url": "https://discourse.jalview.org/"
+        },
+        {
+            "type": [
+                "Issue tracker"
+            ],
+            "url": "https://issues.jalview.org/"
         },
         {
             "type": [
@@ -318,7 +341,7 @@
                         "name": "Waterhouse A.M."
                     }
                 ],
-                "citationCount": 5258,
+                "citationCount": 5654,
                 "date": "2009-05-07T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "Jalview Version 2-A multiple sequence alignment editor and analysis workbench"
@@ -326,6 +349,14 @@
         }
     ],
     "relation": [
+        {
+            "biotoolsID": "3d-beacons",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "bioconda",
+            "type": "includedIn"
+        },
         {
             "biotoolsID": "chimera",
             "type": "uses"
@@ -334,13 +365,33 @@
             "biotoolsID": "chimerax",
             "type": "uses"
         },
+        {
+            "biotoolsID": "ensembl",
+            "type": "uses"
+        },
         {
             "biotoolsID": "jabaws",
             "type": "uses"
         },
+        {
+            "biotoolsID": "pdb",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "pfam",
+            "type": "uses"
+        },
         {
             "biotoolsID": "pymol",
             "type": "uses"
+        },
+        {
+            "biotoolsID": "rfam",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "uniprot",
+            "type": "uses"
         }
     ],
     "toolType": [
@@ -358,6 +409,6 @@
     ],
     "validated": 1,
     "version": [
-        "2.11.2.3"
+        "2.11.2.6"
     ]
 }
diff --git a/data/jcbie/jcbie.biotools.json b/data/jcbie/jcbie.biotools.json
new file mode 100644
index 0000000000000..00a17d93be988
--- /dev/null
+++ b/data/jcbie/jcbie.biotools.json
@@ -0,0 +1,117 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-23T23:48:08.424595Z",
+    "biotoolsCURIE": "biotools:jcbie",
+    "biotoolsID": "jcbie",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "cli@xjtu.edu.cn",
+            "name": "Chen Li",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Erik Cambria"
+        },
+        {
+            "name": "Kai He"
+        },
+        {
+            "name": "Rui Mao"
+        },
+        {
+            "name": "Tieliang Gong"
+        }
+    ],
+    "description": "A joint continual learning neural network for biomedical information extraction.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Named-entity and concept recognition",
+                    "uri": "http://edamontology.org/operation_3280"
+                },
+                {
+                    "term": "Relation extraction",
+                    "uri": "http://edamontology.org/operation_3625"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/KaiHe-better/JCBIE.git",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-23T23:48:08.427809Z",
+    "license": "Not licensed",
+    "name": "JCBIE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05096-W",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Extracting knowledge from heterogeneous data sources is fundamental for the construction of structured biomedical knowledge graphs (BKGs), where entities and relations are represented as nodes and edges in the graphs, respectively. Previous biomedical knowledge extraction methods simply considered limited entity types and relations by using a task-specific training set, which is insufficient for large-scale BKGs development and downstream task applications in different scenarios. To alleviate this issue, we propose a joint continual learning biomedical information extraction (JCBIE) network to extract entities and relations from different biomedical information datasets. By empirically studying different joint learning and continual learning strategies, the proposed JCBIE can learn and expand different types of entities and relations from different datasets. JCBIE uses two separated encoders in joint-feature extraction, hence can effectively avoid the feature confusion problem comparing with using one hard-parameter sharing encoder. Specifically, it allows us to adopt entity augmented inputs to establish the interaction between named entity recognition and relation extraction. Finally, a novel evaluation mechanism is proposed for measuring cross-corpus generalization errors, which was ignored by traditional evaluation methods. Our empirical studies show that JCBIE achieves promising performance when continual learning strategy is adopted with multiple corpora.",
+                "authors": [
+                    {
+                        "name": "Cambria E."
+                    },
+                    {
+                        "name": "Gong T."
+                    },
+                    {
+                        "name": "He K."
+                    },
+                    {
+                        "name": "Li C."
+                    },
+                    {
+                        "name": "Mao R."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "JCBIE: a joint continual learning neural network for biomedical information extraction"
+            },
+            "pmcid": "PMC9761970",
+            "pmid": "36536280"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Data mining",
+            "uri": "http://edamontology.org/topic_3473"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        }
+    ]
+}
diff --git a/data/jlcrb/jlcrb.biotools.json b/data/jlcrb/jlcrb.biotools.json
new file mode 100644
index 0000000000000..23eb83024fef0
--- /dev/null
+++ b/data/jlcrb/jlcrb.biotools.json
@@ -0,0 +1,97 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T18:41:08.330275Z",
+    "biotoolsCURIE": "biotools:jlcrb",
+    "biotoolsID": "jlcrb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Xiuquan Du"
+        },
+        {
+            "name": "Zhigang Xue"
+        }
+    ],
+    "description": "A unified multi-view-based joint representation learning for CircRNA binding sites prediction.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence",
+                        "uri": "http://edamontology.org/data_2044"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Binding site prediction",
+                    "uri": "http://edamontology.org/operation_2575"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://82.157.188.204/JLCRB/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-29T18:41:08.332899Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/Xuezg/JLCRB"
+        }
+    ],
+    "name": "JLCRB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.JBI.2022.104231",
+            "metadata": {
+                "abstract": "© 2022 Elsevier Inc.CircRNAs usually bind to the corresponding RBPs(RNA Binding proteins) and play a key role in gene regulation. Therefore, it is important to identify the binding sites of RBPs on CircRNAs for the regulation of certain diseases. Due to the information provided by the single view feature is limited, the current mainstream methods are mainly to detect the RBP binding sites by constructing multi-view models. However, with the number of view features increases, the invalid information also increases, and the existing methods only simply concatenate together various features from different views, while ignoring the intrinsic connection between multi-view data. To solve this problem, we propose a new multi-view joint representation learning network by improving the consistency of multi-view feature information. First, the network uses different feature encoding methods to fully extract the feature information of RNA, respectively. Then we construct the intrinsic connection between the views by generating a global joint representation of multiple views, and this is used for feature calibration of each view to highlight important features and suppress unimportant ones. Finally, the depth features obtained from the fusion of multiple views are used to detect the binding sites of RNAs. The average AUC of our method is 93.68% in 37 CircRNA-RBP datasets. The experimental results show that the prediction performance of the method is better than existing methods. The code and datasets are obtained at https://github.com/Xuezg/JLCRB. In addition, we also provide a free web server that is freely available at http://82.157.188.204/JLCRB/.",
+                "authors": [
+                    {
+                        "name": "Du X."
+                    },
+                    {
+                        "name": "Xue Z."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Journal of Biomedical Informatics",
+                "title": "JLCRB: A unified multi-view-based joint representation learning for CircRNA binding sites prediction"
+            },
+            "pmid": "36309196"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene regulation",
+            "uri": "http://edamontology.org/topic_0204"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/jupyter_book/jupyter_book.biotools.json b/data/jupyter_book/jupyter_book.biotools.json
index 45b22d85cde43..0254ef44c5e24 100644
--- a/data/jupyter_book/jupyter_book.biotools.json
+++ b/data/jupyter_book/jupyter_book.biotools.json
@@ -3,6 +3,9 @@
     "additionDate": "2021-12-07T10:51:55.580418Z",
     "biotoolsCURIE": "biotools:jupyter_book",
     "biotoolsID": "jupyter_book",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
     "confidence_flag": "tool",
     "cost": "Free of charge",
     "credit": [
@@ -17,7 +20,10 @@
     ],
     "description": "Jupyter Book is an open-source tool for building publication-quality books and documents from computational material.",
     "editPermission": {
-        "type": "private"
+        "authors": [
+            "iacs-biocomputacion"
+        ],
+        "type": "group"
     },
     "function": [
         {
@@ -35,7 +41,7 @@
         "Python",
         "Shell"
     ],
-    "lastUpdate": "2021-12-07T10:51:55.583369Z",
+    "lastUpdate": "2023-02-01T13:02:14.203575Z",
     "license": "BSD-3-Clause",
     "link": [
         {
@@ -81,5 +87,6 @@
             "term": "Software engineering",
             "uri": "http://edamontology.org/topic_3372"
         }
-    ]
+    ],
+    "validated": 1
 }
diff --git a/data/jupytope/jupytope.biotools.json b/data/jupytope/jupytope.biotools.json
new file mode 100644
index 0000000000000..3cd0c4c6f1415
--- /dev/null
+++ b/data/jupytope/jupytope.biotools.json
@@ -0,0 +1,100 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T21:25:33.219115Z",
+    "biotoolsCURIE": "biotools:jupytope",
+    "biotoolsID": "jupytope",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "asckkwoh@ntu.edu.sg",
+            "name": "Kwoh Chee Keong",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ng Teng Ann"
+        },
+        {
+            "name": "Shamima Rashid"
+        }
+    ],
+    "description": "Computational extraction of structural properties of viral epitopes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Side chain modelling",
+                    "uri": "http://edamontology.org/operation_0480"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/shamimarashid/Jupytope",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-17T21:25:33.221668Z",
+    "license": "GPL-3.0",
+    "name": "Jupytope",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bib/bbac362",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Epitope residues located on viral surface proteins are of immense interest in immunology and related applications such as vaccine development, disease diagnosis and drug design. Most tools rely on sequence-based statistical comparisons, such as information entropy of residue positions in aligned columns to infer location and properties of epitope sites. To facilitate cross-structural comparisons of epitopes on viral surface proteins, a python-based extraction tool implemented with Jupyter notebook is presented (Jupytope). Given a viral antigen structure of interest, a list of known epitope sites and a reference structure, the corresponding epitope structural properties can quickly be obtained. The tool integrates biopython modules for commonly used software such as NACCESS, DSSP as well as residue depth and outputs a list of structure-derived properties such as dihedral angles, solvent accessibility, residue depth and secondary structure that can be saved in several convenient data formats. To ensure correct spatial alignment, Jupytope takes a list of given epitope sites and their corresponding reference structure and aligns them before extracting the desired properties. Examples are demonstrated for epitopes of Influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) viral strains. The extracted properties assist detection of two Influenza subtypes and show potential in distinguishing between four major clades of SARS-CoV2, as compared with randomized labels. The tool will facilitate analytical and predictive works on viral epitopes through the extracted structural information. Jupytope and extracted datasets are available at https://github.com/shamimarashid/Jupytope.",
+                "authors": [
+                    {
+                        "name": "Kwoh C.K."
+                    },
+                    {
+                        "name": "Ng T.A."
+                    },
+                    {
+                        "name": "Rashid S."
+                    }
+                ],
+                "date": "2022-11-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "Jupytope: computational extraction of structural properties of viral epitopes"
+            },
+            "pmid": "36094101"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Protein structural motifs and surfaces",
+            "uri": "http://edamontology.org/topic_0166"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Vaccinology",
+            "uri": "http://edamontology.org/topic_3966"
+        }
+    ]
+}
diff --git a/data/justdeepit/justdeepit.biotools.json b/data/justdeepit/justdeepit.biotools.json
new file mode 100644
index 0000000000000..0491612a31de6
--- /dev/null
+++ b/data/justdeepit/justdeepit.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T18:36:13.406814Z",
+    "biotoolsCURIE": "biotools:justdeepit",
+    "biotoolsID": "justdeepit",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "sun@biunit.dev",
+            "name": "Jianqiang Sun",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Takehiko Yamanaka"
+        },
+        {
+            "name": "Wei Cao"
+        }
+    ],
+    "description": "Software tool with graphical and character user interfaces for deep learning-based object detection and segmentation in image analysis.\n\nDeep learning has been applied to solve various problems, especially in image recognition, across many fields including the life sciences and agriculture.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Image annotation",
+                    "uri": "http://edamontology.org/operation_3553"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/biunit/JustDeepIt",
+    "language": [
+        "JavaScript",
+        "Python"
+    ],
+    "lastUpdate": "2022-12-29T18:36:13.409218Z",
+    "license": "MIT",
+    "name": "JustDeepIt",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FPLS.2022.964058",
+            "metadata": {
+                "abstract": "Copyright © 2022 Sun, Cao and Yamanaka.Image processing and analysis based on deep learning are becoming mainstream and increasingly accessible for solving various scientific problems in diverse fields. However, it requires advanced computer programming skills and a basic familiarity with character user interfaces (CUIs). Consequently, programming beginners face a considerable technical hurdle. Because potential users of image analysis are experimentalists, who often use graphical user interfaces (GUIs) in their daily work, there is a need to develop GUI-based easy-to-use deep learning software to support their work. Here, we introduce JustDeepIt, a software written in Python, to simplify object detection and instance segmentation using deep learning. JustDeepIt provides both a GUI and a CUI. It contains various functional modules for model building and inference, and it is built upon the popular PyTorch, MMDetection, and Detectron2 libraries. The GUI is implemented using the Python library FastAPI, simplifying model building for various deep learning approaches for beginners. As practical examples of JustDeepIt, we prepared four case studies that cover critical issues in plant science: (1) wheat head detection with Faster R-CNN, YOLOv3, SSD, and RetinaNet; (2) sugar beet and weed segmentation with Mask R-CNN; (3) plant segmentation with U2-Net; and (4) leaf segmentation with U2-Net. The results support the wide applicability of JustDeepIt in plant science applications. In addition, we believe that JustDeepIt has the potential to be applied to deep learning-based image analysis in various fields beyond plant science.",
+                "authors": [
+                    {
+                        "name": "Cao W."
+                    },
+                    {
+                        "name": "Sun J."
+                    },
+                    {
+                        "name": "Yamanaka T."
+                    }
+                ],
+                "date": "2022-10-06T00:00:00Z",
+                "journal": "Frontiers in Plant Science",
+                "title": "JustDeepIt: Software tool with graphical and character user interfaces for deep learning-based object detection and segmentation in image analysis"
+            },
+            "pmcid": "PMC9583140",
+            "pmid": "36275541"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Informatics",
+            "uri": "http://edamontology.org/topic_0605"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        }
+    ]
+}
diff --git a/data/kage/kage.biotools.json b/data/kage/kage.biotools.json
new file mode 100644
index 0000000000000..358391731b2be
--- /dev/null
+++ b/data/kage/kage.biotools.json
@@ -0,0 +1,110 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T18:31:05.193188Z",
+    "biotoolsCURIE": "biotools:kage",
+    "biotoolsID": "kage",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ivargry@ifi.uio.no",
+            "name": "Ivar Grytten",
+            "orcidid": "https://orcid.org/0000-0001-8941-942X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Geir Kjetil Sandve"
+        },
+        {
+            "name": "Knut Dagestad Rand"
+        }
+    ],
+    "description": "KAGE is a tool for efficiently genotyping short SNPs and indels from short genomic reads.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Haplotype mapping",
+                    "uri": "http://edamontology.org/operation_0487"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ivargr/kage",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-29T18:31:05.196220Z",
+    "license": "GPL-3.0",
+    "name": "KAGE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13059-022-02771-2",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Genotyping is a core application of high-throughput sequencing. We present KAGE, a genotyper for SNPs and short indels that is inspired by recent developments within graph-based genome representations and alignment-free methods. KAGE uses a pan-genome representation of the population to efficiently and accurately predict genotypes. Two novel ideas improve both the speed and accuracy: a Bayesian model incorporates genotypes from thousands of individuals to improve prediction accuracy, and a computationally efficient method leverages correlation between variants. We show that the accuracy of KAGE is at par with the best existing alignment-free genotypers, while being an order of magnitude faster.",
+                "authors": [
+                    {
+                        "name": "Dagestad Rand K."
+                    },
+                    {
+                        "name": "Grytten I."
+                    },
+                    {
+                        "name": "Sandve G.K."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Genome Biology",
+                "title": "KAGE: fast alignment-free graph-based genotyping of SNPs and short indels"
+            },
+            "pmcid": "PMC9531401",
+            "pmid": "36195962"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Genotyping experiment",
+            "uri": "http://edamontology.org/topic_3516"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/kargamobile/kargamobile.biotools.json b/data/kargamobile/kargamobile.biotools.json
new file mode 100644
index 0000000000000..8657372251bbb
--- /dev/null
+++ b/data/kargamobile/kargamobile.biotools.json
@@ -0,0 +1,92 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-11T07:36:07.512699Z",
+    "biotoolsCURIE": "biotools:kargamobile",
+    "biotoolsID": "kargamobile",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "m.prosperi@ufl.edu",
+            "name": "Mattia Prosperi",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Android app for portable, real-time, easily interpretable analysis of antibiotic resistance genes via nanopore sequencing",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Antimicrobial resistance prediction",
+                    "uri": "http://edamontology.org/operation_3482"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Ruiz-HCI-Lab/KargaMobile",
+    "language": [
+        "Java"
+    ],
+    "lastUpdate": "2023-02-11T07:36:07.515188Z",
+    "license": "MIT",
+    "name": "KARGAMobile",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FBIOE.2022.1016408",
+            "metadata": {
+                "abstract": "Copyright © 2022 Barquero, Marini, Boucher, Ruiz and Prosperi.Nanopore technology enables portable, real-time sequencing of microbial populations from clinical and ecological samples. An emerging healthcare application for Nanopore includes point-of-care, timely identification of antibiotic resistance genes (ARGs) to help developing targeted treatments of bacterial infections, and monitoring resistant outbreaks in the environment. While several computational tools exist for classifying ARGs from sequencing data, to date (2022) none have been developed for mobile devices. We present here KARGAMobile, a mobile app for portable, real-time, easily interpretable analysis of ARGs from Nanopore sequencing. KARGAMobile is the porting of an existing ARG identification tool named KARGA; it retains the same algorithmic structure, but it is optimized for mobile devices. Specifically, KARGAMobile employs a compressed ARG reference database and different internal data structures to save RAM usage. The KARGAMobile app features a friendly graphical user interface that guides through file browsing, loading, parameter setup, and process execution. More importantly, the output files are post-processed to create visual, printable and shareable reports, aiding users to interpret the ARG findings. The difference in classification performance between KARGAMobile and KARGA is minimal (96.2% vs. 96.9% f-measure on semi-synthetic datasets of 1 million reads with known resistance ground truth). Using real Nanopore experiments, KARGAMobile processes on average 1 GB data every 23–48 min (targeted sequencing - metagenomics), with peak RAM usage below 500MB, independently from input file sizes, and an average temperature of 49°C after 1 h of continuous data processing. KARGAMobile is written in Java and is available at https://github.com/Ruiz-HCI-Lab/KargaMobile under the MIT license.",
+                "authors": [
+                    {
+                        "name": "Barquero A."
+                    },
+                    {
+                        "name": "Boucher C."
+                    },
+                    {
+                        "name": "Marini S."
+                    },
+                    {
+                        "name": "Prosperi M."
+                    },
+                    {
+                        "name": "Ruiz J."
+                    }
+                ],
+                "date": "2022-10-17T00:00:00Z",
+                "journal": "Frontiers in Bioengineering and Biotechnology",
+                "title": "KARGAMobile: Android app for portable, real-time, easily interpretable analysis of antibiotic resistance genes via nanopore sequencing"
+            },
+            "pmcid": "PMC9618647",
+            "pmid": "36324897"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Metagenomic sequencing",
+            "uri": "http://edamontology.org/topic_3837"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        }
+    ]
+}
diff --git a/data/karyon/karyon.biotools.json b/data/karyon/karyon.biotools.json
index b5c136c7c9f72..ecbd7d8877ea7 100644
--- a/data/karyon/karyon.biotools.json
+++ b/data/karyon/karyon.biotools.json
@@ -51,7 +51,7 @@
         "Python",
         "Shell"
     ],
-    "lastUpdate": "2022-07-08T07:42:27.066123Z",
+    "lastUpdate": "2023-03-09T14:39:26.480528Z",
     "license": "GPL-3.0",
     "link": [
         {
@@ -71,6 +71,24 @@
             ]
         }
     ],
+    "relation": [
+        {
+            "biotoolsID": "mpileup",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "redundans",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "soapdenovo2",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "spades",
+            "type": "uses"
+        }
+    ],
     "toolType": [
         "Workbench",
         "Workflow"
diff --git a/data/keras_r-cnn/keras_r-cnn.biotools.json b/data/keras_r-cnn/keras_r-cnn.biotools.json
index 7fa29293de15a..8a2b0f47a2c89 100644
--- a/data/keras_r-cnn/keras_r-cnn.biotools.json
+++ b/data/keras_r-cnn/keras_r-cnn.biotools.json
@@ -2,6 +2,9 @@
     "additionDate": "2021-01-18T09:54:46Z",
     "biotoolsCURIE": "biotools:keras_r-cnn",
     "biotoolsID": "keras_r-cnn",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
     "confidence_flag": "tool",
     "credit": [
         {
@@ -13,7 +16,10 @@
     ],
     "description": "library for cell detection in biological images using deep neural networks.\n\nkeras-rcnn is the Keras package for region-based convolutional neural networks.",
     "editPermission": {
-        "type": "private"
+        "authors": [
+            "iacs-biocomputacion"
+        ],
+        "type": "group"
     },
     "function": [
         {
@@ -29,7 +35,7 @@
     "language": [
         "Python"
     ],
-    "lastUpdate": "2021-02-12T10:25:38Z",
+    "lastUpdate": "2023-02-01T13:01:10.227141Z",
     "name": "Keras R-CNN",
     "owner": "Niclaskn",
     "publication": [
@@ -84,7 +90,7 @@
                         "name": "Renia L."
                     }
                 ],
-                "citationCount": 5,
+                "citationCount": 22,
                 "date": "2020-07-11T00:00:00Z",
                 "journal": "BMC Bioinformatics",
                 "title": "Keras R-CNN: Library for cell detection in biological images using deep neural networks"
diff --git a/data/kmdiff/kmdiff.biotools.json b/data/kmdiff/kmdiff.biotools.json
new file mode 100644
index 0000000000000..0c8da3145b7bb
--- /dev/null
+++ b/data/kmdiff/kmdiff.biotools.json
@@ -0,0 +1,86 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T18:17:05.271259Z",
+    "biotoolsCURIE": "biotools:kmdiff",
+    "biotoolsID": "kmdiff",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "pierre.peterlongo@inria.fr",
+            "name": "Pierre Peterlongo",
+            "orcidid": "https://orcid.org/0000-0003-0776-6407",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Rayan Chikhi"
+        },
+        {
+            "name": "Téo Lemane",
+            "orcidid": "https://orcid.org/0000-0002-7210-3178"
+        }
+    ],
+    "description": "kmdiff provides differential k-mers analysis between two populations (control and case). Each population is represented by a set of short-read sequencing. Outputs are differentially represented k-mers between controls and cases.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/tlemane/kmdiff",
+    "language": [
+        "C++",
+        "Shell"
+    ],
+    "lastUpdate": "2022-12-29T18:17:05.273839Z",
+    "license": "AGPL-3.0",
+    "name": "kmdiff",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC689",
+            "pmid": "36315078"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        }
+    ]
+}
diff --git a/data/knowledge4covid-19/knowledge4covid-19.biotools.json b/data/knowledge4covid-19/knowledge4covid-19.biotools.json
new file mode 100644
index 0000000000000..781dcb2c9c94d
--- /dev/null
+++ b/data/knowledge4covid-19/knowledge4covid-19.biotools.json
@@ -0,0 +1,146 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2022-12-29T18:13:27.284679Z",
+    "biotoolsCURIE": "biotools:knowledge4covid-19",
+    "biotoolsID": "knowledge4covid-19",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Ahmad Sakor"
+        },
+        {
+            "name": "Samaneh Jozashoori"
+        },
+        {
+            "name": "Fotis Aisopos",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Maria-Esther Vidal",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments' toxicities.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
+                },
+                {
+                    "term": "Named-entity and concept recognition",
+                    "uri": "http://edamontology.org/operation_3280"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/SDM-TIB/Knowledge4COVID-19",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2022-12-29T18:13:27.287582Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://zenodo.org/record/4701817#.YH336-8zbol"
+        }
+    ],
+    "name": "Knowledge4COVID-19",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.WEBSEM.2022.100760",
+            "metadata": {
+                "abstract": "© 2022 Elsevier B.V.In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the power of integrating disparate sources of knowledge to discover adverse drug effects caused by drug–drug interactions among COVID-19 treatments and pre-existing condition drugs. Initially, we focus on constructing the Knowledge4COVID-19 knowledge graph (KG) from the declarative definition of mapping rules using the RDF Mapping Language. Since valuable information about drug treatments, drug–drug interactions, and side effects is present in textual descriptions in scientific databases (e.g., DrugBank) or in scientific literature (e.g., the CORD-19, the Covid-19 Open Research Dataset), the Knowledge4COVID-19 framework implements Natural Language Processing. The Knowledge4COVID-19 framework extracts relevant entities and predicates that enable the fine-grained description of COVID-19 treatments and the potential adverse events that may occur when these treatments are combined with treatments of common comorbidities, e.g., hypertension, diabetes, or asthma. Moreover, on top of the KG, several techniques for the discovery and prediction of interactions and potential adverse effects of drugs have been developed with the aim of suggesting more accurate treatments for treating the virus. We provide services to traverse the KG and visualize the effects that a group of drugs may have on a treatment outcome. Knowledge4COVID-19 was part of the Pan-European hackathon#EUvsVirus in April 2020 and is publicly available as a resource through a GitHub repository and a DOI.",
+                "authors": [
+                    {
+                        "name": "Aisopos F."
+                    },
+                    {
+                        "name": "Bougiatiotis K."
+                    },
+                    {
+                        "name": "Iglesias E."
+                    },
+                    {
+                        "name": "Jozashoori S."
+                    },
+                    {
+                        "name": "Krithara A."
+                    },
+                    {
+                        "name": "Niazmand E."
+                    },
+                    {
+                        "name": "Padiya T."
+                    },
+                    {
+                        "name": "Paliouras G."
+                    },
+                    {
+                        "name": "Rivas A."
+                    },
+                    {
+                        "name": "Rohde P.D."
+                    },
+                    {
+                        "name": "Sakor A."
+                    },
+                    {
+                        "name": "Vidal M.-E."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Journal of Web Semantics",
+                "title": "Knowledge4COVID-19: A semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments’ toxicities"
+            },
+            "pmcid": "PMC9558693",
+            "pmid": "36268112"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Drug metabolism",
+            "uri": "http://edamontology.org/topic_3375"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Pharmacology",
+            "uri": "http://edamontology.org/topic_0202"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/konnect2prot/konnect2prot.biotools.json b/data/konnect2prot/konnect2prot.biotools.json
new file mode 100644
index 0000000000000..53679cbb39e60
--- /dev/null
+++ b/data/konnect2prot/konnect2prot.biotools.json
@@ -0,0 +1,88 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-23T23:42:00.070084Z",
+    "biotoolsCURIE": "biotools:konnect2prot",
+    "biotoolsID": "konnect2prot",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "samrat.chatterjee@thsti.res.in",
+            "name": "Samrat Chatterjee",
+            "orcidid": "https://orcid.org/0000-0002-5010-2799",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Dipanka Tanu Sarmah"
+        },
+        {
+            "name": "Shailendra Asthana"
+        },
+        {
+            "name": "Shivam Kumar"
+        }
+    ],
+    "description": "A web application to explore the protein properties in a functional protein-protein interaction network.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network visualisation",
+                    "uri": "http://edamontology.org/operation_3925"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "Protein interaction network analysis",
+                    "uri": "http://edamontology.org/operation_0276"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://konnect2prot.thsti.in",
+    "lastUpdate": "2023-02-23T23:42:00.072731Z",
+    "name": "konnect2prot",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC815",
+            "pmcid": "PMC9848060",
+            "pmid": "36545703"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein properties",
+            "uri": "http://edamontology.org/topic_0123"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/kova/kova.biotools.json b/data/kova/kova.biotools.json
new file mode 100644
index 0000000000000..24155f73cd27e
--- /dev/null
+++ b/data/kova/kova.biotools.json
@@ -0,0 +1,168 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T14:53:07.445680Z",
+    "biotoolsCURIE": "biotools:kova",
+    "biotoolsID": "kova",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Jean Lee"
+        },
+        {
+            "name": "Jeongeun Lee"
+        },
+        {
+            "name": "Jong Hwa Bhak"
+        },
+        {
+            "name": "Murim Choi"
+        }
+    ],
+    "description": "A database of 5,305 healthy Korean individuals reveals genetic and clinical implications for an East Asian population.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene name",
+                        "uri": "http://edamontology.org/data_2299"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Genetic variation analysis",
+                    "uri": "http://edamontology.org/operation_3197"
+                },
+                {
+                    "term": "Linkage disequilibrium calculation",
+                    "uri": "http://edamontology.org/operation_0488"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.kobic.re.kr/kova/",
+    "lastUpdate": "2023-02-26T14:53:07.448322Z",
+    "name": "KOVA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/s12276-022-00871-4",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Despite substantial advances in disease genetics, studies to date have largely focused on individuals of European descent. This limits further discoveries of novel functional genetic variants in other ethnic groups. To alleviate the paucity of East Asian population genome resources, we established the Korean Variant Archive 2 (KOVA 2), which is composed of 1896 whole-genome sequences and 3409 whole-exome sequences from healthy individuals of Korean ethnicity. This is the largest genome database from the ethnic Korean population to date, surpassing the 1909 Korean individuals deposited in gnomAD. The variants in KOVA 2 displayed all the known genetic features of those from previous genome databases, and we compiled data from Korean-specific runs of homozygosity, positively selected intervals, and structural variants. In doing so, we found loci, such as the loci of ADH1A/1B and UHRF1BP1, that are strongly selected in the Korean population relative to other East Asian populations. Our analysis of allele ages revealed a correlation between variant functionality and evolutionary age. The data can be browsed and downloaded from a public website (https://www.kobic.re.kr/kova/). We anticipate that KOVA 2 will serve as a valuable resource for genetic studies involving East Asian populations.",
+                "authors": [
+                    {
+                        "name": "Baek D."
+                    },
+                    {
+                        "name": "Bhak J.H."
+                    },
+                    {
+                        "name": "Chae J.-H."
+                    },
+                    {
+                        "name": "Choi B.-O."
+                    },
+                    {
+                        "name": "Choi J."
+                    },
+                    {
+                        "name": "Choi M."
+                    },
+                    {
+                        "name": "Gee H.Y."
+                    },
+                    {
+                        "name": "Jang I."
+                    },
+                    {
+                        "name": "Jang I.-J."
+                    },
+                    {
+                        "name": "Jeon S."
+                    },
+                    {
+                        "name": "Kim Y.-J."
+                    },
+                    {
+                        "name": "Koh Y."
+                    },
+                    {
+                        "name": "Lee B."
+                    },
+                    {
+                        "name": "Lee J."
+                    },
+                    {
+                        "name": "Lee J."
+                    },
+                    {
+                        "name": "Lee J."
+                    },
+                    {
+                        "name": "Lee S."
+                    },
+                    {
+                        "name": "Oh J."
+                    },
+                    {
+                        "name": "Park S."
+                    },
+                    {
+                        "name": "Park W.-Y."
+                    },
+                    {
+                        "name": "Yang J.O."
+                    },
+                    {
+                        "name": "Yoon S.-S."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Experimental and Molecular Medicine",
+                "title": "A database of 5305 healthy Korean individuals reveals genetic and clinical implications for an East Asian population"
+            },
+            "pmcid": "PMC9628380",
+            "pmid": "36323850"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Population genetics",
+            "uri": "http://edamontology.org/topic_3056"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/l-rapit/l-rapit.biotools.json b/data/l-rapit/l-rapit.biotools.json
new file mode 100644
index 0000000000000..b540308abca58
--- /dev/null
+++ b/data/l-rapit/l-rapit.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-23T23:34:26.762651Z",
+    "biotoolsCURIE": "biotools:l-rapit",
+    "biotoolsID": "l-rapit",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tp2405@cumc.columbia.edu",
+            "name": "Thomas S. Postler",
+            "orcidid": "https://orcid.org/0000-0002-3558-9084",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Sankar Ghosh"
+        },
+        {
+            "name": "Theodore M. Nelson",
+            "orcidid": "https://orcid.org/0000-0002-8600-0444"
+        }
+    ],
+    "description": "A Cloud-Based Computing Pipeline for the Analysis of Long-Read RNA Sequencing Data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Alternative splicing prediction",
+                    "uri": "http://edamontology.org/operation_0264"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Sequence merging",
+                    "uri": "http://edamontology.org/operation_0232"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Theo-Nelson/long-read-sequencing-pipeline",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-23T23:34:26.765082Z",
+    "license": "Not licensed",
+    "name": "L-RAPiT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/IJMS232415851",
+            "metadata": {
+                "abstract": "© 2022 by the authors.Long-read sequencing (LRS) has been adopted to meet a wide variety of research needs, ranging from the construction of novel transcriptome annotations to the rapid identification of emerging virus variants. Amongst other advantages, LRS preserves more information about RNA at the transcript level than conventional high-throughput sequencing, including far more accurate and quantitative records of splicing patterns. New studies with LRS datasets are being published at an exponential rate, generating a vast reservoir of information that can be leveraged to address a host of different research questions. However, mining such publicly available data in a tailored fashion is currently not easy, as the available software tools typically require familiarity with the command-line interface, which constitutes a significant obstacle to many researchers. Additionally, different research groups utilize different software packages to perform LRS analysis, which often prevents a direct comparison of published results across different studies. To address these challenges, we have developed the Long-Read Analysis Pipeline for Transcriptomics (L-RAPiT), a user-friendly, free pipeline requiring no dedicated computational resources or bioinformatics expertise. L-RAPiT can be implemented directly through Google Colaboratory, a system based on the open-source Jupyter notebook environment, and allows for the direct analysis of transcriptomic reads from Oxford Nanopore and PacBio LRS machines. This new pipeline enables the rapid, convenient, and standardized analysis of publicly available or newly generated LRS datasets.",
+                "authors": [
+                    {
+                        "name": "Ghosh S."
+                    },
+                    {
+                        "name": "Nelson T.M."
+                    },
+                    {
+                        "name": "Postler T.S."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "International Journal of Molecular Sciences",
+                "title": "L-RAPiT: A Cloud-Based Computing Pipeline for the Analysis of Long-Read RNA Sequencing Data"
+            },
+            "pmcid": "PMC9781625",
+            "pmid": "36555493"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/lapine/lapine.biotools.json b/data/lapine/lapine.biotools.json
new file mode 100644
index 0000000000000..9d06da8dcea15
--- /dev/null
+++ b/data/lapine/lapine.biotools.json
@@ -0,0 +1,145 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-23T23:09:38.012832Z",
+    "biotoolsCURIE": "biotools:lapine",
+    "biotoolsID": "lapine",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dhlee@kaist.ac.kr",
+            "name": "Doheon Lee",
+            "orcidid": "https://orcid.org/0000-0001-9070-4316",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jaegyun Jung",
+            "orcidid": "https://orcid.org/0000-0002-3222-2965"
+        },
+        {
+            "name": "Jaesub Park",
+            "orcidid": "https://orcid.org/0000-0002-4905-5980"
+        },
+        {
+            "name": "Kwansoo Kim",
+            "orcidid": "https://orcid.org/0000-0002-1951-8921"
+        },
+        {
+            "name": "Sangyeon Lee",
+            "orcidid": "https://orcid.org/0000-0002-3260-4285"
+        }
+    ],
+    "description": "Large-scale prediction of adverse drug reactions-related proteins with network embedding.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://lapine.readthedocs.io/en/latest/index.html"
+        }
+    ],
+    "download": [
+        {
+            "type": "Other",
+            "url": "https://figshare.com/articles/software/LAPINE/21750245"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular docking",
+                    "uri": "http://edamontology.org/operation_0478"
+                },
+                {
+                    "term": "Protein identification",
+                    "uri": "http://edamontology.org/operation_3767"
+                },
+                {
+                    "term": "Protein interaction network analysis",
+                    "uri": "http://edamontology.org/operation_0276"
+                },
+                {
+                    "term": "Protein interaction network prediction",
+                    "uri": "http://edamontology.org/operation_3094"
+                },
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/rupinas/LAPINE",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-23T23:09:38.016151Z",
+    "license": "MIT",
+    "name": "LAPINE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC843",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Adverse drug reactions (ADRs) are a major issue in drug development and clinical pharmacology. As most ADRs are caused by unintended activity at off-targets of drugs, the identification of drug targets responsible for ADRs becomes a key process for resolving ADRs. Recently, with the increase in the number of ADR-related data sources, several computational methodologies have been proposed to analyze ADR-protein relations. However, the identification of ADR-related proteins on a large scale with high reliability remains an important challenge. RESULTS: In this article, we suggest a computational approach, Large-scale ADR-related Proteins Identification with Network Embedding (LAPINE). LAPINE combines a novel concept called single-target compound with a network embedding technique to enable large-scale prediction of ADR-related proteins for any proteins in the protein-protein interaction network. Analysis of benchmark datasets confirms the need to expand the scope of potential ADR-related proteins to be analyzed, as well as LAPINE's capability for high recovery of known ADR-related proteins. Moreover, LAPINE provides more reliable predictions for ADR-related proteins (Value-added positive predictive value = 0.12), compared to a previously proposed method (P < 0.001). Furthermore, two case studies show that most predictive proteins related to ADRs in LAPINE are supported by literature evidence. Overall, LAPINE can provide reliable insights into the relationship between ADRs and proteomes to understand the mechanism of ADRs leading to their prevention. AVAILABILITY AND IMPLEMENTATION: The source code is available at GitHub (https://github.com/rupinas/LAPINE) and Figshare (https://figshare.com/articles/software/LAPINE/21750245) to facilitate its use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Jung J."
+                    },
+                    {
+                        "name": "Kim K."
+                    },
+                    {
+                        "name": "Lee D."
+                    },
+                    {
+                        "name": "Lee S."
+                    },
+                    {
+                        "name": "Park J."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Large-scale prediction of adverse drug reactions-related proteins with network embedding"
+            },
+            "pmcid": "PMC9825773",
+            "pmid": "36579854"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Drug development",
+            "uri": "http://edamontology.org/topic_3373"
+        },
+        {
+            "term": "Pharmacology",
+            "uri": "http://edamontology.org/topic_0202"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/layerumap/layerumap.biotools.json b/data/layerumap/layerumap.biotools.json
new file mode 100644
index 0000000000000..7963eef643ea7
--- /dev/null
+++ b/data/layerumap/layerumap.biotools.json
@@ -0,0 +1,105 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-11T07:37:42.449629Z",
+    "biotoolsCURIE": "biotools:layerumap",
+    "biotoolsID": "layerumap",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "ljs@swmu.edu.cn",
+            "name": "Jiesi Luo",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xinyan_scu@126.com",
+            "name": "Lezheng Yu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A tool for visualizing and understanding deep learning models in biological sequence classification using UMAP.",
+    "download": [
+        {
+            "type": "Software package",
+            "url": "https://github.com/jingry/autoBioSeqpy/blob/2.0/examples/layerUMAP.zip"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Sequence classification",
+                    "uri": "http://edamontology.org/operation_2995"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/jingry/autoBioSeqpy/blob/2.0/tool/layerUMAP.py",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-11T07:37:42.482499Z",
+    "name": "layerUMAP",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1016/J.ISCI.2022.105530",
+            "metadata": {
+                "abstract": "© 2022 The Author(s)Despite the impressive success of deep learning techniques in various types of classification and prediction tasks, interpreting these models and explaining their predictions are still major challenges. In this article, we present an easy-to-use command line tool capable of visualizing and analyzing alternative representations of biological observations learned by deep learning models. This new tool, namely, layerUMAP, integrates autoBioSeqpy software and the UMAP library to address learned high-level representations. An important advantage of the tool is that it provides an interactive option that enables users to visualize the outputs of hidden layers along the depth of the model. We use two different classes of examples to illustrate the potential power of layerUMAP, and the results demonstrate that layerUMAP can provide insightful visual feedback about models and further guide us to develop better models.",
+                "authors": [
+                    {
+                        "name": "Jing R."
+                    },
+                    {
+                        "name": "Li M."
+                    },
+                    {
+                        "name": "Luo J."
+                    },
+                    {
+                        "name": "Xue L."
+                    },
+                    {
+                        "name": "Yu L."
+                    }
+                ],
+                "date": "2022-12-22T00:00:00Z",
+                "journal": "iScience",
+                "title": "layerUMAP: A tool for visualizing and understanding deep learning models in biological sequence classification using UMAP"
+            },
+            "pmcid": "PMC9678764",
+            "pmid": "36425757"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Systems biology",
+            "uri": "http://edamontology.org/topic_2259"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/lcel/lcel.biotools.json b/data/lcel/lcel.biotools.json
new file mode 100644
index 0000000000000..5bb62d7dad026
--- /dev/null
+++ b/data/lcel/lcel.biotools.json
@@ -0,0 +1,95 @@
+{
+    "additionDate": "2023-02-08T14:44:33.436210Z",
+    "biotoolsCURIE": "biotools:lcel",
+    "biotoolsID": "lcel",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "gujinghangnlp@gmail.com",
+            "name": "Jinghang Gu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Ensemble learning for COVID-19 multi-label classification.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Text mining",
+                    "uri": "http://edamontology.org/operation_0306"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/JHnlp/LCEL",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-08T14:44:33.438734Z",
+    "license": "Not licensed",
+    "name": "LCEL",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/DATABASE/BAAC103",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press.The Coronavirus Disease 2019 (COVID-19) pandemic has shifted the focus of research worldwide, and more than 10 000 new articles per month have concentrated on COVID-19-related topics. Considering this rapidly growing literature, the efficient and precise extraction of the main topics of COVID-19-relevant articles is of great importance. The manual curation of this information for biomedical literature is labor-intensive and time-consuming, and as such the procedure is insufficient and difficult to maintain. In response to these complications, the BioCreative VII community has proposed a challenging task, LitCovid Track, calling for a global effort to automatically extract semantic topics for COVID-19 literature. This article describes our work on the BioCreative VII LitCovid Track. We proposed the LitCovid Ensemble Learning (LCEL) method for the tasks and integrated multiple biomedical pretrained models to address the COVID-19 multi-label classification problem. Specifically, seven different transformer-based pretrained models were ensembled for the initialization and fine-tuning processes independently. To enhance the representation abilities of the deep neural models, diverse additional biomedical knowledge was utilized to facilitate the fruitfulness of the semantic expressions. Simple yet effective data augmentation was also leveraged to address the learning deficiency during the training phase. In addition, given the imbalanced label distribution of the challenging task, a novel asymmetric loss function was applied to the LCEL model, which explicitly adjusted the negative-positive importance by assigning different exponential decay factors and helped the model focus on the positive samples. After the training phase, an ensemble bagging strategy was adopted to merge the outputs from each model for final predictions. The experimental results show the effectiveness of our proposed approach, as LCEL obtains the state-of-the-art performance on the LitCovid dataset. Database URL: https://github.com/JHnlp/LCEL",
+                "authors": [
+                    {
+                        "name": "Chersoni E."
+                    },
+                    {
+                        "name": "Gu J."
+                    },
+                    {
+                        "name": "Huang C.-R."
+                    },
+                    {
+                        "name": "Qian L."
+                    },
+                    {
+                        "name": "Wang X."
+                    },
+                    {
+                        "name": "Zhou G."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Database",
+                "title": "LitCovid ensemble learning for COVID-19 multi-label classification"
+            },
+            "pmcid": "PMC9693804",
+            "pmid": "36426767"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        }
+    ]
+}
diff --git a/data/ldak-gbat/ldak-gbat.biotools.json b/data/ldak-gbat/ldak-gbat.biotools.json
new file mode 100644
index 0000000000000..e63f52dddbad9
--- /dev/null
+++ b/data/ldak-gbat/ldak-gbat.biotools.json
@@ -0,0 +1,98 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-23T22:45:45.825960Z",
+    "biotoolsCURIE": "biotools:ldak-gbat",
+    "biotoolsID": "ldak-gbat",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "David Balding"
+        },
+        {
+            "name": "Doug Speed"
+        },
+        {
+            "name": "Takiy-Eddine Berrandou"
+        }
+    ],
+    "description": "Fast and powerful gene-based association testing using summary statistics.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "DNA testing",
+                    "uri": "http://edamontology.org/operation_3920"
+                },
+                {
+                    "term": "Genetic mapping",
+                    "uri": "http://edamontology.org/operation_0282"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://dougspeed.com/ldak-gbat/",
+    "lastUpdate": "2023-02-23T22:45:45.828674Z",
+    "name": "LDAK-GBAT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.AJHG.2022.11.010",
+            "metadata": {
+                "abstract": "© 2022 American Society of Human GeneticsWe present LDAK-GBAT, a tool for gene-based association testing using summary statistics from genome-wide association studies that is computationally efficient, produces well-calibrated p values, and is significantly more powerful than existing tools. LDAK-GBAT takes approximately 30 min to analyze imputed data (2.9M common, genic SNPs), requiring less than 10 Gb memory. It shows good control of type 1 error given an appropriate reference panel. Across 109 phenotypes (82 from the UK Biobank, 18 from the Million Veteran Program, and nine from the Psychiatric Genetics Consortium), LDAK-GBAT finds on average 19% (SE: 1%) more significant genes than the existing tool sumFREGAT-ACAT, with even greater gains in comparison with MAGMA, GCTA-fastBAT, sumFREGAT-SKAT-O, and sumFREGAT-PCA.",
+                "authors": [
+                    {
+                        "name": "Balding D."
+                    },
+                    {
+                        "name": "Berrandou T.-E."
+                    },
+                    {
+                        "name": "Speed D."
+                    }
+                ],
+                "date": "2023-01-05T00:00:00Z",
+                "journal": "American Journal of Human Genetics",
+                "title": "LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics"
+            },
+            "pmid": "36480927"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/libroadrunner/libroadrunner.biotools.json b/data/libroadrunner/libroadrunner.biotools.json
index f81d65fb9cbd9..dc2a0afd969b2 100644
--- a/data/libroadrunner/libroadrunner.biotools.json
+++ b/data/libroadrunner/libroadrunner.biotools.json
@@ -1,10 +1,26 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2017-08-03T19:06:39Z",
     "biotoolsCURIE": "biotools:libroadrunner",
     "biotoolsID": "libroadrunner",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "credit": [
         {
-            "typeEntity": "Person",
+            "email": "hsauro@uw.edu",
+            "name": "Herbert M Sauro",
+            "orcidid": "https://orcid.org/0000-0002-3659-6817"
+        },
+        {
+            "name": "Kiri Choi",
+            "orcidid": "https://orcid.org/0000-0002-0156-8410"
+        },
+        {
+            "name": "Matthias König",
+            "orcidid": "https://orcid.org/0000-0003-1725-179X"
+        },
+        {
+            "typeEntity": "Project",
             "typeRole": [
                 "Primary contact"
             ],
@@ -39,7 +55,8 @@
         "C++",
         "Python"
     ],
-    "lastUpdate": "2018-12-10T12:58:49Z",
+    "lastUpdate": "2023-02-23T22:34:58.418719Z",
+    "license": "Apache-2.0",
     "link": [
         {
             "type": [
@@ -57,6 +74,7 @@
     "owner": "mbs_import",
     "publication": [
         {
+            "doi": "10.1093/bioinformatics/btv363",
             "metadata": {
                 "abstract": "© 2015 Published by Oxford University Press.Motivation: This article presents libRoadRunner, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using Systems Biology Markup Language (SBML). SBML is the most widely used standard for representing dynamic networks, especially biochemical networks. libRoadRunner is fast enough to support large-scale problems such as tissue models, studies that require large numbers of repeated runs and interactive simulations. Results: libRoadRunner is a self-contained library, able to run both as a component inside other tools via its C++ and C bindings, and interactively through its Python interface. Its Python Application Programming Interface (API) is similar to the APIs of MATLAB (www.mathworks.com) and SciPy (http://www.scipy.org/), making it fast and easy to learn. libRoadRunner uses a custom Just-In-Time (JIT) compiler built on the widely used LLVM JIT compiler framework. It compiles SBML-specified models directly into native machine code for a variety of processors, making it appropriate for solving extremely large models or repeated runs. libRoadRunner is flexible, supporting the bulk of the SBML specification (except for delay and non-linear algebraic equations) including several SBML extensions (composition and distributions). It offers multiple deterministic and stochastic integrators, as well as tools for steady-state analysis, stability analysis and structural analysis of the stoichiometric matrix. Availability and implementation: libRoadRunner binary distributions are available for Mac OS X, Linux and Windows. The library is licensed under Apache License Version 2.0. libRoadRunner is also available for ARM-based computers such as the Raspberry Pi. http://www.libroadrunner.org provides online documentation, full build instructions, binaries and a git source repository.",
                 "authors": [
@@ -82,12 +100,18 @@
                         "name": "Swat M.H."
                     }
                 ],
-                "citationCount": 55,
+                "citationCount": 72,
                 "date": "2015-03-25T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "LibRoadRunner: A high performance SBML simulation and analysis library"
             },
+            "pmcid": "PMC4607739",
             "pmid": "26085503"
+        },
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC770",
+            "pmcid": "PMC9825722",
+            "pmid": "36478036"
         }
     ],
     "toolType": [
@@ -103,5 +127,8 @@
             "uri": "http://edamontology.org/topic_2259"
         }
     ],
-    "validated": 1
+    "validated": 1,
+    "version": [
+        "2.0"
+    ]
 }
diff --git a/data/linearsampling/linearsampling.biotools.json b/data/linearsampling/linearsampling.biotools.json
new file mode 100644
index 0000000000000..89a39c5604abe
--- /dev/null
+++ b/data/linearsampling/linearsampling.biotools.json
@@ -0,0 +1,98 @@
+{
+    "additionDate": "2023-02-08T14:49:24.046015Z",
+    "biotoolsCURIE": "biotools:linearsampling",
+    "biotoolsID": "linearsampling",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "liang.huang.sh@gmail.com",
+            "name": "Liang Huang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Fast stochastic sampling of RNA secondary structure with applications to SARS-CoV-2.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "RNA secondary structure alignment",
+                    "uri": "http://edamontology.org/operation_0502"
+                },
+                {
+                    "term": "RNA secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0278"
+                },
+                {
+                    "term": "RNA structure covariance model generation",
+                    "uri": "http://edamontology.org/operation_3469"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/LinearFold/LinearSampling",
+    "language": [
+        "C++",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-08T14:49:24.048614Z",
+    "license": "Other",
+    "name": "LinearSampling",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1029",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Many RNAs fold into multiple structures at equilibrium, and there is a need to sample these structures according to their probabilities in the ensemble. The conventional sampling algorithm suffers from two limitations: (i) the sampling phase is slow due to many repeated calculations; and (ii) the end-to-end runtime scales cubically with the sequence length. These issues make it difficult to be applied to long RNAs, such as the full genomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To address these problems, we devise a new sampling algorithm, LazySampling, which eliminates redundant work via on-demand caching. Based on LazySampling, we further derive LinearSampling, an end-to-end linear time sampling algorithm. Benchmarking on nine diverse RNA families, the sampled structures from LinearSampling correlate better with the well-established secondary structures than Vienna RNAsubopt and RNAplfold. More importantly, LinearSampling is orders of magnitude faster than standard tools, being 428× faster (72 s versus 8.6 h) than RNAsubopt on the full genome of SARS-CoV-2 (29 903 nt). The resulting sample landscape correlates well with the experimentally guided secondary structure models, and is closer to the alternative conformations revealed by experimentally driven analysis. Finally, LinearSampling finds 23 regions of 15 nt with high accessibilities in the SARS-CoV-2 genome, which are potential targets for COVID-19 diagnostics and therapeutics.",
+                "authors": [
+                    {
+                        "name": "Huang L."
+                    },
+                    {
+                        "name": "Li S."
+                    },
+                    {
+                        "name": "Mathews D.H."
+                    },
+                    {
+                        "name": "Zhang H."
+                    },
+                    {
+                        "name": "Zhang L."
+                    }
+                ],
+                "date": "2023-01-25T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "LazySampling and LinearSampling: fast stochastic sampling of RNA secondary structure with applications to SARS-CoV-2"
+            },
+            "pmid": "36401871"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Nucleic acid structure analysis",
+            "uri": "http://edamontology.org/topic_0097"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "Sample collections",
+            "uri": "http://edamontology.org/topic_3277"
+        }
+    ]
+}
diff --git a/data/litcovid_2022/litcovid_2022.biotools.json b/data/litcovid_2022/litcovid_2022.biotools.json
new file mode 100644
index 0000000000000..1e2842fcd5a5e
--- /dev/null
+++ b/data/litcovid_2022/litcovid_2022.biotools.json
@@ -0,0 +1,112 @@
+{
+    "additionDate": "2023-02-08T14:55:37.017089Z",
+    "biotoolsCURIE": "biotools:litcovid_2022",
+    "biotoolsID": "litcovid_2022",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "high",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhiyong.lu@nih.gov",
+            "name": "Zhiyong Lu",
+            "orcidid": "https://orcid.org/0000-0001-9998-916X",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An information resource for the COVID-19 literature.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Text annotation",
+                    "uri": "http://edamontology.org/operation_3778"
+                },
+                {
+                    "term": "Text mining",
+                    "uri": "http://edamontology.org/operation_0306"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.ncbi.nlm.nih.gov/research/coronavirus/",
+    "lastUpdate": "2023-02-08T14:55:37.019862Z",
+    "license": "Other",
+    "name": "LitCovid 2022",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1005",
+            "metadata": {
+                "abstract": "Published by Oxford University Press on behalf of Nucleic Acids Research 2022.LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/)-first launched in February 2020-is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19. The number of articles in LitCovid has increased from 55 000 to ∼300 000 over the past 2.5 years, with a consistent growth rate of ∼10 000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the United States. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last 2 years. First, we introduced the long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating and standardizing the latest knowledge on the COVID-19 literature.",
+                "authors": [
+                    {
+                        "name": "Aghaarabi E."
+                    },
+                    {
+                        "name": "Allot A."
+                    },
+                    {
+                        "name": "Chen Q."
+                    },
+                    {
+                        "name": "Guerrerio J.J."
+                    },
+                    {
+                        "name": "Leaman R."
+                    },
+                    {
+                        "name": "Lu Z."
+                    },
+                    {
+                        "name": "Wei C.-H."
+                    },
+                    {
+                        "name": "Xu L."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "LitCovid in 2022: an information resource for the COVID-19 literature"
+            },
+            "pmcid": "PMC9825538",
+            "pmid": "36350613"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cardiology",
+            "uri": "http://edamontology.org/topic_3335"
+        },
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        }
+    ]
+}
diff --git a/data/lmas/lmas.biotools.json b/data/lmas/lmas.biotools.json
new file mode 100644
index 0000000000000..37a02d21e31c5
--- /dev/null
+++ b/data/lmas/lmas.biotools.json
@@ -0,0 +1,147 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-23T22:20:57.528905Z",
+    "biotoolsCURIE": "biotools:lmas",
+    "biotoolsID": "lmas",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "cimendes@medicina.ulisboa.pt",
+            "name": "Catarina Inês Mendes",
+            "orcidid": "https://orcid.org/0000-0002-3090-7426",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jacob Moran-Gilad",
+            "orcidid": "https://orcid.org/0000-0001-9134-050X"
+        },
+        {
+            "name": "João André Carriço",
+            "orcidid": "https://orcid.org/0000-0002-5274-2722"
+        },
+        {
+            "name": "Mário Ramirez",
+            "orcidid": "https://orcid.org/0000-0002-4084-6233"
+        },
+        {
+            "name": "Pedro Vila-Cerqueira",
+            "orcidid": "https://orcid.org/0000-0002-6121-8906"
+        },
+        {
+            "name": "Yair Motro",
+            "orcidid": "https://orcid.org/0000-0003-1289-6919"
+        }
+    ],
+    "description": "Evaluating metagenomic short de novo assembly methods through defined communities.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://lmas.readthedocs.io/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "De-novo assembly",
+                    "uri": "http://edamontology.org/operation_0524"
+                },
+                {
+                    "term": "Genome assembly",
+                    "uri": "http://edamontology.org/operation_0525"
+                },
+                {
+                    "term": "Mapping assembly",
+                    "uri": "http://edamontology.org/operation_0523"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                },
+                {
+                    "term": "de Novo sequencing",
+                    "uri": "http://edamontology.org/operation_3644"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/B-UMMI/LMAS",
+    "language": [
+        "Groovy",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-23T22:20:57.531365Z",
+    "license": "GPL-3.0",
+    "name": "LMAS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/GIGASCIENCE/GIAC122",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press GigaScience.BACKGROUND: The de novo assembly of raw sequence data is key in metagenomic analysis. It allows recovering draft genomes from a pool of mixed raw reads, yielding longer sequences that offer contextual information and provide a more complete picture of the microbial community. FINDINGS: To better compare de novo assemblers for metagenomic analysis, LMAS (Last Metagenomic Assembler Standing) was developed as a flexible platform allowing users to evaluate assembler performance given known standard communities. Overall, in our test datasets, k-mer De Bruijn graph assemblers outperformed the alternative approaches but came with a greater computational cost. Furthermore, assemblers branded as metagenomic specific did not consistently outperform other genomic assemblers in metagenomic samples. Some assemblers still in use, such as ABySS, MetaHipmer2, minia, and VelvetOptimiser, perform relatively poorly and should be used with caution when assembling complex samples. Meaningful strain resolution at the single-nucleotide polymorphism level was not achieved, even by the best assemblers tested. CONCLUSIONS: The choice of a de novo assembler depends on the computational resources available, the replicon of interest, and the major goals of the analysis. No single assembler appeared an ideal choice for short-read metagenomic prokaryote replicon assembly, each showing specific strengths. The choice of metagenomic assembler should be guided by user requirements and characteristics of the sample of interest, and LMAS provides an interactive evaluation platform for this purpose. LMAS is open source, and the workflow and its documentation are available at https://github.com/B-UMMI/LMAS and https://lmas.readthedocs.io/, respectively.",
+                "authors": [
+                    {
+                        "name": "Carrico J.A."
+                    },
+                    {
+                        "name": "Mendes C.I."
+                    },
+                    {
+                        "name": "Moran-Gilad J."
+                    },
+                    {
+                        "name": "Motro Y."
+                    },
+                    {
+                        "name": "Ramirez M."
+                    },
+                    {
+                        "name": "Vila-Cerqueira P."
+                    }
+                ],
+                "date": "2022-12-28T00:00:00Z",
+                "journal": "GigaScience",
+                "title": "LMAS: evaluating metagenomic short de novo assembly methods through defined communities"
+            },
+            "pmcid": "PMC9795473",
+            "pmid": "36576131"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Metagenomic sequencing",
+            "uri": "http://edamontology.org/topic_3837"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/lmerseq/lmerseq.biotools.json b/data/lmerseq/lmerseq.biotools.json
new file mode 100644
index 0000000000000..a7f75fdebc4af
--- /dev/null
+++ b/data/lmerseq/lmerseq.biotools.json
@@ -0,0 +1,90 @@
+{
+    "additionDate": "2023-02-08T14:58:41.446382Z",
+    "biotoolsCURIE": "biotools:lmerseq",
+    "biotoolsID": "lmerseq",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "email": "vestalb@njhealth.org",
+            "name": "Brian E. Vestal",
+            "orcidid": "https://orcid.org/0000-0002-3772-1691",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An R package for analyzing transformed RNA-Seq data with linear mixed effects models.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Modelling and simulation",
+                    "uri": "http://edamontology.org/operation_2426"
+                },
+                {
+                    "term": "RNA-Seq analysis",
+                    "uri": "http://edamontology.org/operation_3680"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/stop-pre16/lmerSeq",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-08T14:58:41.450763Z",
+    "license": "Not licensed",
+    "name": "lmerSeq",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05019-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Studies that utilize RNA Sequencing (RNA-Seq) in conjunction with designs that introduce dependence between observations (e.g. longitudinal sampling) require specialized analysis tools to accommodate this additional complexity. This R package contains a set of utilities to fit linear mixed effects models to transformed RNA-Seq counts that properly account for this dependence when performing statistical analyses. Results: In a simulation study comparing lmerSeq and two existing methodologies that also work with transformed RNA-Seq counts, we found that lmerSeq was comprehensively better in terms of nominal error rate control and statistical power. Conclusions: Existing R packages for analyzing transformed RNA-Seq data with linear mixed models are limited in the variance structures they allow and/or the transformation methods they support. The lmerSeq package offers more flexibility in both of these areas and gave substantially better results in our simulations.",
+                "authors": [
+                    {
+                        "name": "Moore C.M."
+                    },
+                    {
+                        "name": "Vestal B.E."
+                    },
+                    {
+                        "name": "Wynn E."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models"
+            },
+            "pmcid": "PMC9670578",
+            "pmid": "36384492"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/lncbook/lncbook.biotools.json b/data/lncbook/lncbook.biotools.json
index b4bf5584fc209..bfb0757065338 100644
--- a/data/lncbook/lncbook.biotools.json
+++ b/data/lncbook/lncbook.biotools.json
@@ -3,7 +3,20 @@
     "biotoolsCURIE": "biotools:LncBook",
     "biotoolsID": "LncBook",
     "confidence_flag": "tool",
-    "description": "Community Curation and Expert Curation of Human Long Noncoding RNAs with LncRNAWiki and LncBook | LncBook a curated knowledgebase of human long non-coding RNAs | To facilitate overall investigation of various RNAs, a comprehensive RNA reference dataset was created, including lncRNA annotations from LncBook and other RNAs’annotations derived from GENCODE v31",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "malina@big.ac.cn",
+            "name": "Lina Ma",
+            "orcidid": "https://orcid.org/0000-0001-6390-6289"
+        },
+        {
+            "email": "zhangzhang@big.ac.cn",
+            "name": "Zhang Zhang",
+            "orcidid": "https://orcid.org/0000-0001-6603-5060"
+        }
+    ],
+    "description": "LncBook accommodates a high-quality collection of human lncRNA genes and incorporates their abundant annotations at different omics levels, thereby enabling users to decipher functional signatures of lncRNAs in human diseases and different biological contexts.",
     "editPermission": {
         "type": "public"
     },
@@ -13,13 +26,34 @@
                 {
                     "term": "Expression analysis",
                     "uri": "http://edamontology.org/operation_2495"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                },
+                {
+                    "term": "miRNA expression analysis",
+                    "uri": "http://edamontology.org/operation_3792"
                 }
             ]
         }
     ],
-    "homepage": "http://bigd.big.ac.cn/lncbook",
-    "lastUpdate": "2020-12-22T11:15:44Z",
+    "homepage": "https://ngdc.cncb.ac.cn/lncbook",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-08T15:04:18.610858Z",
+    "license": "CC-BY-4.0",
     "name": "LncBook",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "Pub2Tools",
     "publication": [
         {
@@ -64,7 +98,7 @@
                         "name": "Zou D."
                     }
                 ],
-                "citationCount": 3,
+                "citationCount": 6,
                 "date": "2019-09-01T00:00:00Z",
                 "journal": "Current Protocols in Bioinformatics",
                 "title": "Community Curation and Expert Curation of Human Long Noncoding RNAs with LncRNAWiki and LncBook"
@@ -89,5 +123,8 @@
             "uri": "http://edamontology.org/topic_0634"
         }
     ],
-    "validated": 1
+    "validated": 1,
+    "version": [
+        "2.0"
+    ]
 }
diff --git a/data/lncdc/lncdc.biotools.json b/data/lncdc/lncdc.biotools.json
new file mode 100644
index 0000000000000..d2ea5223f6207
--- /dev/null
+++ b/data/lncdc/lncdc.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T15:07:11.267256Z",
+    "biotoolsCURIE": "biotools:lncdc",
+    "biotoolsID": "lncdc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "liangc@miamioh.edu",
+            "name": "Chun Liang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "lim74@miamioh.edu",
+            "name": "Minghua Li",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A machine learning-based tool for long non-coding RNA detection from RNA-Seq data.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Protein secondary structure comparison",
+                    "uri": "http://edamontology.org/operation_2488"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                },
+                {
+                    "term": "RNA secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0278"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/lim74/LncDC",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-08T15:07:11.269970Z",
+    "license": "MIT",
+    "name": "LncDC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1038/S41598-022-22082-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Long non-coding RNAs (lncRNAs) play an essential role in diverse biological processes and disease development. Accurate classification of lncRNAs and mRNAs is important for the identification of tissue- or disease-specific lncRNAs. Here, we present our tool LncDC (Long non-coding RNA detection) that is able to accurately predict lncRNAs with an XGBoost model using features extracted from RNA sequences, secondary structures, and translated proteins. Benchmarking experiments showed that LncDC consistently outperformed six state-of-the-art tools in distinguishing lncRNAs from mRNAs. Notably, the use of sequence and secondary structure (SASS) k-mer score features and flexible ORF features improved the classification capability of LncDC. We anticipate that LncDC will definitely promote the discovery of more and novel disease-specific lncRNAs. LncDC is implemented in Python and freely available at https://github.com/lim74/LncDC.",
+                "authors": [
+                    {
+                        "name": "Li M."
+                    },
+                    {
+                        "name": "Liang C."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "LncDC: a machine learning-based tool for long non-coding RNA detection from RNA-Seq data"
+            },
+            "pmcid": "PMC9646749",
+            "pmid": "36351980"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/lncrnasnp/lncrnasnp.biotools.json b/data/lncrnasnp/lncrnasnp.biotools.json
new file mode 100644
index 0000000000000..ec593d4a7dae2
--- /dev/null
+++ b/data/lncrnasnp/lncrnasnp.biotools.json
@@ -0,0 +1,125 @@
+{
+    "additionDate": "2023-02-08T15:09:44.240118Z",
+    "biotoolsCURIE": "biotools:lncrnasnp",
+    "biotoolsID": "lncrnasnp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "gong.jing@mail.hzau.edu.cn",
+            "name": "Jing Gong",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "guoay@hust.edu.cn",
+            "name": "An-Yuan Guo",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An database for functional variants in long non-coding RNAs.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "SNP annotation",
+                    "uri": "http://edamontology.org/operation_3661"
+                },
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://gong_lab.hzau.edu.cn/lncRNASNP3/",
+    "lastUpdate": "2023-02-08T15:09:44.243588Z",
+    "license": "Other",
+    "name": "lncRNASNP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC981",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Long non-coding RNAs (lncRNAs) act as versatile regulators of many biological processes and play vital roles in various diseases. lncRNASNP is dedicated to providing a comprehensive repository of single nucleotide polymorphisms (SNPs) and somatic mutations in lncRNAs and their impacts on lncRNA structure and function. Since the last release in 2018, there has been a huge increase in the number of variants and lncRNAs. Thus, we updated the lncRNASNP to version 3 by expanding the species to eight eukaryotic species (human, chimpanzee, pig, mouse, rat, chicken, zebrafish, and fruitfly), updating the data and adding several new features. SNPs in lncRNASNP have increased from 11 181 387 to 67 513 785. The human mutations have increased from 1 174 768 to 2 387 685, including 1 031 639 TCGA mutations and 1 356 046 CosmicNCVs. Compared with the last release, updated and new features in lncRNASNP v3 include (i) SNPs in lncRNAs and their impacts on lncRNAs for eight species, (ii) SNP effects on miRNA-lncRNA interactions for eight species, (iii) lncRNA expression profiles for six species, (iv) disease & GWAS-associated lncRNAs and variants, (v) experimental & predicted lncRNAs and drug target associations and (vi) SNP effects on lncRNA expression (eQTL) across tumor & normal tissues. The lncRNASNP v3 is freely available at http://gong_lab.hzau.edu.cn/lncRNASNP3/.",
+                "authors": [
+                    {
+                        "name": "Cao W."
+                    },
+                    {
+                        "name": "Gong J."
+                    },
+                    {
+                        "name": "Guo A.-Y."
+                    },
+                    {
+                        "name": "Luo H."
+                    },
+                    {
+                        "name": "Miao Y.-R."
+                    },
+                    {
+                        "name": "Wang D."
+                    },
+                    {
+                        "name": "Wu X."
+                    },
+                    {
+                        "name": "Yang J."
+                    },
+                    {
+                        "name": "Yang W."
+                    },
+                    {
+                        "name": "Yang Y."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "lncRNASNP v3: an updated database for functional variants in long non-coding RNAs"
+            },
+            "pmcid": "PMC9825536",
+            "pmid": "36350671"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ],
+    "version": [
+        "3.0"
+    ]
+}
diff --git a/data/lnm/lnm.biotools.json b/data/lnm/lnm.biotools.json
new file mode 100644
index 0000000000000..97bdefd3c0892
--- /dev/null
+++ b/data/lnm/lnm.biotools.json
@@ -0,0 +1,187 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-23T22:10:57.134188Z",
+    "biotoolsCURIE": "biotools:lnm",
+    "biotoolsID": "lnm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chengliangyin@163.com",
+            "name": "Chengliang Yin",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "dxzhao@jlu.edu.cn",
+            "name": "Dongxu Zhao",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhouhui@tmu.edu.cnn",
+            "name": "Hui Zhou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Wenle Li",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Early distinction of lymph node metastasis in patients with soft tissue sarcoma and individualized survival prediction using the online available nomograms.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Incident curve plotting",
+                    "uri": "http://edamontology.org/operation_3503"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://tyxupup.shinyapps.io/OSofSTSpatientswithLNM/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-23T22:10:57.137052Z",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py"
+        },
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://tyxupup.shinyapps.io/CSSofSTSpatientswithLNM/"
+        },
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://tyxupup.shinyapps.io/probabilityofLNMforSTSpatients/"
+        }
+    ],
+    "name": "LNM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FENDO.2022.1054358",
+            "metadata": {
+                "abstract": "Copyright © 2022 Feng, Hong, Liu, Xu, Li, Yang, Song, Li, Li, Zhou and Yin.Background: Lymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer. Methods: Data on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds. Results: The training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients. Conclusions: The predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.",
+                "authors": [
+                    {
+                        "name": "Feng X."
+                    },
+                    {
+                        "name": "Hong T."
+                    },
+                    {
+                        "name": "Li T."
+                    },
+                    {
+                        "name": "Li W."
+                    },
+                    {
+                        "name": "Li W."
+                    },
+                    {
+                        "name": "Liu W."
+                    },
+                    {
+                        "name": "Song Y."
+                    },
+                    {
+                        "name": "Xu C."
+                    },
+                    {
+                        "name": "Yang B."
+                    },
+                    {
+                        "name": "Yin C."
+                    },
+                    {
+                        "name": "Zhou H."
+                    }
+                ],
+                "date": "2022-11-18T00:00:00Z",
+                "journal": "Frontiers in Endocrinology",
+                "title": "Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma"
+            },
+            "pmcid": "PMC9716136",
+            "pmid": "36465636"
+        },
+        {
+            "doi": "10.3389/FONC.2022.959804",
+            "metadata": {
+                "abstract": "Copyright © 2022 Tong, Pi, Cui, Jiang, Gong and Zhao.Background: The presence of metastatic tumor cells in regional lymph nodes is considered as a significant indicator for inferior prognosis. This study aimed to construct some predictive models to quantify the probability of lymph node metastasis (LNM) and survival rate of patients with soft tissue sarcoma (STS) with LNM. Methods: Research data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2017, and data of patients with STS from our medical institution were collected to form an external testing set. Univariate and multivariate logistic regression analyses were used to determine the independent risk factors for developing LNM. On the basis of the identified variables, we developed a diagnostic nomogram to predict the risk of LNM in patients with STS. Those patients with STS presenting with LNM were retrieved to build a cohort for identifying the independent prognostic factors through univariate and multivariate Cox regression analysis. Then, two nomograms incorporating the independent prognostic predictors were developed to predict the overall survival (OS) and cancer-specific survival (CSS) for patients with STS with LNM. Kaplan–Meier (K-M) survival analysis was conducted to study the survival difference. Moreover, validations of these nomograms were performed by the receiver operating characteristic curves, the area under the curve, calibration curves, and the decision curve analysis (DCA). Results: A total of 16,601 patients with STS from the SEER database were enrolled in our study, of which 659 (3.97%) had LNM at the initial diagnosis. K-M survival analysis indicated that patients with LNM had poorer survival rate. Sex, histology, primary site, grade, M stage, and T stage were found to be independently related with development of LNM in patients with STS. Age, grade, histology, M stage, T stage, chemotherapy, radiotherapy, and surgery were identified as the independent prognostic factors for OS of patients with STS with LNM, and age, grade, M stage, T stage, radiotherapy, and surgery were determined as the independent prognostic factors for CSS. Subsequently, we constructed three nomograms, and their online versions are as follows: https://tyxupup.shinyapps.io/probabilityofLNMforSTSpatients/, https://tyxupup.shinyapps.io/OSofSTSpatientswithLNM/, and https://tyxupup.shinyapps.io/CSSofSTSpatientswithLNM/. The areas under the curve (AUCs) of diagnostic nomogram were 0.839 in the training set, 0.811 in the testing set, and 0.852 in the external testing set. For prognostic nomograms, the AUCs of 24-, 36-, and 48-month OS were 0.820, 0.794, and 0.792 in the training set and 0.759, 0.728, and 0.775 in the testing set, respectively; the AUCs of 24-, 36-, and 48-month CSS were 0.793, 0.777, and 0.775 in the training set and 0.775, 0.744, and 0.738 in the testing set, respectively. Furthermore, calibration curves suggested that the predicted values were consistent with the actual values. For the DCA, our nomograms showed a superior net benefit across a wider scale of threshold probabilities for the prediction of risk and survival rate for patients with STS with LNM. Conclusion: These newly proposed nomograms promise to be useful tools in predicting the risk of LNM for patients with STS and individualized survival prediction for patients with STS with LNM, which may help to guide clinical practice.",
+                "authors": [
+                    {
+                        "name": "Cui Y."
+                    },
+                    {
+                        "name": "Gong Y."
+                    },
+                    {
+                        "name": "Jiang L."
+                    },
+                    {
+                        "name": "Pi Y."
+                    },
+                    {
+                        "name": "Tong Y."
+                    },
+                    {
+                        "name": "Zhao D."
+                    }
+                ],
+                "date": "2022-12-07T00:00:00Z",
+                "journal": "Frontiers in Oncology",
+                "title": "Early distinction of lymph node metastasis in patients with soft tissue sarcoma and individualized survival prediction using the online available nomograms: A population-based analysis"
+            },
+            "pmcid": "PMC9767978",
+            "pmid": "36568161"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        },
+        {
+            "term": "Surgery",
+            "uri": "http://edamontology.org/topic_3421"
+        },
+        {
+            "term": "Urology and nephrology",
+            "uri": "http://edamontology.org/topic_3422"
+        }
+    ]
+}
diff --git a/data/ltm/ltm.biotools.json b/data/ltm/ltm.biotools.json
new file mode 100644
index 0000000000000..f79cd46755b5c
--- /dev/null
+++ b/data/ltm/ltm.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-23T09:05:06.453195Z",
+    "biotoolsCURIE": "biotools:ltm",
+    "biotoolsID": "ltm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "john.hogenesch@cchmc.org",
+            "name": "John B. Hogenesch",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Gang Wu"
+        },
+        {
+            "name": "Ron C. Anafi"
+        },
+        {
+            "name": "Marc D. Ruben",
+            "orcidid": "http://orcid.org/0000-0002-7893-0238"
+        }
+    ],
+    "description": "LTM is an in silico screen to infer genetic influences on circadian clock function. LTM uses natural variation in gene expression data and directly links gene expression variation to clock strength independent of longitudinal data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Pathway analysis",
+                    "uri": "http://edamontology.org/operation_3928"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/gangwug/LTMR",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-23T09:05:06.456750Z",
+    "license": "Not licensed",
+    "name": "LTM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac686",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Years of time-series gene expression studies have built a strong understanding of clock-controlled pathways across species. However, comparatively little is known about how 'non-clock' pathways influence clock function. We need a strong understanding of clock-coupled pathways in human tissues to better appreciate the links between disease and clock function. RESULTS: We developed a new computational approach to explore candidate pathways coupled to the clock in human tissues. This method, termed LTM, is an in silico screen to infer genetic influences on circadian clock function. LTM uses natural variation in gene expression in human data and directly links gene expression variation to clock strength independent of longitudinal data. We applied LTM to three human skin and one melanoma datasets and found that the cell cycle is the top candidate clock-coupled pathway in healthy skin. In addition, we applied LTM to thousands of tumor samples from 11 cancer types in the TCGA database and found that extracellular matrix organization-related pathways are tightly associated with the clock strength in humans. Further analysis shows that clock strength in tumor samples is correlated with the proportion of cancer-associated fibroblasts and endothelial cells. Therefore, we show both the power of LTM in predicting clock-coupled pathways and classify factors associated with clock strength in human tissues. AVAILABILITY AND IMPLEMENTATION: LTM is available on GitHub (https://github.com/gangwug/LTMR) and figshare (https://figshare.com/articles/software/LTMR/21217604) to facilitate its use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Anafi R.C."
+                    },
+                    {
+                        "name": "Francey L.J."
+                    },
+                    {
+                        "name": "Hogenesch J.B."
+                    },
+                    {
+                        "name": "Lee Y.Y."
+                    },
+                    {
+                        "name": "Ruben M.D."
+                    },
+                    {
+                        "name": "Wu G."
+                    }
+                ],
+                "date": "2022-12-13T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "An in silico genome-wide screen for circadian clock strength in human samples"
+            },
+            "pmcid": "PMC9750125",
+            "pmid": "36321857"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/luna/luna.biotools.json b/data/luna/luna.biotools.json
new file mode 100644
index 0000000000000..262982a2dbb83
--- /dev/null
+++ b/data/luna/luna.biotools.json
@@ -0,0 +1,129 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T14:49:25.884030Z",
+    "biotoolsCURIE": "biotools:luna",
+    "biotoolsID": "luna",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Alexandre V. Fassio",
+            "orcidid": "http://orcid.org/0000-0002-2182-4709"
+        },
+        {
+            "name": "Laura Shub",
+            "orcidid": "http://orcid.org/0000-0003-0211-0396"
+        },
+        {
+            "name": "Michael J. Keiser",
+            "orcidid": "http://orcid.org/0000-0002-1240-2192"
+        },
+        {
+            "name": "Raquel C. de Melo Minardi",
+            "orcidid": "http://orcid.org/0000-0001-5190-100X"
+        }
+    ],
+    "description": "Prioritizing virtual screening with interpretable interaction fingerprints.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://luna-toolkit.readthedocs.io/en/latest/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein fragment weight comparison",
+                    "uri": "http://edamontology.org/operation_2929"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/keiserlab/LUNA",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T14:49:25.886492Z",
+    "license": "MIT",
+    "name": "LUNA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/acs.jcim.2c00695",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society.Machine learning-based drug discovery success depends on molecular representation. Yet traditional molecular fingerprints omit both the protein and pointers back to structural information that would enable better model interpretability. Therefore, we propose LUNA, a Python 3 toolkit that calculates and encodes protein-ligand interactions into new hashed fingerprints inspired by Extended Connectivity FingerPrint (ECFP): EIFP (Extended Interaction FingerPrint), FIFP (Functional Interaction FingerPrint), and Hybrid Interaction FingerPrint (HIFP). LUNA also provides visual strategies to make the fingerprints interpretable. We performed three major experiments exploring the fingerprints' use. First, we trained machine learning models to reproduce DOCK3.7 scores using 1 million docked Dopamine D4 complexes. We found that EIFP-4,096 performed (R2 = 0.61) superior to related molecular and interaction fingerprints. Second, we used LUNA to support interpretable machine learning models. Finally, we demonstrate that interaction fingerprints can accurately identify similarities across molecular complexes that other fingerprints overlook. Hence, we envision LUNA and its interface fingerprints as promising methods for machine learning-based virtual screening campaigns. LUNA is freely available at https://github.com/keiserlab/LUNA.",
+                "authors": [
+                    {
+                        "name": "De Melo Minardi R.C."
+                    },
+                    {
+                        "name": "Fassio A.V."
+                    },
+                    {
+                        "name": "Ferreira R.S."
+                    },
+                    {
+                        "name": "Keiser M.J."
+                    },
+                    {
+                        "name": "McKinley J."
+                    },
+                    {
+                        "name": "O'Meara M.J."
+                    },
+                    {
+                        "name": "Ponzoni L."
+                    },
+                    {
+                        "name": "Shub L."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-09-26T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "Prioritizing Virtual Screening with Interpretable Interaction Fingerprints"
+            },
+            "pmid": "36102784"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        }
+    ]
+}
diff --git a/data/macadamiaggd/macadamiaggd.biotools.json b/data/macadamiaggd/macadamiaggd.biotools.json
new file mode 100644
index 0000000000000..388754978e537
--- /dev/null
+++ b/data/macadamiaggd/macadamiaggd.biotools.json
@@ -0,0 +1,117 @@
+{
+    "additionDate": "2023-02-08T15:13:58.168347Z",
+    "biotoolsCURIE": "biotools:macadamiaggd",
+    "biotoolsID": "macadamiaggd",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "nijun@gxu.edu.cn",
+            "name": "Jun Ni",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zfxu@gxu.edu.cn",
+            "name": "Zeng-Fu Xu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A comprehensive platform for germplasm innovation and functional genomics in Macadamia.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genetic mapping",
+                    "uri": "http://edamontology.org/operation_0282"
+                },
+                {
+                    "term": "Genome alignment",
+                    "uri": "http://edamontology.org/operation_3182"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "PCR primer design",
+                    "uri": "http://edamontology.org/operation_0308"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://MacadamiaGGD.net",
+    "lastUpdate": "2023-02-08T15:13:58.171132Z",
+    "license": "Other",
+    "name": "MacadamiaGGD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.3389/FPLS.2022.1007266",
+            "metadata": {
+                "abstract": "Copyright © 2022 Wang, Mo, Wang, Fei, Huang, Ni and Xu.As an important nut crop species, macadamia continues to gain increased amounts of attention worldwide. Nevertheless, with the vast increase in macadamia omic data, it is becoming difficult for researchers to effectively process and utilize the information. In this work, we developed the first integrated germplasm and genomic database for macadamia (MacadamiaGGD), which includes five genomes of four species; three chloroplast and mitochondrial genomes; genome annotations; transcriptomic data for three macadamia varieties, germplasm data for four species and 262 main varieties; nine genetic linkage maps; and 35 single-nucleotide polymorphisms (SNPs). The database serves as a valuable collection of simple sequence repeat (SSR) markers, including both markers that are based on macadamia genomic sequences and developed in this study and markers developed previously. MacadamiaGGD is also integrated with multiple bioinformatic tools, such as search, JBrowse, BLAST, primer designer, sequence fetch, enrichment analysis, multiple sequence alignment, genome alignment, and gene homology annotation, which allows users to conveniently analyze their data of interest. MacadamiaGGD is freely available online (http://MacadamiaGGD.net). We believe that the database and additional information of the SSR markers can help scientists better understand the genomic sequence information of macadamia and further facilitate molecular breeding efforts of this species.",
+                "authors": [
+                    {
+                        "name": "Fei Y."
+                    },
+                    {
+                        "name": "Huang J."
+                    },
+                    {
+                        "name": "Mo Y."
+                    },
+                    {
+                        "name": "Ni J."
+                    },
+                    {
+                        "name": "Wang P."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Xu Z.-F."
+                    }
+                ],
+                "date": "2022-10-27T00:00:00Z",
+                "journal": "Frontiers in Plant Science",
+                "title": "Macadamia germplasm and genomic database (MacadamiaGGD): A comprehensive platform for germplasm innovation and functional genomics in Macadamia"
+            },
+            "pmcid": "PMC9646992",
+            "pmid": "36388568"
+        }
+    ],
+    "toolType": [
+        "Desktop application",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Probes and primers",
+            "uri": "http://edamontology.org/topic_0632"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/mag-sd/mag-sd.biotools.json b/data/mag-sd/mag-sd.biotools.json
new file mode 100644
index 0000000000000..49b6c114f0bc0
--- /dev/null
+++ b/data/mag-sd/mag-sd.biotools.json
@@ -0,0 +1,113 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T00:34:34.183689Z",
+    "biotoolsCURIE": "biotools:mag-sd",
+    "biotoolsID": "mag-sd",
+    "collectionID": [
+        "COVID-19",
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "wangyaqi@hdu.edu.cn",
+            "name": "Yaqi Wang",
+            "orcidid": "https://orcid.org/0000-0002-4627-3392",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jingxiong Li",
+            "orcidid": "https://orcid.org/0000-0002-6519-5043"
+        },
+        {
+            "name": "Lingling Sun",
+            "orcidid": "https://orcid.org/0000-0002-6410-1471"
+        },
+        {
+            "name": "Qun Jin",
+            "orcidid": "https://orcid.org/0000-0002-1325-4275"
+        }
+    ],
+    "description": "MAG-SD is an image classification model focusing on pneumonia (including COVID-19) using CXR images.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/JasonLeeGHub/MAG-SD",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-27T00:34:34.186171Z",
+    "license": "Not licensed",
+    "name": "MAG-SD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1109/JBHI.2021.3058293",
+            "metadata": {
+                "abstract": "© 2013 IEEE.Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at https://github.com/JasonLeeGHub/MAG-SD.",
+                "authors": [
+                    {
+                        "name": "Jin Q."
+                    },
+                    {
+                        "name": "Li J."
+                    },
+                    {
+                        "name": "Liu J."
+                    },
+                    {
+                        "name": "Sun L."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Wang Y."
+                    }
+                ],
+                "citationCount": 18,
+                "date": "2021-05-01T00:00:00Z",
+                "journal": "IEEE Journal of Biomedical and Health Informatics",
+                "title": "Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images"
+            },
+            "pmcid": "PMC8545167",
+            "pmid": "33560995"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Tomography",
+            "uri": "http://edamontology.org/topic_3452"
+        }
+    ]
+}
diff --git a/data/magmd/magmd.biotools.json b/data/magmd/magmd.biotools.json
new file mode 100644
index 0000000000000..27da7d09d1be4
--- /dev/null
+++ b/data/magmd/magmd.biotools.json
@@ -0,0 +1,129 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-23T21:27:01.110525Z",
+    "biotoolsCURIE": "biotools:magmd",
+    "biotoolsID": "magmd",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jwu@bio.ecnu.edu.cn",
+            "name": "Wu Jun",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "tlshi@bio.encu.edu.cn",
+            "name": "Tieliu Shi",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Haipeng Qin"
+        },
+        {
+            "name": "Jiajia Zhou"
+        },
+        {
+            "name": "Jian Ouyang"
+        },
+        {
+            "name": "Zihao Gao"
+        }
+    ],
+    "description": "MagMD is a database mainly describing the interactions between human gut microbes, enzymes and active substances. Later, we may add some prediction results of the interaction of gut microbes, enzymes and active substances(mainly pharmaceutical).",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Organism name",
+                        "uri": "http://edamontology.org/data_2909"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.unimd.org/magmd",
+    "lastUpdate": "2023-02-23T21:27:01.113553Z",
+    "name": "MagMD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.11.021",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsAn increasing number of studies have reported that microbiome can affect drug response by altering pharmacokinetics and pharmacodynamics of formation of toxic metabolites. With the development of metagenomic sequencing, gut microbial composition as well as the metabolic function are drawing more and more attention for the patient stratification. The established microbiota databases provide useful information about the gut microbe-drug interactions. However, these databases generally lacked the detailed effects on substance and the metabolites, which are helpful in elucidating the mechanisms underlying drug biotransformation and personalized medicine. To address these issues, in this study, we developed Metabolic action of gut Microbiota to Drugs (MagMD), a database and a web-service covering 32, 678 records of interactions between 2,146 gut microbes, 36 enzymes and 219 substrates (mainly drugs). The detailed annotations for each entry, including the taxonomic level of microbes, the molecular form and PubChem ID of drugs from PubChem Compound Database, types of microbial secreted enzymes and the original reference links can also be accessed from the web service. Availability and implementation: MagMD is a publicly available resource, constantly updated. It has an intuitive web interface and can be freely accessed at http://www.unimd.org/magmd.",
+                "authors": [
+                    {
+                        "name": "Gao Z."
+                    },
+                    {
+                        "name": "Jun W."
+                    },
+                    {
+                        "name": "Ouyang J."
+                    },
+                    {
+                        "name": "Qin H."
+                    },
+                    {
+                        "name": "Shi T."
+                    },
+                    {
+                        "name": "Zhou J."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "MagMD: Database summarizing the metabolic action of gut microbiota to drugs"
+            },
+            "pmcid": "PMC9685347",
+            "pmid": "36467581"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Drug metabolism",
+            "uri": "http://edamontology.org/topic_3375"
+        },
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Enzymes",
+            "uri": "http://edamontology.org/topic_0821"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/malpaca/malpaca.biotools.json b/data/malpaca/malpaca.biotools.json
new file mode 100644
index 0000000000000..b518de3819db1
--- /dev/null
+++ b/data/malpaca/malpaca.biotools.json
@@ -0,0 +1,120 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-23T21:22:45.955045Z",
+    "biotoolsCURIE": "biotools:malpaca",
+    "biotoolsID": "malpaca",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "maga@uw.edu",
+            "name": "A. Murat Maga",
+            "orcidid": "https://orcid.org/0000-0002-7921-9018",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Altan Kocatulum"
+        },
+        {
+            "name": "Arthur Porto"
+        },
+        {
+            "name": "Sara Rolfe"
+        },
+        {
+            "name": "Chi Zhang",
+            "orcidid": "https://orcid.org/0000-0002-0418-6354"
+        }
+    ],
+    "description": "Automated landmarking through pointcloud alignment and correspondence analysis.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/SlicerMorph/Tutorials/blob/main/MALPACA/MALPACA.md"
+        }
+    ],
+    "download": [
+        {
+            "type": "Other",
+            "url": "https://github.com/SlicerMorph/Mouse_Models"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Principal component analysis",
+                    "uri": "http://edamontology.org/operation_3960"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/SlicerMorph/Tutorials/tree/main/ALPACA",
+    "lastUpdate": "2023-02-23T21:22:45.957895Z",
+    "name": "MALPACA",
+    "operatingSystem": [
+        "Linux",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PONE.0278035",
+            "metadata": {
+                "abstract": "Copyright: © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Manually collecting landmarks for quantifying complex morphological phenotypes can be laborious and subject to intra and interobserver errors. However, most automated landmarking methods for efficiency and consistency fall short of landmarking highly variable samples due to the bias introduced by the use of a single template. We introduce a fast and open source automated landmarking pipeline (MALPACA) that utilizes multiple templates for accommodating large-scale variations. We also introduce a K-means method of choosing the templates that can be used in conjunction with MALPACA, when no prior information for selecting templates is available. Our results confirm that MALPACA significantly outperforms single-template methods in landmarking both single and multi-species samples. K-means based template selection can also avoid choosing the worst set of templates when compared to random template selection. We further offer an example of post-hoc quality check for each individual template for further refinement. In summary, MALPACA is an efficient and reproducible method that can accommodate large morphological variability, such as those commonly found in evolutionary studies. To support the research community, we have developed open-source and user-friendly software tools for performing K-means multitemplates selection and MALPACA.",
+                "authors": [
+                    {
+                        "name": "Kocatulum A."
+                    },
+                    {
+                        "name": "Maga A.M."
+                    },
+                    {
+                        "name": "Porto A."
+                    },
+                    {
+                        "name": "Rolfe S."
+                    },
+                    {
+                        "name": "Zhang C."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "Automated landmarking via multiple templates"
+            },
+            "pmcid": "PMC9714854",
+            "pmid": "36454982"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Gynaecology and obstetrics",
+            "uri": "http://edamontology.org/topic_3411"
+        },
+        {
+            "term": "Sample collections",
+            "uri": "http://edamontology.org/topic_3277"
+        }
+    ]
+}
diff --git a/data/manyfold/manyfold.biotools.json b/data/manyfold/manyfold.biotools.json
new file mode 100644
index 0000000000000..ff68e00e2187e
--- /dev/null
+++ b/data/manyfold/manyfold.biotools.json
@@ -0,0 +1,104 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-22T10:30:20.743915Z",
+    "biotoolsCURIE": "biotools:manyfold",
+    "biotoolsID": "manyfold",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "t.barrett@instadeep.com",
+            "name": "Thomas D Barrett",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Arthur Flajolet"
+        },
+        {
+            "name": "Louis Robinson"
+        },
+        {
+            "name": "Amelia Villegas-Morcillo",
+            "orcidid": "https://orcid.org/0000-0002-3286-049X"
+        }
+    ],
+    "description": "An efficient and flexible library for training and validating protein folding models.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein folding analysis",
+                    "uri": "http://edamontology.org/operation_2415"
+                },
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                },
+                {
+                    "term": "Protein structure validation",
+                    "uri": "http://edamontology.org/operation_0321"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/instadeepai/manyfold",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-22T10:30:20.746362Z",
+    "license": "CC-BY-NC-SA-4.0",
+    "name": "ManyFold",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC773",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: ManyFold is a flexible library for protein structure prediction with deep learning that (i) supports models that use both multiple sequence alignments (MSAs) and protein language model (pLM) embedding as inputs, (ii) allows inference of existing models (AlphaFold and OpenFold), (iii) is fully trainable, allowing for both fine-tuning and the training of new models from scratch and (iv) is written in Jax to support efficient batched operation in distributed settings. A proof-of-concept pLM-based model, pLMFold, is trained from scratch to obtain reasonable results with reduced computational overheads in comparison to AlphaFold. AVAILABILITY AND IMPLEMENTATION: The source code for ManyFold, the validation dataset and a small sample of training data are available at https://github.com/instadeepai/manyfold. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Barrett T.D."
+                    },
+                    {
+                        "name": "Flajolet A."
+                    },
+                    {
+                        "name": "Robinson L."
+                    },
+                    {
+                        "name": "Villegas-Morcillo A."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "ManyFold: an efficient and flexible library for training and validating protein folding models"
+            },
+            "pmcid": "PMC9825755",
+            "pmid": "36495196"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Protein folding, stability and design",
+            "uri": "http://edamontology.org/topic_0130"
+        },
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        }
+    ]
+}
diff --git a/data/matlab/matlab.biotools.json b/data/matlab/matlab.biotools.json
index 6ad432b09b018..dd61212f7e128 100644
--- a/data/matlab/matlab.biotools.json
+++ b/data/matlab/matlab.biotools.json
@@ -2,6 +2,9 @@
     "additionDate": "2020-05-18T19:09:30Z",
     "biotoolsCURIE": "biotools:matlab",
     "biotoolsID": "matlab",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
     "cost": "Commercial",
     "description": "MATLAB is a general use development environment and scientific computing language.",
     "documentation": [
@@ -13,10 +16,13 @@
         }
     ],
     "editPermission": {
-        "type": "private"
+        "authors": [
+            "iacs-biocomputacion"
+        ],
+        "type": "group"
     },
     "homepage": "https://www.mathworks.com/products/matlab.html",
-    "lastUpdate": "2020-05-18T19:12:59Z",
+    "lastUpdate": "2023-02-01T12:58:22.832548Z",
     "maturity": "Mature",
     "name": "MATLAB",
     "operatingSystem": [
diff --git a/data/matplotlib/matplotlib.biotools.json b/data/matplotlib/matplotlib.biotools.json
new file mode 100644
index 0000000000000..387948a6fffb9
--- /dev/null
+++ b/data/matplotlib/matplotlib.biotools.json
@@ -0,0 +1,62 @@
+{
+    "additionDate": "2023-01-27T12:38:00.914570Z",
+    "biotoolsCURIE": "biotools:matplotlib",
+    "biotoolsID": "matplotlib",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "John Hunter",
+            "url": "https://matplotlib.org/stable/users/project/citing.html"
+        }
+    ],
+    "description": "Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.",
+    "documentation": [
+        {
+            "type": [
+                "Installation instructions"
+            ],
+            "url": "https://matplotlib.org/stable/index.html"
+        }
+    ],
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://matplotlib.org/stable/users/getting_started/"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://matplotlib.org/",
+    "lastUpdate": "2023-02-01T12:36:54.881910Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/ucsd-ccbb/visJS2jupyter"
+        }
+    ],
+    "name": "Matplotlib",
+    "owner": "iacs-biocomputacion",
+    "version": [
+        "3.6.0 Released"
+    ]
+}
diff --git a/data/matrisomedb/matrisomedb.biotools.json b/data/matrisomedb/matrisomedb.biotools.json
index 84c6f43007a9a..949fb7dc02ba7 100644
--- a/data/matrisomedb/matrisomedb.biotools.json
+++ b/data/matrisomedb/matrisomedb.biotools.json
@@ -3,7 +3,18 @@
     "biotoolsCURIE": "biotools:MatrisomeDB",
     "biotoolsID": "MatrisomeDB",
     "confidence_flag": "tool",
-    "description": "The ECM-protein knowledge database.\n\nPlease follow MatrisomeDB. MatrisomeDB will be hosted at matrisomedb.org very soon.",
+    "credit": [
+        {
+            "email": "yugao@uic.edu",
+            "name": "Yu (Tom) Gao"
+        },
+        {
+            "email": "anaba@uic.edu",
+            "name": "Alexandra Naba",
+            "orcidid": "https://orcid.org/0000-0002-4796-5614"
+        }
+    ],
+    "description": "The ECM-protein knowledge database.",
     "editPermission": {
         "type": "public"
     },
@@ -25,13 +36,38 @@
                 {
                     "term": "PTM localisation",
                     "uri": "http://edamontology.org/operation_3755"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
                 }
             ]
         }
     ],
-    "homepage": "http://www.pepchem.org/matrisomedb",
-    "lastUpdate": "2020-12-23T07:53:38Z",
+    "homepage": "https://matrisomedb.org",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-08T15:21:00.243192Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/blackjack-uic/MatrisomeDB2"
+        }
+    ],
     "name": "MatrisomeDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "Pub2Tools",
     "publication": [
         {
@@ -55,7 +91,7 @@
                         "name": "Taha I.N."
                     }
                 ],
-                "citationCount": 26,
+                "citationCount": 72,
                 "date": "2020-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "MatrisomeDB: The ECM-protein knowledge database"
@@ -64,9 +100,14 @@
         }
     ],
     "toolType": [
-        "Database portal"
+        "Database portal",
+        "Web application"
     ],
     "topic": [
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
         {
             "term": "Oncology",
             "uri": "http://edamontology.org/topic_2640"
diff --git a/data/mddi-scl/mddi-scl.biotools.json b/data/mddi-scl/mddi-scl.biotools.json
new file mode 100644
index 0000000000000..243c32176798b
--- /dev/null
+++ b/data/mddi-scl/mddi-scl.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T10:28:54.200956Z",
+    "biotoolsCURIE": "biotools:mddi-scl",
+    "biotoolsID": "mddi-scl",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dqwei@sjtu.edu.cn",
+            "name": "Dong-Qing Wei",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xiongyi@sjtu.edu.cn",
+            "name": "Yi Xiong",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Shenggeng Lin"
+        },
+        {
+            "name": "Weizhi Chen"
+        }
+    ],
+    "description": "Predicting multi-type drug-drug interactions via supervised contrastive learning.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ShenggengLin/MDDI-SCL",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-08T10:28:54.203616Z",
+    "license": "MIT",
+    "name": "MDDI-SCL",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13321-022-00659-8",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).The joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and result in adverse consequence to the patients. Accurate identification of DDI types can not only provide hints to avoid these accidental events, but also elaborate the underlying mechanisms by how DDIs occur. Several computational methods have been proposed for multi-type DDI prediction, but room remains for improvement in prediction performance. In this study, we propose a supervised contrastive learning based method, MDDI-SCL, implemented by three-level loss functions, to predict multi-type DDIs. MDDI-SCL is mainly composed of three modules: drug feature encoder and mean squared error loss module, drug latent feature fusion and supervised contrastive loss module, multi-type DDI prediction and classification loss module. The drug feature encoder and mean squared error loss module uses self-attention mechanism and autoencoder to learn drug-level latent features. The drug latent feature fusion and supervised contrastive loss module uses multi-scale feature fusion to learn drug pair-level latent features. The prediction and classification loss module predicts DDI types of each drug pair. We evaluate MDDI-SCL on three different tasks of two datasets. Experimental results demonstrate that MDDI-SCL achieves better or comparable performance as the state-of-the-art methods. Furthermore, the effectiveness of supervised contrastive learning is validated by ablation experiment, and the feasibility of MDDI-SCL is supported by case studies. The source codes are available at https://github.com/ShenggengLin/MDDI-SCL.",
+                "authors": [
+                    {
+                        "name": "Chen G."
+                    },
+                    {
+                        "name": "Chen W."
+                    },
+                    {
+                        "name": "Lin S."
+                    },
+                    {
+                        "name": "Wei D.-Q."
+                    },
+                    {
+                        "name": "Xiong Y."
+                    },
+                    {
+                        "name": "Zhou S."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Journal of Cheminformatics",
+                "title": "MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning"
+            },
+            "pmcid": "PMC9667597",
+            "pmid": "36380384"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Biotherapeutics",
+            "uri": "http://edamontology.org/topic_3374"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Pharmacology",
+            "uri": "http://edamontology.org/topic_0202"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/mecaf/mecaf.biotools.json b/data/mecaf/mecaf.biotools.json
new file mode 100644
index 0000000000000..4e8c3edfb86d4
--- /dev/null
+++ b/data/mecaf/mecaf.biotools.json
@@ -0,0 +1,109 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T10:20:54.309300Z",
+    "biotoolsCURIE": "biotools:mecaf",
+    "biotoolsID": "mecaf",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Jiyuan.Hu@nyulangone.org",
+            "name": "Jiyuan Hu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Hongping Guo"
+        },
+        {
+            "name": "TingFang Lee"
+        },
+        {
+            "name": "Xiaochen Yu"
+        },
+        {
+            "name": "Zhengbang Li"
+        }
+    ],
+    "description": "A maximum-type microbial differential abundance test with application to high-dimensional microbiome data analyses.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Statistical calculation",
+                    "uri": "http://edamontology.org/operation_2238"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Jiyuan-NYU-Langone/MECAF",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-08T10:20:54.311844Z",
+    "license": "GPL-3.0",
+    "name": "MECAF",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FCIMB.2022.988717",
+            "metadata": {
+                "abstract": "Copyright © 2022 Li, Yu, Guo, Lee and Hu.Background: High-throughput metagenomic sequencing technologies have shown prominent advantages over traditional pathogen detection methods, bringing great potential in clinical pathogen diagnosis and treatment of infectious diseases. Nevertheless, how to accurately detect the difference in microbiome profiles between treatment or disease conditions remains computationally challenging. Results: In this study, we propose a novel test for identifying the difference between two high-dimensional microbiome abundance data matrices based on the centered log-ratio transformation of the microbiome compositions. The test p-value can be calculated directly with a closed-form solution from the derived asymptotic null distribution. We also investigate the asymptotic statistical power against sparse alternatives that are typically encountered in microbiome studies. The proposed test is maximum-type equal-covariance-assumption-free (MECAF), making it widely applicable to studies that compare microbiome compositions between conditions. Our simulation studies demonstrated that the proposed MECAF test achieves more desirable power than competing methods while having the type I error rate well controlled under various scenarios. The usefulness of the proposed test is further illustrated with two real microbiome data analyses. The source code of the proposed method is freely available at https://github.com/Jiyuan-NYU-Langone/MECAF. Conclusions: MECAF is a flexible differential abundance test and achieves statistical efficiency in analyzing high-throughput microbiome data. The proposed new method will allow us to efficiently discover shifts in microbiome abundances between disease and treatment conditions, broadening our understanding of the disease and ultimately improving clinical diagnosis and treatment.",
+                "authors": [
+                    {
+                        "name": "Guo H."
+                    },
+                    {
+                        "name": "Hu J."
+                    },
+                    {
+                        "name": "Lee T."
+                    },
+                    {
+                        "name": "Li Z."
+                    },
+                    {
+                        "name": "Yu X."
+                    }
+                ],
+                "date": "2022-10-28T00:00:00Z",
+                "journal": "Frontiers in Cellular and Infection Microbiology",
+                "title": "A maximum-type microbial differential abundance test with application to high-dimensional microbiome data analyses"
+            },
+            "pmcid": "PMC9650337",
+            "pmid": "36389165"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/megabayesc/megabayesc.biotools.json b/data/megabayesc/megabayesc.biotools.json
new file mode 100644
index 0000000000000..53471741453ab
--- /dev/null
+++ b/data/megabayesc/megabayesc.biotools.json
@@ -0,0 +1,94 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-22T10:16:22.884581Z",
+    "biotoolsCURIE": "biotools:megabayesc",
+    "biotoolsID": "megabayesc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "deruncie@ucdavis.edu",
+            "name": "Daniel Runcie",
+            "orcidid": "https://orcid.org/0000-0002-3008-9312",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "qtlcheng@ucdavis.edu",
+            "name": "Hao Cheng",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jiayi Qu"
+        }
+    ],
+    "description": "Mega-scale Bayesian Regression methods for genome-wide prediction and association studies with thousands of traits.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Collapsing methods",
+                    "uri": "http://edamontology.org/operation_3791"
+                },
+                {
+                    "term": "Gene expression QTL analysis",
+                    "uri": "http://edamontology.org/operation_3232"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Jiayi-Qu/Mega-BayesC",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-22T10:16:22.887140Z",
+    "license": "Not licensed",
+    "name": "MegaBayesC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/GENETICS/IYAC183",
+            "pmid": "36529897"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/membrain_pipeline/membrain_pipeline.biotools.json b/data/membrain_pipeline/membrain_pipeline.biotools.json
new file mode 100644
index 0000000000000..7c25cd797a2c8
--- /dev/null
+++ b/data/membrain_pipeline/membrain_pipeline.biotools.json
@@ -0,0 +1,122 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T21:35:03.050563Z",
+    "biotoolsCURIE": "biotools:membrain_pipeline",
+    "biotoolsID": "membrain_pipeline",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ben.engel@unibas.ch",
+            "name": "Benjamin D. Engel",
+            "orcidid": "http://orcid.org/0000-0002-0941-4387",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "tingying.peng@helmholtz-muenchen.de",
+            "name": "Tingying Peng",
+            "orcidid": "http://orcid.org/0000-0002-7881-1749",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Lorenz Lamm",
+            "orcidid": "http://orcid.org/0000-0003-0698-7769"
+        },
+        {
+            "name": "Ricardo D. Righetto",
+            "orcidid": "http://orcid.org/0000-0003-4247-4303"
+        }
+    ],
+    "description": "A Deep Learning-aided Pipeline for Automated Detection of Membrane Proteins in Cryo-electron Tomograms.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Single particle alignment and classification",
+                    "uri": "http://edamontology.org/operation_3458"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/CellArchLab/MemBrain",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-17T21:35:03.053149Z",
+    "license": "MPL-2.0",
+    "name": "MemBrain",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.cmpb.2022.106990",
+            "metadata": {
+                "abstract": "© 2022Background and Objective: Cryo-electron tomography (cryo-ET) is an imaging technique that enables 3D visualization of the native cellular environment at sub-nanometer resolution, providing unpreceded insights into the molecular organization of cells. However, cryo-electron tomograms suffer from low signal-to-noise ratios and anisotropic resolution, which makes subsequent image analysis challenging. In particular, the efficient detection of membrane-embedded proteins is a problem still lacking satisfactory solutions. Methods: We present MemBrain – a new deep learning-aided pipeline that automatically detects membrane-bound protein complexes in cryo-electron tomograms. After subvolumes are sampled along a segmented membrane, each subvolume is assigned a score using a convolutional neural network (CNN), and protein positions are extracted by a clustering algorithm. Incorporating rotational subvolume normalization and using a tiny receptive field simplify the task of protein detection and thus facilitate the network training. Results: MemBrain requires only a small quantity of training labels and achieves excellent performance with only a single annotated membrane (F1 score: 0.88). A detailed evaluation shows that our fully trained pipeline outperforms existing classical computer vision-based and CNN-based approaches by a large margin (F1 score: 0.92 vs. max. 0.63). Furthermore, in addition to protein center positions, MemBrain can determine protein orientations, which has not been implemented by any existing CNN-based method to date. We also show that a pre-trained MemBrain program generalizes to tomograms acquired using different cryo-ET methods and depicting different types of cells. Conclusions: MemBrain is a powerful and annotation-efficient tool for the detection of membrane protein complexes in cryo-ET data, with the potential to be used in a wide range of biological studies. It is generalizable to various kinds of tomograms, making it possible to use pretrained models for different tasks. Its efficiency in terms of required annotations also allows rapid training and fine-tuning of models. The corresponding code, pretrained models, and instructions for operating the MemBrain program can be found at: https://github.com/CellArchLab/MemBrain.",
+                "authors": [
+                    {
+                        "name": "Engel B.D."
+                    },
+                    {
+                        "name": "Lamm L."
+                    },
+                    {
+                        "name": "Martinez-Sanchez A."
+                    },
+                    {
+                        "name": "Peng T."
+                    },
+                    {
+                        "name": "Poge M."
+                    },
+                    {
+                        "name": "Righetto R.D."
+                    },
+                    {
+                        "name": "Wietrzynski W."
+                    }
+                ],
+                "citationCount": 3,
+                "date": "2022-09-01T00:00:00Z",
+                "journal": "Computer Methods and Programs in Biomedicine",
+                "title": "MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms"
+            },
+            "pmid": "35858496"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Membrane and lipoproteins",
+            "uri": "http://edamontology.org/topic_0820"
+        },
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        },
+        {
+            "term": "Tomography",
+            "uri": "http://edamontology.org/topic_3452"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/membranefold/membranefold.biotools.json b/data/membranefold/membranefold.biotools.json
new file mode 100644
index 0000000000000..2d7f3c5ee47f5
--- /dev/null
+++ b/data/membranefold/membranefold.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-22T10:09:27.431594Z",
+    "biotoolsCURIE": "biotools:membranefold",
+    "biotoolsID": "membranefold",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Felix Teufel"
+        },
+        {
+            "name": "Santiago Gutierrez"
+        },
+        {
+            "name": "Wojciech G. Tyczynski"
+        },
+        {
+            "name": "Wouter Boomsma"
+        },
+        {
+            "name": "Ole Winther",
+            "orcidid": "http://orcid.org/0000-0002-1966-3205"
+        }
+    ],
+    "description": "Visualising transmembrane protein structure and topology.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence",
+                        "uri": "http://edamontology.org/data_2044"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        },
+                        {
+                            "term": "PDB",
+                            "uri": "http://edamontology.org/format_1476"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Protein structure prediction",
+                    "uri": "http://edamontology.org/operation_0474"
+                },
+                {
+                    "term": "Transmembrane protein prediction",
+                    "uri": "http://edamontology.org/operation_0269"
+                },
+                {
+                    "term": "Transmembrane protein visualisation",
+                    "uri": "http://edamontology.org/operation_2241"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ku.biolib.com/MembraneFold/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-22T10:09:27.434348Z",
+    "license": "Other",
+    "name": "MembraneFold",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1101/2022.12.06.518085"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biology",
+            "uri": "http://edamontology.org/topic_3070"
+        },
+        {
+            "term": "Membrane and lipoproteins",
+            "uri": "http://edamontology.org/topic_0820"
+        },
+        {
+            "term": "Protein structure analysis",
+            "uri": "http://edamontology.org/topic_2814"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        }
+    ]
+}
diff --git a/data/memtrax/memtrax.biotools.json b/data/memtrax/memtrax.biotools.json
new file mode 100644
index 0000000000000..c01aac46fa789
--- /dev/null
+++ b/data/memtrax/memtrax.biotools.json
@@ -0,0 +1,109 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T10:08:07.953643Z",
+    "biotoolsCURIE": "biotools:memtrax",
+    "biotoolsID": "memtrax",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ashford@stanford.edu",
+            "name": "J. Wesson Ashford",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Curtis B. Ashford"
+        },
+        {
+            "name": "James O. Clifford"
+        },
+        {
+            "name": "Peter J. Bayley"
+        }
+    ],
+    "description": "Correctness and response time distributions in the MemTrax continuous recognition task.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Anonymisation",
+                    "uri": "http://edamontology.org/operation_3283"
+                },
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Format detection",
+                    "uri": "http://edamontology.org/operation_3357"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://memtrax.com",
+    "lastUpdate": "2023-02-08T10:08:07.956057Z",
+    "name": "MemTrax",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FNAGI.2022.1005298",
+            "metadata": {
+                "abstract": "Copyright © 2022 Ashford, Clifford, Anand, Bergeron, Ashford and Bayley.A critical issue in addressing medical conditions is measurement. Memory measurement is difficult, especially episodic memory, which is disrupted by many conditions. On-line computer testing can precisely measure and assess several memory functions. This study analyzed memory performances from a large group of anonymous, on-line participants using a continuous recognition task (CRT) implemented at https://memtrax.com. These analyses estimated ranges of acceptable performance and average response time (RT). For 344,165 presumed unique individuals completing the CRT a total of 602,272 times, data were stored on a server, including each correct response (HIT), Correct Rejection, and RT to the thousandth of a second. Responses were analyzed, distributions and relationships of these parameters were ascertained, and mean RTs were determined for each participant across the population. From 322,996 valid first tests, analysis of correctness showed that 63% of these tests achieved at least 45 correct (90%), 92% scored at or above 40 correct (80%), and 3% scored 35 correct (70%) or less. The distribution of RTs was skewed with 1% faster than 0.62 s, a median at 0.890 s, and 1% slower than 1.57 s. The RT distribution was best explained by a novel model, the reverse-exponential (RevEx) function. Increased RT speed was most closely associated with increased HIT accuracy. The MemTrax on-line memory test readily provides valid and reliable metrics for assessing individual episodic memory function that could have practical clinical utility for precise assessment of memory dysfunction in many conditions, including improvement or deterioration over time.",
+                "authors": [
+                    {
+                        "name": "Anand S."
+                    },
+                    {
+                        "name": "Ashford C.B."
+                    },
+                    {
+                        "name": "Ashford J.W."
+                    },
+                    {
+                        "name": "Bayley P.J."
+                    },
+                    {
+                        "name": "Bergeron M.F."
+                    },
+                    {
+                        "name": "Clifford J.O."
+                    }
+                ],
+                "date": "2022-11-03T00:00:00Z",
+                "journal": "Frontiers in Aging Neuroscience",
+                "title": "Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model"
+            },
+            "pmcid": "PMC9682919",
+            "pmid": "36437986"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        }
+    ]
+}
diff --git a/data/meta-boa/meta-boa.biotools.json b/data/meta-boa/meta-boa.biotools.json
new file mode 100644
index 0000000000000..ccea04a686b5a
--- /dev/null
+++ b/data/meta-boa/meta-boa.biotools.json
@@ -0,0 +1,110 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-23T09:14:36.383238Z",
+    "biotoolsCURIE": "biotools:meta-boa",
+    "biotoolsID": "meta-boa",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "cuperlovim@nrc.ca",
+            "name": "Miroslava Čuperlović-Culf",
+            "orcidid": "http://orcid.org/0000-0002-9483-8159",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Emily Hashimoto-Roth"
+        },
+        {
+            "name": "Mathieu Lavallée-Adam"
+        },
+        {
+            "name": "Anuradha Surendra",
+            "orcidid": "http://orcid.org/0000-0002-4736-3592"
+        },
+        {
+            "name": "Steffany A. L. Bennett",
+            "orcidid": "http://orcid.org/0000-0001-7944-5800"
+        }
+    ],
+    "description": "META-BOA (METAbolomics data Balancing with Over-sampling Algorithms) is a software solution for handling sample imbalance primarily for metabolomics and lipidomics datasets.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://complimet.ca/meta-boa",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-23T09:14:36.386097Z",
+    "name": "META-BOA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac649",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.MOTIVATION: Class imbalance, or unequal sample sizes between classes, is an increasing concern in machine learning for metabolomic and lipidomic data mining, which can result in overfitting for the over-represented class. Numerous methods have been developed for handling class imbalance, but they are not readily accessible to users with limited computational experience. Moreover, there is no resource that enables users to easily evaluate the effect of different over-sampling algorithms. RESULTS: METAbolomics data Balancing with Over-sampling Algorithms (META-BOA) is a web-based application that enables users to select between four different methods for class balancing, followed by data visualization and classification of the sample to observe the augmentation effects. META-BOA outputs a newly balanced dataset, generating additional samples in the minority class, according to the user's choice of Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE (BSMOTE), Adaptive Synthetic (ADASYN) or Random Over-Sampling Examples (ROSE). To present the effect of over-sampling on the data META-BOA further displays both principal component analysis and t-distributed stochastic neighbor embedding visualization of data pre- and post-over-sampling. Random forest classification is utilized to compare sample classification in both the original and balanced datasets, enabling users to select the most appropriate method for their further analyses. AVAILABILITY AND IMPLEMENTATION: META-BOA is available at https://complimet.ca/meta-boa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Bennett S.A.L."
+                    },
+                    {
+                        "name": "Cuperlovic-Culf M."
+                    },
+                    {
+                        "name": "Hashimoto-Roth E."
+                    },
+                    {
+                        "name": "Lavallee-Adam M."
+                    },
+                    {
+                        "name": "Surendra A."
+                    }
+                ],
+                "date": "2022-11-30T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "METAbolomics data Balancing with Over-sampling Algorithms (META-BOA): an online resource for addressing class imbalance"
+            },
+            "pmid": "36222566"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Lipids",
+            "uri": "http://edamontology.org/topic_0153"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        }
+    ]
+}
diff --git a/data/meta-disc/meta-disc.biotools.json b/data/meta-disc/meta-disc.biotools.json
new file mode 100644
index 0000000000000..6a36673fcddb2
--- /dev/null
+++ b/data/meta-disc/meta-disc.biotools.json
@@ -0,0 +1,122 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T09:53:43.824324Z",
+    "biotoolsCURIE": "biotools:meta-disc",
+    "biotoolsID": "meta-disc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "nieves.plana@salud.madrid.org",
+            "name": "Maria N. Plana",
+            "orcidid": "https://orcid.org/0000-0003-0921-7954",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ingrid Arevalo-Rodriguez"
+        },
+        {
+            "name": "Javier Zamora"
+        },
+        {
+            "name": "Marta Roqué"
+        }
+    ],
+    "description": "A web application for meta-analysis of diagnostic test accuracy data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Statistical modelling",
+                    "uri": "http://edamontology.org/operation_3664"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.metadisc.es",
+    "language": [
+        "R",
+        "SAS"
+    ],
+    "lastUpdate": "2023-02-08T09:53:43.826901Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://metadisc.sourceforge.io"
+        }
+    ],
+    "name": "Meta-DiSc",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12874-022-01788-2",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Diagnostic evidence of the accuracy of a test for identifying a target condition of interest can be estimated using systematic approaches following standardized methodologies. Statistical methods for the meta-analysis of diagnostic test accuracy (DTA) studies are relatively complex, presenting a challenge for reviewers without extensive statistical expertise. In 2006, we developed Meta-DiSc, a free user-friendly software to perform test accuracy meta-analysis. This statistical program is now widely used for performing DTA meta-analyses. We aimed to build a new version of the Meta-DiSc software to include statistical methods based on hierarchical models and an enhanced web-based interface to improve user experience. Results: In this article, we present the updated version, Meta-DiSc 2.0, a web-based application developed using the R Shiny package. This new version implements recommended state-of-the-art statistical models to overcome the limitations of the statistical approaches included in the previous version. Meta-DiSc 2.0 performs statistical analyses of DTA reviews using a bivariate random effects model. The application offers a thorough analysis of heterogeneity, calculating logit variance estimates of sensitivity and specificity, the bivariate I-squared, the area of the 95% prediction ellipse, and the median odds ratios for sensitivity and specificity, and facilitating subgroup and meta-regression analyses. Furthermore, univariate random effects models can be applied to meta-analyses with few studies or with non-convergent bivariate models. The application interface has an intuitive design set out in four main menus: file upload; graphical description (forest and ROC plane plots); meta-analysis (pooling of sensitivity and specificity, estimation of likelihood ratios and diagnostic odds ratio, sROC curve); and summary of findings (impact of test through downstream consequences in a hypothetical population with a given prevalence). All computational algorithms have been validated in several real datasets by comparing results obtained with STATA/SAS and MetaDTA packages. Conclusion: We have developed and validated an updated version of the Meta-DiSc software that is more accessible and statistically sound. The web application is freely available at www.metadisc.es.",
+                "authors": [
+                    {
+                        "name": "Arevalo-Rodriguez I."
+                    },
+                    {
+                        "name": "Fabregate M."
+                    },
+                    {
+                        "name": "Fernandez-Garcia S."
+                    },
+                    {
+                        "name": "Perez T."
+                    },
+                    {
+                        "name": "Plana M.N."
+                    },
+                    {
+                        "name": "Roque M."
+                    },
+                    {
+                        "name": "Soto J."
+                    },
+                    {
+                        "name": "Zamora J."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Medical Research Methodology",
+                "title": "Meta-DiSc 2.0: a web application for meta-analysis of diagnostic test accuracy data"
+            },
+            "pmcid": "PMC9707040",
+            "pmid": "36443653"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Allergy, clinical immunology and immunotherapeutics",
+            "uri": "http://edamontology.org/topic_3400"
+        },
+        {
+            "term": "Experimental design and studies",
+            "uri": "http://edamontology.org/topic_3678"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/metaanalyst/metaanalyst.biotools.json b/data/metaanalyst/metaanalyst.biotools.json
new file mode 100644
index 0000000000000..ccf64455f83c7
--- /dev/null
+++ b/data/metaanalyst/metaanalyst.biotools.json
@@ -0,0 +1,120 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-22T09:57:07.201649Z",
+    "biotoolsCURIE": "biotools:metaanalyst",
+    "biotoolsID": "metaanalyst",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mustafa.shawaqfeh@gju.edu.jo",
+            "name": "Mustafa Alshawaqfeh",
+            "orcidid": "https://orcid.org/0000-0003-2170-6830",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Abdullah Hayajneh"
+        },
+        {
+            "name": "Ammar Gharaibeh"
+        },
+        {
+            "name": "Erchin Serpedin"
+        },
+        {
+            "name": "Salahelden Rababah"
+        }
+    ],
+    "description": "A user-friendly tool for metagenomic biomarker detection and phenotype classification.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/mshawaqfeh/MetaAnalyst/blob/main/User%20Manual.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Formatting",
+                    "uri": "http://edamontology.org/operation_0335"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/mshawaqfeh/MetaAnalyst",
+    "lastUpdate": "2023-02-22T09:57:07.204218Z",
+    "name": "MetaAnalyst",
+    "operatingSystem": [
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12874-022-01812-5",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Many metagenomic studies have linked the imbalance in microbial abundance profiles to a wide range of diseases. These studies suggest utilizing the microbial abundance profiles as potential markers for metagenomic-associated conditions. Due to the inevitable importance of biomarkers in understanding the disease progression and the development of possible therapies, various computational tools have been proposed for metagenomic biomarker detection. However, most existing tools require prior scripting knowledge and lack user friendly interfaces, causing considerable time and effort to install, configure, and run these tools. Besides, there is no available all-in-one solution for running and comparing various metagenomic biomarker detection simultaneously. In addition, most of these tools just present the suggested biomarkers without any statistical evaluation for their quality. Results: To overcome these limitations, this work presents MetaAnalyst, a software package with a simple graphical user interface (GUI) that (i) automates the installation and configuration of 28 state-of-the-art tools, (ii) supports flexible study design to enable studying the dataset under different scenarios smoothly, iii) runs and evaluates several algorithms simultaneously iv) supports different input formats and provides the user with several preprocessing capabilities, v) provides a variety of metrics to evaluate the quality of the suggested markers, and vi) presents the outcomes in the form of publication quality plots with various formatting capabilities as well as Excel sheets. Conclusions: The utility of this tool has been verified through studying a metagenomic dataset under four scenarios. The executable file for MetaAnalyst along with its user manual are made available at https://github.com/mshawaqfeh/MetaAnalyst.",
+                "authors": [
+                    {
+                        "name": "Alshawaqfeh M."
+                    },
+                    {
+                        "name": "Gharaibeh A."
+                    },
+                    {
+                        "name": "Hayajneh A."
+                    },
+                    {
+                        "name": "Rababah S."
+                    },
+                    {
+                        "name": "Serpedin E."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Medical Research Methodology",
+                "title": "MetaAnalyst: a user-friendly tool for metagenomic biomarker detection and phenotype classification"
+            },
+            "pmcid": "PMC9795700",
+            "pmid": "36577938"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Cardiology",
+            "uri": "http://edamontology.org/topic_3335"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/metaboanalyst/metaboanalyst.biotools.json b/data/metaboanalyst/metaboanalyst.biotools.json
index 1f1f1b86303a4..ecbbc88b6350c 100644
--- a/data/metaboanalyst/metaboanalyst.biotools.json
+++ b/data/metaboanalyst/metaboanalyst.biotools.json
@@ -80,9 +80,9 @@
         "Java",
         "R"
     ],
-    "lastUpdate": "2018-12-10T12:58:59Z",
+    "lastUpdate": "2023-02-26T09:49:28.946232Z",
     "license": "GPL-3.0",
-    "name": "MetaboAnalyst 4.0",
+    "name": "MetaboAnalyst",
     "owner": "aotamendi.1",
     "publication": [
         {
@@ -115,11 +115,45 @@
                         "name": "Xia J."
                     }
                 ],
-                "citationCount": 1681,
+                "citationCount": 2382,
                 "date": "2018-07-02T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "MetaboAnalyst 4.0: Towards more transparent and integrative metabolomics analysis"
             }
+        },
+        {
+            "doi": "10.1038/s41596-022-00710-w",
+            "metadata": {
+                "abstract": "© 2022, Springer Nature Limited.Liquid chromatography coupled with high-resolution mass spectrometry (LC–HRMS) has become a workhorse in global metabolomics studies with growing applications across biomedical and environmental sciences. However, outstanding bioinformatics challenges in terms of data processing, statistical analysis and functional interpretation remain critical barriers to the wider adoption of this technology. To help the user community overcome these barriers, we have made major updates to the well-established MetaboAnalyst platform (www.metaboanalyst.ca). This protocol extends the previous 2011 Nature Protocol by providing stepwise instructions on how to use MetaboAnalyst 5.0 to: optimize parameters for LC–HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata. Parameter optimization may take ~2 h to complete depending on the server load, and the remaining three stages may be executed in ~60 min.",
+                "authors": [
+                    {
+                        "name": "Basu N."
+                    },
+                    {
+                        "name": "Chang L."
+                    },
+                    {
+                        "name": "Ewald J."
+                    },
+                    {
+                        "name": "Hacariz O."
+                    },
+                    {
+                        "name": "Pang Z."
+                    },
+                    {
+                        "name": "Xia J."
+                    },
+                    {
+                        "name": "Zhou G."
+                    }
+                ],
+                "citationCount": 73,
+                "date": "2022-08-01T00:00:00Z",
+                "journal": "Nature Protocols",
+                "title": "Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data"
+            },
+            "pmid": "35715522"
         }
     ],
     "toolType": [
@@ -141,6 +175,7 @@
     ],
     "validated": 1,
     "version": [
-        "4.0"
+        "4.0",
+        "5.0"
     ]
 }
diff --git a/data/metabolicatlas/metabolicatlas.biotools.json b/data/metabolicatlas/metabolicatlas.biotools.json
index 10140ec0c7b3e..fabf5cc7511e8 100644
--- a/data/metabolicatlas/metabolicatlas.biotools.json
+++ b/data/metabolicatlas/metabolicatlas.biotools.json
@@ -4,10 +4,15 @@
     "biotoolsID": "metabolicatlas",
     "credit": [
         {
-            "email": "contact@metabolicatlas.org"
+            "email": "contact@metabolicatlas.org",
+            "name": "Mihail Anton",
+            "orcidid": "https://orcid.org/0000-0002-7753-9042",
+            "typeRole": [
+                "Primary contact"
+            ]
         }
     ],
-    "description": "Metabolic Atlas integrates open source genome-scale metabolic models (GEMs) of human and yeast for easy browsing and analysis. It also contains many more GEMs constructed by our organization. Detailed biochemical information is provided for individual model components, such as reactions, metabolites, and genes. These components are also associated with standard identifiers, facilitating integration with external databases, such as the Human Protein Atlas.",
+    "description": "Metabolic Atlas is a web platform integrating open-source genome scale metabolic models (GEMs) for easy browsing and analysis. The goal is to collect curated GEMs, and to bring these models closer to FAIR principles. The website provides visualisations and comparisons of the GEMs, and links to resources, algorithms, other databases, and more general software applications. Metabolic Atlas is intended to be used for applications in metabolomics, clinical chemistry, biomarker discovery and general education. In short, the vision is to create a one-stop-shop for everything metabolism related.",
     "documentation": [
         {
             "type": [
@@ -22,9 +27,26 @@
             "url": "https://metabolicatlas.org/documentation"
         }
     ],
+    "download": [
+        {
+            "type": "API specification",
+            "url": "https://metabolicatlas.org/api/v2/"
+        },
+        {
+            "type": "Icon",
+            "url": "https://github.com/MetabolicAtlas/MetabolicAtlas/blob/main/frontend/public/img/logo.png"
+        },
+        {
+            "type": "Source code",
+            "url": "https://github.com/MetabolicAtlas/MetabolicAtlas"
+        }
+    ],
     "editPermission": {
         "type": "private"
     },
+    "elixirNode": [
+        "Sweden"
+    ],
     "function": [
         {
             "operation": [
@@ -44,9 +66,9 @@
         }
     ],
     "homepage": "https://metabolicatlas.org",
-    "lastUpdate": "2020-05-13T07:58:28Z",
+    "lastUpdate": "2023-03-14T15:06:36.177583Z",
     "license": "GPL-3.0",
-    "maturity": "Emerging",
+    "maturity": "Mature",
     "name": "Metabolic Atlas",
     "operatingSystem": [
         "Linux",
@@ -56,16 +78,89 @@
     "otherID": [
         {
             "type": "doi",
-            "value": "doi:10.1126/scisignal.aaz1482",
-            "version": "1.6"
+            "value": "DOI:10.1093/nar/gkac831",
+            "version": "3.3"
         }
     ],
     "owner": "M",
     "publication": [
+        {
+            "doi": "10.1073/pnas.2102344118",
+            "metadata": {
+                "abstract": "Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer’s disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.",
+                "authors": [
+                    {
+                        "name": "Anton M."
+                    },
+                    {
+                        "name": "Cholley P.-E."
+                    },
+                    {
+                        "name": "Gobom J."
+                    },
+                    {
+                        "name": "Gustafsson J."
+                    },
+                    {
+                        "name": "Huang S."
+                    },
+                    {
+                        "name": "Kocabas P."
+                    },
+                    {
+                        "name": "Nielsen J."
+                    },
+                    {
+                        "name": "Robinson J.L."
+                    },
+                    {
+                        "name": "Svensson T."
+                    },
+                    {
+                        "name": "Uhlen M."
+                    },
+                    {
+                        "name": "Wang H."
+                    },
+                    {
+                        "name": "Zetterberg H."
+                    }
+                ],
+                "citationCount": 18,
+                "date": "2021-07-27T00:00:00Z",
+                "journal": "Proceedings of the National Academy of Sciences of the United States of America",
+                "title": "Genome-scale metabolic network reconstruction of model animals as a platform for translational research"
+            },
+            "pmid": "34282017",
+            "version": "2"
+        },
+        {
+            "doi": "10.1093/database/bav068",
+            "metadata": {
+                "abstract": "Human tissue-specific genome-scale metabolic models (GEMs) provide comprehensive understanding of human metabolism, which is of great value to the biomedical research community. To make this kind of data easily accessible to the public, we have designed and deployed the human metabolic atlas (HMA) website (http://www.metabolicatlas.org). This online resource provides comprehensive information about human metabolism, including the results of metabolic network analyses. We hope that it can also serve as an information exchange interface for human metabolism knowledge within the research community. The HMA consists of three major components: Repository, Hreed (Human REaction Entities Database) and Atlas. Repository is a collection of GEMs for specific human cell types and human-related microorganisms in SBML (System Biology Markup Language) format. The current release consists of several types of GEMs: A generic human GEM, 82 GEMs for normal cell types, 16 GEMs for different cancer cell types, 2 curated GEMs and 5 GEMs for human gut bacteria. Hreed contains detailed information about biochemical reactions. A web interface for Hreed facilitates an access to the Hreed reaction data, which can be easily retrieved by using specific keywords or names of related genes, proteins, compounds and cross-references. Atlas web interface can be used for visualization of the GEMs collection overlaid on KEGG metabolic pathway maps with a zoom/pan user interface. The HMA is a unique tool for studying human metabolism, ranging in scope from an individual cell, to a specific organ, to the overall human body. This resource is freely available under a Creative Commons Attribution-NonCommercial 4.0 International License.",
+                "authors": [
+                    {
+                        "name": "Nielsen J."
+                    },
+                    {
+                        "name": "Nookaew I."
+                    },
+                    {
+                        "name": "Pornputtapong N."
+                    }
+                ],
+                "citationCount": 58,
+                "date": "2015-01-01T00:00:00Z",
+                "journal": "Database",
+                "title": "Human metabolic atlas: An online resource for human metabolism"
+            },
+            "pmid": "26209309",
+            "version": "0"
+        },
         {
             "doi": "10.1126/scisignal.aaz1482",
             "metadata": {
-                "abstract": "Copyright © 2020 The Authors, some rights reserved.Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.",
+                "abstract": "Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.",
                 "authors": [
                     {
                         "name": "Anton M."
@@ -128,15 +223,41 @@
                         "name": "Wang H."
                     }
                 ],
-                "citationCount": 44,
+                "citationCount": 109,
                 "date": "2020-03-24T00:00:00Z",
                 "journal": "Science Signaling",
                 "title": "An atlas of human metabolism"
             },
-            "pmid": "32209698"
+            "pmid": "32209698",
+            "version": "1"
+        },
+        {
+            "doi": "10.1093/nar/gkac831",
+            "pmid": "36169223",
+            "version": "3"
         }
     ],
     "relation": [
+        {
+            "biotoolsID": "chebi",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "identifiers.org",
+            "type": "usedBy"
+        },
+        {
+            "biotoolsID": "identifiers.org",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "metanetx",
+            "type": "usedBy"
+        },
+        {
+            "biotoolsID": "metanetx",
+            "type": "uses"
+        },
         {
             "biotoolsID": "proteinatlas",
             "type": "usedBy"
@@ -154,5 +275,10 @@
             "term": "Systems biology",
             "uri": "http://edamontology.org/topic_2259"
         }
+    ],
+    "version": [
+        "1.0 - 1.7",
+        "2.0 - 2.6",
+        "3.0 - 3.3+"
     ]
 }
diff --git a/data/metadensity/metadensity.biotools.json b/data/metadensity/metadensity.biotools.json
new file mode 100644
index 0000000000000..9fbb3dddaa181
--- /dev/null
+++ b/data/metadensity/metadensity.biotools.json
@@ -0,0 +1,104 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T00:45:29.574318Z",
+    "biotoolsCURIE": "biotools:metadensity",
+    "biotoolsID": "metadensity",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "geneyeo@ucsd.edu",
+            "name": "Gene W Yeo",
+            "orcidid": "https://orcid.org/0000-0002-0799-6037",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Evan Boyle"
+        },
+        {
+            "name": "Hsuan-Lin Her",
+            "orcidid": "https://orcid.org/0000-0001-7691-3816"
+        }
+    ],
+    "description": "A background-aware python pipeline for summarizing CLIP signals on various transcriptomic sites.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://metadensity.readthedocs.io/en/latest/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "RNA binding site prediction",
+                    "uri": "http://edamontology.org/operation_3902"
+                },
+                {
+                    "term": "RNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3901"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/YeoLab/Metadensity",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-08T00:46:20.732441Z",
+    "license": "MIT",
+    "name": "Metadensity",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOADV/VBAC083",
+            "pmcid": "PMC9653213",
+            "pmid": "36388152"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3794"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/metagt/metagt.biotools.json b/data/metagt/metagt.biotools.json
new file mode 100644
index 0000000000000..dd209ea2efc43
--- /dev/null
+++ b/data/metagt/metagt.biotools.json
@@ -0,0 +1,126 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T00:36:32.232430Z",
+    "biotoolsCURIE": "biotools:metagt",
+    "biotoolsID": "metagt",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "andrewprzh@gmail.com",
+            "name": "Andrey D. Prjibelski",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Daria Shafranskaya"
+        },
+        {
+            "name": "Rob Finn"
+        },
+        {
+            "name": "Varsha Kale"
+        }
+    ],
+    "description": "metaGT is a bioinformatics analysis pipeline used for improving and quantification metatranscriptome assembly using metagenome data. The pipeline supports Illumina sequencing data and complete metagenome and metatranscriptome assemblies. The pipeline involves the alignment of metatranscriprome assembly to the metagenome assembly with further extracting CDSs, which are covered by transcripts.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "Sequencing quality control",
+                    "uri": "http://edamontology.org/operation_3218"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ablab/metaGT",
+    "language": [
+        "Groovy",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-08T00:36:32.234865Z",
+    "license": "MIT",
+    "name": "MetaGT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FMICB.2022.981458",
+            "metadata": {
+                "abstract": "Copyright © 2022 Shafranskaya, Kale, Finn, Lapidus, Korobeynikov and Prjibelski.While metagenome sequencing may provide insights on the genome sequences and composition of microbial communities, metatranscriptome analysis can be useful for studying the functional activity of a microbiome. RNA-Seq data provides the possibility to determine active genes in the community and how their expression levels depend on external conditions. Although the field of metatranscriptomics is relatively young, the number of projects related to metatranscriptome analysis increases every year and the scope of its applications expands. However, there are several problems that complicate metatranscriptome analysis: complexity of microbial communities, wide dynamic range of transcriptome expression and importantly, the lack of high-quality computational methods for assembling meta-RNA sequencing data. These factors deteriorate the contiguity and completeness of metatranscriptome assemblies, therefore affecting further downstream analysis. Here we present MetaGT, a pipeline for de novo assembly of metatranscriptomes, which is based on the idea of combining both metatranscriptomic and metagenomic data sequenced from the same sample. MetaGT assembles metatranscriptomic contigs and fills in missing regions based on their alignments to metagenome assembly. This approach allows to overcome described complexities and obtain complete RNA sequences, and additionally estimate their abundances. Using various publicly available real and simulated datasets, we demonstrate that MetaGT yields significant improvement in coverage and completeness of metatranscriptome assemblies compared to existing methods that do not exploit metagenomic data. The pipeline is implemented in NextFlow and is freely available from https://github.com/ablab/metaGT.",
+                "authors": [
+                    {
+                        "name": "Finn R."
+                    },
+                    {
+                        "name": "Kale V."
+                    },
+                    {
+                        "name": "Korobeynikov A."
+                    },
+                    {
+                        "name": "Lapidus A.L."
+                    },
+                    {
+                        "name": "Prjibelski A.D."
+                    },
+                    {
+                        "name": "Shafranskaya D."
+                    }
+                ],
+                "date": "2022-10-28T00:00:00Z",
+                "journal": "Frontiers in Microbiology",
+                "title": "MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data"
+            },
+            "pmcid": "PMC9651917",
+            "pmid": "36386613"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Metatranscriptomics",
+            "uri": "http://edamontology.org/topic_3941"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/metaline/metaline.biotools.json b/data/metaline/metaline.biotools.json
index dea4a086102cb..0fb3e094b5296 100644
--- a/data/metaline/metaline.biotools.json
+++ b/data/metaline/metaline.biotools.json
@@ -7,7 +7,10 @@
     ],
     "description": "metagenomics Taxonomic Assignation pipeline in Snakemake",
     "editPermission": {
-        "type": "private"
+        "authors": [
+            "sven_twardziok"
+        ],
+        "type": "group"
     },
     "function": [
         {
@@ -277,22 +280,22 @@
         "Python",
         "R"
     ],
-    "lastUpdate": "2022-05-26T14:21:17.767975Z",
-    "license": "GPL-2.0",
+    "lastUpdate": "2023-03-13T13:04:36.717726Z",
+    "license": "GPL-3.0",
     "maturity": "Emerging",
     "name": "meTAline",
     "owner": "Gabaldonlab",
     "relation": [
         {
-            "biotoolsID": "bowtie2",
+            "biotoolsID": "bracken",
             "type": "uses"
         },
         {
-            "biotoolsID": "bracken",
+            "biotoolsID": "fastqc",
             "type": "uses"
         },
         {
-            "biotoolsID": "fastqc",
+            "biotoolsID": "hisat2",
             "type": "uses"
         },
         {
diff --git a/data/metalwalls/metalwalls.biotools.json b/data/metalwalls/metalwalls.biotools.json
new file mode 100644
index 0000000000000..26dacd5c6e995
--- /dev/null
+++ b/data/metalwalls/metalwalls.biotools.json
@@ -0,0 +1,141 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T00:30:23.552688Z",
+    "biotoolsCURIE": "biotools:metalwalls",
+    "biotoolsID": "metalwalls",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Alessandro Coretti",
+            "orcidid": "https://orcid.org/0000-0002-7131-3210"
+        },
+        {
+            "name": "Camille Bacon",
+            "orcidid": "https://orcid.org/0000-0002-4373-3541"
+        },
+        {
+            "name": "Sara Bonella",
+            "orcidid": "https://orcid.org/0000-0003-4131-2513"
+        },
+        {
+            "name": "Mathieu Salanne",
+            "orcidid": "https://orcid.org/0000-0002-1753-491X",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Simulating electrochemical interfaces between polarizable electrolytes and metallic electrodes.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://gitlab.com/ampere2/metalwalls/-/wikis/home"
+        }
+    ],
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://zenodo.org/record/4912611"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://gitlab.com/ampere2/metalwalls",
+    "language": [
+        "C++",
+        "Fortran"
+    ],
+    "lastUpdate": "2023-02-08T00:30:23.555139Z",
+    "license": "MIT",
+    "name": "MetalWalls",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1063/5.0101777",
+            "metadata": {
+                "abstract": "© 2022 Author(s).Electrochemistry is central to many applications, ranging from biology to energy science. Studies now involve a wide range of techniques, both experimental and theoretical. Modeling and simulations methods, such as density functional theory or molecular dynamics, provide key information on the structural and dynamic properties of the systems. Of particular importance are polarization effects of the electrode/electrolyte interface, which are difficult to simulate accurately. Here, we show how these electrostatic interactions are taken into account in the framework of the Ewald summation method. We discuss, in particular, the formal setup for calculations that enforce periodic boundary conditions in two directions, a geometry that more closely reflects the characteristics of typical electrolyte/electrode systems and presents some differences with respect to the more common case of periodic boundary conditions in three dimensions. These formal developments are implemented and tested in MetalWalls, a molecular dynamics software that captures the polarization of the electrolyte and allows the simulation of electrodes maintained at a constant potential. We also discuss the technical aspects involved in the calculation of two sets of coupled degrees of freedom, namely the induced dipoles and the electrode charges. We validate the implementation, first on simple systems, then on the well-known interface between graphite electrodes and a room-temperature ionic liquid. We finally illustrate the capabilities of MetalWalls by studying the adsorption of a complex functionalized electrolyte on a graphite electrode.",
+                "authors": [
+                    {
+                        "name": "Bacon C."
+                    },
+                    {
+                        "name": "Berthin R."
+                    },
+                    {
+                        "name": "Bonella S."
+                    },
+                    {
+                        "name": "Chubak I."
+                    },
+                    {
+                        "name": "Coretti A."
+                    },
+                    {
+                        "name": "Goloviznina K."
+                    },
+                    {
+                        "name": "Haefele M."
+                    },
+                    {
+                        "name": "Marin-Lafleche A."
+                    },
+                    {
+                        "name": "Rotenberg B."
+                    },
+                    {
+                        "name": "Salanne M."
+                    },
+                    {
+                        "name": "Scalfi L."
+                    },
+                    {
+                        "name": "Serva A."
+                    }
+                ],
+                "citationCount": 3,
+                "date": "2022-11-14T00:00:00Z",
+                "journal": "Journal of Chemical Physics",
+                "title": "MetalWalls: Simulating electrochemical interfaces between polarizable electrolytes and metallic electrodes"
+            },
+            "pmid": "36379806"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biology",
+            "uri": "http://edamontology.org/topic_3070"
+        },
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "Molecular dynamics",
+            "uri": "http://edamontology.org/topic_0176"
+        }
+    ]
+}
diff --git a/data/metaphage/metaphage.biotools.json b/data/metaphage/metaphage.biotools.json
new file mode 100644
index 0000000000000..896acf4619338
--- /dev/null
+++ b/data/metaphage/metaphage.biotools.json
@@ -0,0 +1,120 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-20T00:11:57.272744Z",
+    "biotoolsCURIE": "biotools:metaphage",
+    "biotoolsID": "metaphage",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Evelien M. Adriaenssens"
+        },
+        {
+            "name": "Gioele Lazzari"
+        },
+        {
+            "name": "Mattia Pandolfo"
+        },
+        {
+            "name": "Andrea Telatin",
+            "orcidid": "http://orcid.org/0000-0001-7619-281X"
+        },
+        {
+            "name": "Nicola Vitulo",
+            "orcidid": "http://orcid.org/0000-0002-9571-0747"
+        }
+    ],
+    "description": "An automated pipeline for analyzing, annotating, and classifying bacteriophages in metagenomics sequencing data.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "Installation instructions",
+                "User manual"
+            ],
+            "url": "https://mattiapandolfovr.github.io/MetaPhage/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MattiaPandolfoVR/MetaPhage",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-01-20T00:11:57.275978Z",
+    "license": "GPL-3.0",
+    "name": "MetaPhage",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1128/msystems.00741-22",
+            "metadata": {
+                "abstract": "© 2022 Pandolfo et al.Phages are the most abundant biological entities on the planet, and they play an important role in controlling density, diversity, and network interactions among bacterial communities through predation and gene transfer. To date, a variety of bacteriophage identification tools have been developed that differ in the phage mining strategies used, input files requested, and results produced. However, new users attempting bacteriophage analysis can struggle to select the best methods and interpret the variety of results produced. Here, we present MetaPhage, a comprehensive reads-to-report pipeline that streamlines the use of multiple phage miners and generates an exhaustive report. The report both summarizes and visualizes the key findings and enables further exploration of key results via interactive filterable tables. The pipeline is implemented in Nextflow, a widely adopted workflow manager that enables an optimized parallelization of tasks in different locations, from local server to the cloud; this ensures reproducible results from containerized packages. MetaPhage is designed to enable scalability and reproducibility; also, it can be easily expanded to include new miners and methods as they are developed in this continuously growing field. MetaPhage is freely available under a GPL-3.0 license at https://github.com/ MattiaPandolfoVR/MetaPhage.",
+                "authors": [
+                    {
+                        "name": "Adriaenssens E.M."
+                    },
+                    {
+                        "name": "Lazzari G."
+                    },
+                    {
+                        "name": "Pandolfo M."
+                    },
+                    {
+                        "name": "Telatin A."
+                    },
+                    {
+                        "name": "Vitulo N."
+                    }
+                ],
+                "date": "2022-09-01T00:00:00Z",
+                "journal": "mSystems",
+                "title": "MetaPhage: an Automated Pipeline for Analyzing, Annotating, and Classifying Bacteriophages in Metagenomics Sequencing Data"
+            },
+            "pmcid": "PMC9599279",
+            "pmid": "36069454"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Metagenomic sequencing",
+            "uri": "http://edamontology.org/topic_3837"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/metastaar/metastaar.biotools.json b/data/metastaar/metastaar.biotools.json
new file mode 100644
index 0000000000000..c7812ac9d115c
--- /dev/null
+++ b/data/metastaar/metastaar.biotools.json
@@ -0,0 +1,1394 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-22T09:41:05.272414Z",
+    "biotoolsCURIE": "biotools:metastaar",
+    "biotoolsID": "metastaar",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Hufeng Zhou"
+        },
+        {
+            "name": "Corbin Quick",
+            "orcidid": "http://orcid.org/0000-0001-7199-2930"
+        },
+        {
+            "name": "Xihao Li",
+            "orcidid": "http://orcid.org/0000-0001-8151-0106"
+        },
+        {
+            "name": "Xihong Lin",
+            "orcidid": "http://orcid.org/0000-0001-7067-7752"
+        }
+    ],
+    "description": "Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/xihaoli/MetaSTAAR/blob/main/docs/MetaSTAAR_manual.pdf"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://zenodo.org/record/6668274"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Collapsing methods",
+                    "uri": "http://edamontology.org/operation_3791"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Sequence annotation",
+                    "uri": "http://edamontology.org/operation_0361"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/xihaoli/MetaSTAAR",
+    "language": [
+        "C++",
+        "R"
+    ],
+    "lastUpdate": "2023-02-22T09:41:05.274977Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/xihaoli/STAAR"
+        }
+    ],
+    "name": "MetaSTAAR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41588-022-01225-6",
+            "metadata": {
+                "abstract": "© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.",
+                "authors": [
+                    {
+                        "name": "Abe N."
+                    },
+                    {
+                        "name": "Abecasis G."
+                    },
+                    {
+                        "name": "Aguet F."
+                    },
+                    {
+                        "name": "Albert C."
+                    },
+                    {
+                        "name": "Almasy L."
+                    },
+                    {
+                        "name": "Alonso A."
+                    },
+                    {
+                        "name": "Ament S."
+                    },
+                    {
+                        "name": "Anderson P."
+                    },
+                    {
+                        "name": "Anugu P."
+                    },
+                    {
+                        "name": "Applebaum-Bowden D."
+                    },
+                    {
+                        "name": "Ardlie K."
+                    },
+                    {
+                        "name": "Arnett D.K."
+                    },
+                    {
+                        "name": "Ashley-Koch A."
+                    },
+                    {
+                        "name": "Aslibekyan S."
+                    },
+                    {
+                        "name": "Assimes T."
+                    },
+                    {
+                        "name": "Auer P."
+                    },
+                    {
+                        "name": "Avramopoulos D."
+                    },
+                    {
+                        "name": "Ayas N."
+                    },
+                    {
+                        "name": "Balasubramanian A."
+                    },
+                    {
+                        "name": "Barnard J."
+                    },
+                    {
+                        "name": "Barnes K."
+                    },
+                    {
+                        "name": "Barr R.G."
+                    },
+                    {
+                        "name": "Barron-Casella E."
+                    },
+                    {
+                        "name": "Barwick L."
+                    },
+                    {
+                        "name": "Beaty T."
+                    },
+                    {
+                        "name": "Beck G."
+                    },
+                    {
+                        "name": "Becker D."
+                    },
+                    {
+                        "name": "Becker J.P."
+                    },
+                    {
+                        "name": "Becker L."
+                    },
+                    {
+                        "name": "Beer R."
+                    },
+                    {
+                        "name": "Beitelshees A."
+                    },
+                    {
+                        "name": "Ben Heavner"
+                    },
+                    {
+                        "name": "Benjamin E."
+                    },
+                    {
+                        "name": "Benos T."
+                    },
+                    {
+                        "name": "Bezerra M."
+                    },
+                    {
+                        "name": "Bielak L.F."
+                    },
+                    {
+                        "name": "Bis J.C."
+                    },
+                    {
+                        "name": "Blackwell T."
+                    },
+                    {
+                        "name": "Blangero J."
+                    },
+                    {
+                        "name": "Blue N."
+                    },
+                    {
+                        "name": "Boerwinkle E."
+                    },
+                    {
+                        "name": "Boorgula M.P."
+                    },
+                    {
+                        "name": "Bowden D.W."
+                    },
+                    {
+                        "name": "Bowler R."
+                    },
+                    {
+                        "name": "Brody J.A."
+                    },
+                    {
+                        "name": "Broeckel U."
+                    },
+                    {
+                        "name": "Broome J."
+                    },
+                    {
+                        "name": "Brown D."
+                    },
+                    {
+                        "name": "Bunting K."
+                    },
+                    {
+                        "name": "Burchard E."
+                    },
+                    {
+                        "name": "Bustamante C."
+                    },
+                    {
+                        "name": "Buth E."
+                    },
+                    {
+                        "name": "Cade B.E."
+                    },
+                    {
+                        "name": "Cardwell J."
+                    },
+                    {
+                        "name": "Carey V."
+                    },
+                    {
+                        "name": "Carrier J."
+                    },
+                    {
+                        "name": "Carson A."
+                    },
+                    {
+                        "name": "Carty C."
+                    },
+                    {
+                        "name": "Casaburi R."
+                    },
+                    {
+                        "name": "Casas Romero J.P."
+                    },
+                    {
+                        "name": "Casella J."
+                    },
+                    {
+                        "name": "Castaldi P."
+                    },
+                    {
+                        "name": "Chaffin M."
+                    },
+                    {
+                        "name": "Chang C."
+                    },
+                    {
+                        "name": "Chang Y.-C."
+                    },
+                    {
+                        "name": "Chasman D."
+                    },
+                    {
+                        "name": "Chavan S."
+                    },
+                    {
+                        "name": "Chen B.-J."
+                    },
+                    {
+                        "name": "Chen H."
+                    },
+                    {
+                        "name": "Chen W.-M."
+                    },
+                    {
+                        "name": "Chen Y.-D.I."
+                    },
+                    {
+                        "name": "Cho M."
+                    },
+                    {
+                        "name": "Choi S.H."
+                    },
+                    {
+                        "name": "Chuang L.-M."
+                    },
+                    {
+                        "name": "Chung M."
+                    },
+                    {
+                        "name": "Chung R.-H."
+                    },
+                    {
+                        "name": "Clish C."
+                    },
+                    {
+                        "name": "Comhair S."
+                    },
+                    {
+                        "name": "Conomos M."
+                    },
+                    {
+                        "name": "Cornell E."
+                    },
+                    {
+                        "name": "Correa A."
+                    },
+                    {
+                        "name": "Crandall C."
+                    },
+                    {
+                        "name": "Crapo J."
+                    },
+                    {
+                        "name": "Cupples L.A."
+                    },
+                    {
+                        "name": "Curran J.E."
+                    },
+                    {
+                        "name": "Curtis J."
+                    },
+                    {
+                        "name": "Custer B."
+                    },
+                    {
+                        "name": "Damcott C."
+                    },
+                    {
+                        "name": "Dan Arking"
+                    },
+                    {
+                        "name": "Dan Roden"
+                    },
+                    {
+                        "name": "Darbar D."
+                    },
+                    {
+                        "name": "David S."
+                    },
+                    {
+                        "name": "Davis C."
+                    },
+                    {
+                        "name": "Daya M."
+                    },
+                    {
+                        "name": "DeBaun M."
+                    },
+                    {
+                        "name": "DeMeo D."
+                    },
+                    {
+                        "name": "Deka R."
+                    },
+                    {
+                        "name": "Devine S."
+                    },
+                    {
+                        "name": "Dey R."
+                    },
+                    {
+                        "name": "Dinh H."
+                    },
+                    {
+                        "name": "Doddapaneni H."
+                    },
+                    {
+                        "name": "Duan Q."
+                    },
+                    {
+                        "name": "Dugan-Perez S."
+                    },
+                    {
+                        "name": "Duggirala R."
+                    },
+                    {
+                        "name": "Durda J.P."
+                    },
+                    {
+                        "name": "Dutcher S.K."
+                    },
+                    {
+                        "name": "Eaton C."
+                    },
+                    {
+                        "name": "Ekunwe L."
+                    },
+                    {
+                        "name": "El Boueiz A."
+                    },
+                    {
+                        "name": "Ellinor P."
+                    },
+                    {
+                        "name": "Emery L."
+                    },
+                    {
+                        "name": "Erzurum S."
+                    },
+                    {
+                        "name": "Farber C."
+                    },
+                    {
+                        "name": "Farek J."
+                    },
+                    {
+                        "name": "Fingerlin T."
+                    },
+                    {
+                        "name": "Flickinger M."
+                    },
+                    {
+                        "name": "Fornage M."
+                    },
+                    {
+                        "name": "Franceschini N."
+                    },
+                    {
+                        "name": "Frazar C."
+                    },
+                    {
+                        "name": "Freedman B.I."
+                    },
+                    {
+                        "name": "Fu M."
+                    },
+                    {
+                        "name": "Fullerton S.M."
+                    },
+                    {
+                        "name": "Fulton L."
+                    },
+                    {
+                        "name": "Gabriel S."
+                    },
+                    {
+                        "name": "Gan W."
+                    },
+                    {
+                        "name": "Gao S."
+                    },
+                    {
+                        "name": "Gao Y."
+                    },
+                    {
+                        "name": "Gass M."
+                    },
+                    {
+                        "name": "Gaynor S.M."
+                    },
+                    {
+                        "name": "Geiger H."
+                    },
+                    {
+                        "name": "Gelb B."
+                    },
+                    {
+                        "name": "Geraci M."
+                    },
+                    {
+                        "name": "Germer S."
+                    },
+                    {
+                        "name": "Gerszten R."
+                    },
+                    {
+                        "name": "Ghosh A."
+                    },
+                    {
+                        "name": "Gibbs R."
+                    },
+                    {
+                        "name": "Gignoux C."
+                    },
+                    {
+                        "name": "Gladwin M."
+                    },
+                    {
+                        "name": "Glahn D."
+                    },
+                    {
+                        "name": "Gogarten S."
+                    },
+                    {
+                        "name": "Gong D.-W."
+                    },
+                    {
+                        "name": "Goring H.H.H."
+                    },
+                    {
+                        "name": "Graw S."
+                    },
+                    {
+                        "name": "Gray K.J."
+                    },
+                    {
+                        "name": "Grine D."
+                    },
+                    {
+                        "name": "Gross C."
+                    },
+                    {
+                        "name": "Gu C.C."
+                    },
+                    {
+                        "name": "Guan Y."
+                    },
+                    {
+                        "name": "Guo X."
+                    },
+                    {
+                        "name": "Gupta N."
+                    },
+                    {
+                        "name": "Haessler J."
+                    },
+                    {
+                        "name": "Hall M."
+                    },
+                    {
+                        "name": "Han Y."
+                    },
+                    {
+                        "name": "Hanly P."
+                    },
+                    {
+                        "name": "Harris D."
+                    },
+                    {
+                        "name": "Hawley N.L."
+                    },
+                    {
+                        "name": "He J."
+                    },
+                    {
+                        "name": "Heckbert S."
+                    },
+                    {
+                        "name": "Hernandez R."
+                    },
+                    {
+                        "name": "Herrington D."
+                    },
+                    {
+                        "name": "Hersh C."
+                    },
+                    {
+                        "name": "Hidalgo B."
+                    },
+                    {
+                        "name": "Hixson J."
+                    },
+                    {
+                        "name": "Hobbs B."
+                    },
+                    {
+                        "name": "Hokanson J."
+                    },
+                    {
+                        "name": "Hong E."
+                    },
+                    {
+                        "name": "Hoth K."
+                    },
+                    {
+                        "name": "Hsiung C.A."
+                    },
+                    {
+                        "name": "Hu J."
+                    },
+                    {
+                        "name": "Hung Y.-J."
+                    },
+                    {
+                        "name": "Huston H."
+                    },
+                    {
+                        "name": "Hwu C.M."
+                    },
+                    {
+                        "name": "Irvin M.R."
+                    },
+                    {
+                        "name": "Jackson R."
+                    },
+                    {
+                        "name": "Jain D."
+                    },
+                    {
+                        "name": "Jaquish C."
+                    },
+                    {
+                        "name": "Johnsen J."
+                    },
+                    {
+                        "name": "Johnson A."
+                    },
+                    {
+                        "name": "Johnson C."
+                    },
+                    {
+                        "name": "Johnston R."
+                    },
+                    {
+                        "name": "Jones K."
+                    },
+                    {
+                        "name": "Kalyani R.R."
+                    },
+                    {
+                        "name": "Kang H.M."
+                    },
+                    {
+                        "name": "Kaplan R."
+                    },
+                    {
+                        "name": "Kardia S."
+                    },
+                    {
+                        "name": "Kelly S."
+                    },
+                    {
+                        "name": "Kenny E."
+                    },
+                    {
+                        "name": "Kessler M."
+                    },
+                    {
+                        "name": "Khan A."
+                    },
+                    {
+                        "name": "Khan Z."
+                    },
+                    {
+                        "name": "Kim W."
+                    },
+                    {
+                        "name": "Kimoff J."
+                    },
+                    {
+                        "name": "Kinney G."
+                    },
+                    {
+                        "name": "Konkle B."
+                    },
+                    {
+                        "name": "Kooperberg C."
+                    },
+                    {
+                        "name": "Kral B.G."
+                    },
+                    {
+                        "name": "Kramer H."
+                    },
+                    {
+                        "name": "Lange C."
+                    },
+                    {
+                        "name": "Lange E."
+                    },
+                    {
+                        "name": "Lange L.A."
+                    },
+                    {
+                        "name": "Laurie C."
+                    },
+                    {
+                        "name": "Laurie C."
+                    },
+                    {
+                        "name": "LeBoff M."
+                    },
+                    {
+                        "name": "LeFaive J."
+                    },
+                    {
+                        "name": "Lee J."
+                    },
+                    {
+                        "name": "Lee S."
+                    },
+                    {
+                        "name": "Lee W.-J."
+                    },
+                    {
+                        "name": "Levine D."
+                    },
+                    {
+                        "name": "Levy D."
+                    },
+                    {
+                        "name": "Lewis J."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Li Z."
+                    },
+                    {
+                        "name": "Lin H."
+                    },
+                    {
+                        "name": "Lin H."
+                    },
+                    {
+                        "name": "Lin X."
+                    },
+                    {
+                        "name": "Liu S."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Loos R.J.F."
+                    },
+                    {
+                        "name": "Lubitz S."
+                    },
+                    {
+                        "name": "Lunetta K."
+                    },
+                    {
+                        "name": "Luo J."
+                    },
+                    {
+                        "name": "Magalang U."
+                    },
+                    {
+                        "name": "Mahaney M."
+                    },
+                    {
+                        "name": "Make B."
+                    },
+                    {
+                        "name": "Manichaikul A."
+                    },
+                    {
+                        "name": "Manning A."
+                    },
+                    {
+                        "name": "Manson J.A."
+                    },
+                    {
+                        "name": "Martin L.W."
+                    },
+                    {
+                        "name": "Marton M."
+                    },
+                    {
+                        "name": "Mathai S."
+                    },
+                    {
+                        "name": "Mathias R."
+                    },
+                    {
+                        "name": "May S."
+                    },
+                    {
+                        "name": "McArdle P."
+                    },
+                    {
+                        "name": "McDonald M.-L."
+                    },
+                    {
+                        "name": "McFarland S."
+                    },
+                    {
+                        "name": "McGarvey S.T."
+                    },
+                    {
+                        "name": "McGoldrick D."
+                    },
+                    {
+                        "name": "McHugh C."
+                    },
+                    {
+                        "name": "McNeil B."
+                    },
+                    {
+                        "name": "Mei H."
+                    },
+                    {
+                        "name": "Meigs J."
+                    },
+                    {
+                        "name": "Menon V."
+                    },
+                    {
+                        "name": "Mestroni L."
+                    },
+                    {
+                        "name": "Metcalf G."
+                    },
+                    {
+                        "name": "Meyers D.A."
+                    },
+                    {
+                        "name": "Mignot E."
+                    },
+                    {
+                        "name": "Mikulla J."
+                    },
+                    {
+                        "name": "Min N."
+                    },
+                    {
+                        "name": "Minear M."
+                    },
+                    {
+                        "name": "Minster R.L."
+                    },
+                    {
+                        "name": "Mitchell B.D."
+                    },
+                    {
+                        "name": "Moll M."
+                    },
+                    {
+                        "name": "Momin Z."
+                    },
+                    {
+                        "name": "Montasser M.E."
+                    },
+                    {
+                        "name": "Montgomery C."
+                    },
+                    {
+                        "name": "Morrison A.C."
+                    },
+                    {
+                        "name": "Muzny D."
+                    },
+                    {
+                        "name": "Mychaleckyj J.C."
+                    },
+                    {
+                        "name": "Nadkarni G."
+                    },
+                    {
+                        "name": "Naik R."
+                    },
+                    {
+                        "name": "Naseri T."
+                    },
+                    {
+                        "name": "Natarajan P."
+                    },
+                    {
+                        "name": "Nekhai S."
+                    },
+                    {
+                        "name": "Nelson S.C."
+                    },
+                    {
+                        "name": "Neltner B."
+                    },
+                    {
+                        "name": "Nessner C."
+                    },
+                    {
+                        "name": "Nickerson D."
+                    },
+                    {
+                        "name": "Nkechinyere O."
+                    },
+                    {
+                        "name": "North K."
+                    },
+                    {
+                        "name": "O'Connell J.R."
+                    },
+                    {
+                        "name": "O'Connor T."
+                    },
+                    {
+                        "name": "Ochs-Balcom H."
+                    },
+                    {
+                        "name": "Okwuonu G."
+                    },
+                    {
+                        "name": "Pack A."
+                    },
+                    {
+                        "name": "Paik D.T."
+                    },
+                    {
+                        "name": "Palmer N.D."
+                    },
+                    {
+                        "name": "Pankow J."
+                    },
+                    {
+                        "name": "Papanicolaou G."
+                    },
+                    {
+                        "name": "Parker C."
+                    },
+                    {
+                        "name": "Peloso G.M."
+                    },
+                    {
+                        "name": "Peralta J.M."
+                    },
+                    {
+                        "name": "Perez M."
+                    },
+                    {
+                        "name": "Perry J."
+                    },
+                    {
+                        "name": "Peters U."
+                    },
+                    {
+                        "name": "Peyser P.A."
+                    },
+                    {
+                        "name": "Phillips L.S."
+                    },
+                    {
+                        "name": "Pleiness J."
+                    },
+                    {
+                        "name": "Pollin T."
+                    },
+                    {
+                        "name": "Post W."
+                    },
+                    {
+                        "name": "Preuss M."
+                    },
+                    {
+                        "name": "Psaty B.M."
+                    },
+                    {
+                        "name": "Qasba P."
+                    },
+                    {
+                        "name": "Qiao D."
+                    },
+                    {
+                        "name": "Qin Z."
+                    },
+                    {
+                        "name": "Quick C."
+                    },
+                    {
+                        "name": "Rafaels N."
+                    },
+                    {
+                        "name": "Raffield L.M."
+                    },
+                    {
+                        "name": "Rajendran M."
+                    },
+                    {
+                        "name": "Rao D.C."
+                    },
+                    {
+                        "name": "Rasmussen-Torvik L."
+                    },
+                    {
+                        "name": "Ratan A."
+                    },
+                    {
+                        "name": "Redline S."
+                    },
+                    {
+                        "name": "Reed R."
+                    },
+                    {
+                        "name": "Reeves C."
+                    },
+                    {
+                        "name": "Regan E."
+                    },
+                    {
+                        "name": "Reiner A.P."
+                    },
+                    {
+                        "name": "Reupena M.S."
+                    },
+                    {
+                        "name": "Rice K.M."
+                    },
+                    {
+                        "name": "Rich S.S."
+                    },
+                    {
+                        "name": "Robillard R."
+                    },
+                    {
+                        "name": "Robine N."
+                    },
+                    {
+                        "name": "Roselli C."
+                    },
+                    {
+                        "name": "Rotter J.I."
+                    },
+                    {
+                        "name": "Ruczinski I."
+                    },
+                    {
+                        "name": "Runnels A."
+                    },
+                    {
+                        "name": "Russell P."
+                    },
+                    {
+                        "name": "Ruuska S."
+                    },
+                    {
+                        "name": "Ryan K."
+                    },
+                    {
+                        "name": "Sabino E.C."
+                    },
+                    {
+                        "name": "Saleheen D."
+                    },
+                    {
+                        "name": "Salimi S."
+                    },
+                    {
+                        "name": "Salvi S."
+                    },
+                    {
+                        "name": "Salzberg S."
+                    },
+                    {
+                        "name": "Sandow K."
+                    },
+                    {
+                        "name": "Sankaran V.G."
+                    },
+                    {
+                        "name": "Santibanez J."
+                    },
+                    {
+                        "name": "Schwander K."
+                    },
+                    {
+                        "name": "Schwartz D."
+                    },
+                    {
+                        "name": "Sciurba F."
+                    },
+                    {
+                        "name": "Seidman C."
+                    },
+                    {
+                        "name": "Seidman J."
+                    },
+                    {
+                        "name": "Selvaraj M.S."
+                    },
+                    {
+                        "name": "Series F."
+                    },
+                    {
+                        "name": "Sheehan V."
+                    },
+                    {
+                        "name": "Sherman S.L."
+                    },
+                    {
+                        "name": "Shetty A."
+                    },
+                    {
+                        "name": "Shetty A."
+                    },
+                    {
+                        "name": "Sheu W.H.-H."
+                    },
+                    {
+                        "name": "Shoemaker M.B."
+                    },
+                    {
+                        "name": "Silver B."
+                    },
+                    {
+                        "name": "Silverman E."
+                    },
+                    {
+                        "name": "Sitlani C.M."
+                    },
+                    {
+                        "name": "Skomro R."
+                    },
+                    {
+                        "name": "Smith A.V."
+                    },
+                    {
+                        "name": "Smith J."
+                    },
+                    {
+                        "name": "Smith J.A."
+                    },
+                    {
+                        "name": "Smith N."
+                    },
+                    {
+                        "name": "Smith T."
+                    },
+                    {
+                        "name": "Smoller S."
+                    },
+                    {
+                        "name": "Snively B."
+                    },
+                    {
+                        "name": "Snyder M."
+                    },
+                    {
+                        "name": "Sofer T."
+                    },
+                    {
+                        "name": "Sotoodehnia N."
+                    },
+                    {
+                        "name": "Stilp A.M."
+                    },
+                    {
+                        "name": "Storm G."
+                    },
+                    {
+                        "name": "Streeten E."
+                    },
+                    {
+                        "name": "Su J.L."
+                    },
+                    {
+                        "name": "Sun R."
+                    },
+                    {
+                        "name": "Sung Y.J."
+                    },
+                    {
+                        "name": "Sylvia J."
+                    },
+                    {
+                        "name": "Szpiro A."
+                    },
+                    {
+                        "name": "Taliun D."
+                    },
+                    {
+                        "name": "Tang H."
+                    },
+                    {
+                        "name": "Taub M."
+                    },
+                    {
+                        "name": "Taylor K.D."
+                    },
+                    {
+                        "name": "Taylor M."
+                    },
+                    {
+                        "name": "Taylor S."
+                    },
+                    {
+                        "name": "Telen M."
+                    },
+                    {
+                        "name": "Thornton T.A."
+                    },
+                    {
+                        "name": "Threlkeld M."
+                    },
+                    {
+                        "name": "Tinker L."
+                    },
+                    {
+                        "name": "Tirschwell D."
+                    },
+                    {
+                        "name": "Tishkoff S."
+                    },
+                    {
+                        "name": "Tiwari H."
+                    },
+                    {
+                        "name": "Tong C."
+                    },
+                    {
+                        "name": "Tracy R."
+                    },
+                    {
+                        "name": "Tsai M."
+                    },
+                    {
+                        "name": "Vaidya D."
+                    },
+                    {
+                        "name": "Van Den Berg D."
+                    },
+                    {
+                        "name": "VandeHaar P."
+                    },
+                    {
+                        "name": "Vasan R.S."
+                    },
+                    {
+                        "name": "Vrieze S."
+                    },
+                    {
+                        "name": "Walker T."
+                    },
+                    {
+                        "name": "Wallace R."
+                    },
+                    {
+                        "name": "Walts A."
+                    },
+                    {
+                        "name": "Wang F.F."
+                    },
+                    {
+                        "name": "Wang H."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Watson K."
+                    },
+                    {
+                        "name": "Watt J."
+                    },
+                    {
+                        "name": "Weeks D.E."
+                    },
+                    {
+                        "name": "Weinstock J."
+                    },
+                    {
+                        "name": "Weir B."
+                    },
+                    {
+                        "name": "Weiss S.T."
+                    },
+                    {
+                        "name": "Weng L.-C."
+                    },
+                    {
+                        "name": "Wessel J."
+                    },
+                    {
+                        "name": "Willer C.J."
+                    },
+                    {
+                        "name": "Williams K."
+                    },
+                    {
+                        "name": "Williams L.K."
+                    },
+                    {
+                        "name": "Wilson C."
+                    },
+                    {
+                        "name": "Wilson J.G."
+                    },
+                    {
+                        "name": "Winterkorn L."
+                    },
+                    {
+                        "name": "Wong Q."
+                    },
+                    {
+                        "name": "Wu J."
+                    },
+                    {
+                        "name": "Xu H."
+                    },
+                    {
+                        "name": "Yanek L.R."
+                    },
+                    {
+                        "name": "Yang I."
+                    },
+                    {
+                        "name": "Yu K."
+                    },
+                    {
+                        "name": "Zekavat S.M."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zhao S.X."
+                    },
+                    {
+                        "name": "Zhao W."
+                    },
+                    {
+                        "name": "Zhou H."
+                    },
+                    {
+                        "name": "Zhu X."
+                    },
+                    {
+                        "name": "Ziv E."
+                    },
+                    {
+                        "name": "Zody M."
+                    },
+                    {
+                        "name": "Zoellner S."
+                    },
+                    {
+                        "name": "de Andrade M."
+                    },
+                    {
+                        "name": "de Vries P.S."
+                    },
+                    {
+                        "name": "de las Fuentes L."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Nature Genetics",
+                "title": "Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies"
+            },
+            "pmid": "36564505"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cardiology",
+            "uri": "http://edamontology.org/topic_3335"
+        },
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Lipids",
+            "uri": "http://edamontology.org/topic_0153"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/methbank/methbank.biotools.json b/data/methbank/methbank.biotools.json
new file mode 100644
index 0000000000000..dc4bb790b1519
--- /dev/null
+++ b/data/methbank/methbank.biotools.json
@@ -0,0 +1,162 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T00:21:43.202823Z",
+    "biotoolsCURIE": "biotools:methbank",
+    "biotoolsID": "methbank",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhangzhang@big.ac.cn",
+            "name": "Zhang Zhang",
+            "orcidid": "https://orcid.org/0000-0001-6603-5060",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "baoym@big.ac.cn",
+            "name": "Yiming Bao",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "lirj@big.ac.cn",
+            "name": "Rujiao Li",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Mochen Zhang",
+            "orcidid": "https://orcid.org/0000-0001-9136-451X"
+        }
+    ],
+    "description": "An updated database of DNA methylation across a variety of species.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Species name",
+                        "uri": "http://edamontology.org/data_1045"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Bisulfite mapping",
+                    "uri": "http://edamontology.org/operation_3186"
+                },
+                {
+                    "term": "DMR identification",
+                    "uri": "http://edamontology.org/operation_3809"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Filtering",
+                    "uri": "http://edamontology.org/operation_3695"
+                },
+                {
+                    "term": "Gene methylation analysis",
+                    "uri": "http://edamontology.org/operation_3207"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ngdc.cncb.ac.cn/methbank/",
+    "lastUpdate": "2023-02-08T00:21:43.205291Z",
+    "name": "MethBank",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC969",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.DNA methylation, as the most intensively studied epigenetic mark, regulates gene expression in numerous biological processes including development, aging, and disease. With the rapid accumulation of whole-genome bisulfite sequencing data, integrating, archiving, analyzing, and visualizing those data becomes critical. Since its first publication in 2015, MethBank has been continuously updated to include more DNA methylomes across more diverse species. Here, we present MethBank 4.0 (https://ngdc.cncb.ac.cn/methbank/), which reports an increase of 309% in data volume, with 1449 single-base resolution methylomes of 23 species, covering 236 tissues/cell lines and 15 biological contexts. Value-added information, such as more rigorous quality evaluation, more standardized metadata, and comprehensive downstream annotations have been integrated in the new version. Moreover, expert-curated knowledge modules of featured differentially methylated genes associated with biological contexts and methylation analysis tools have been incorporated as new components of MethBank. In addition, MethBank 4.0 is equipped with a series of new web interfaces to browse, search, and visualize DNA methylation profiles and related information. With all these improvements, we believe the updated MethBank 4.0 will serve as a fundamental resource to provide a wide range of data services for the global research community.",
+                "authors": [
+                    {
+                        "name": "Bao Y."
+                    },
+                    {
+                        "name": "Guo X."
+                    },
+                    {
+                        "name": "Li R."
+                    },
+                    {
+                        "name": "Ma Y."
+                    },
+                    {
+                        "name": "Wang G."
+                    },
+                    {
+                        "name": "Wu S."
+                    },
+                    {
+                        "name": "Xiong Z."
+                    },
+                    {
+                        "name": "Yang F."
+                    },
+                    {
+                        "name": "Zhang M."
+                    },
+                    {
+                        "name": "Zhang X."
+                    },
+                    {
+                        "name": "Zhang Z."
+                    },
+                    {
+                        "name": "Zhao W."
+                    },
+                    {
+                        "name": "Zong W."
+                    },
+                    {
+                        "name": "Zou D."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "MethBank 4.0: an updated database of DNA methylation across a variety of species"
+            },
+            "pmcid": "PMC9825483",
+            "pmid": "36318250"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Methylated DNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3674"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ],
+    "version": [
+        "4.0"
+    ]
+}
diff --git a/data/mgidi/mgidi.biotools.json b/data/mgidi/mgidi.biotools.json
new file mode 100644
index 0000000000000..d46b72e531926
--- /dev/null
+++ b/data/mgidi/mgidi.biotools.json
@@ -0,0 +1,115 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T00:14:52.436336Z",
+    "biotoolsCURIE": "biotools:mgidi",
+    "biotoolsID": "mgidi",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tiagoolivoto@gmail.com",
+            "name": "Tiago Olivoto",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alessandro D. Lúcio"
+        },
+        {
+            "name": "Denise Schmidt"
+        },
+        {
+            "name": "Maria I. Diel"
+        }
+    ],
+    "description": "A powerful tool to analyze plant multivariate data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bit.ly/site-mgidi-pm",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-08T00:14:52.439585Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/TiagoOlivoto/paper_mgidi_pm"
+        }
+    ],
+    "name": "MGIDI",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13007-022-00952-5",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Commonly, several traits are assessed in agronomic experiments to better understand the factors under study. However, it is also common to see that even when several traits are available, researchers opt to follow the easiest way by applying univariate analyses and post-hoc tests for mean comparison for each trait, which arouses the hypothesis that the benefits of a multi-trait framework analysis may have not been fully exploited in this area. Results: In this paper, we extended the theoretical foundations of the multi-trait genotype-ideotype distance index (MGIDI) to analyze multivariate data either in simple experiments (e.g., one-way layout with few treatments and traits) or complex experiments (e.g., with a factorial treatment structure). We proposed an optional weighting process that makes the ranking of treatments that stands out in traits with higher weights more likely. Its application is illustrated using (1) simulated data and (2) real data from a strawberry experiment that aims to select better factor combinations (namely, cultivar, transplant origin, and substrate mixture) based on the desired performance of 22 phenological, productive, physiological, and qualitative traits. Our results show that most of the strawberry traits are influenced by the cultivar, transplant origin, cultivation substrates, as well as by the interaction between cultivar and transplant origin. The MGIDI ranked the Albion cultivar originated from Imported transplants and the Camarosa cultivar originated from National transplants as the better factor combinations. The substrates with burned rice husk as the main component (70%) showed satisfactory physical proprieties, providing higher water use efficiency. The strengths and weakness view provided by the MGIDI revealed that looking for an ideal treatment should direct the efforts on increasing fruit production of Albion transplants from Imported origin. On the other hand, this treatment has strengths related to productive precocity, total soluble solids, and flesh firmness. Conclusions: Overall, this study opens the door to the use of MGIDI beyond the plant breeding context, providing a unique, practical, robust, and easy-to-handle multi-trait-based framework to analyze multivariate data. There is an exciting possibility for this to open up new avenues of research, mainly because using the MGIDI in future studies will dramatically reduce the number of tables/figures needed, serving as a powerful tool to guide researchers toward better treatment recommendations.",
+                "authors": [
+                    {
+                        "name": "Diel M.I."
+                    },
+                    {
+                        "name": "Lucio A.D."
+                    },
+                    {
+                        "name": "Olivoto T."
+                    },
+                    {
+                        "name": "Schmidt D."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Plant Methods",
+                "title": "MGIDI: a powerful tool to analyze plant multivariate data"
+            },
+            "pmcid": "PMC9652799",
+            "pmid": "36371210"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Physics",
+            "uri": "http://edamontology.org/topic_3318"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Surgery",
+            "uri": "http://edamontology.org/topic_3421"
+        }
+    ]
+}
diff --git a/data/mgtdb/mgtdb.biotools.json b/data/mgtdb/mgtdb.biotools.json
new file mode 100644
index 0000000000000..0e9456634667b
--- /dev/null
+++ b/data/mgtdb/mgtdb.biotools.json
@@ -0,0 +1,146 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T14:43:58.779736Z",
+    "biotoolsCURIE": "biotools:mgtdb",
+    "biotoolsID": "mgtdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "r.lan@unsw.edu.au",
+            "name": "Ruiting Lan",
+            "orcidid": "http://orcid.org/0000-0001-9834-5258",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Mark M. Tanaka",
+            "orcidid": "http://orcid.org/0000-0003-2198-1402"
+        },
+        {
+            "name": "Michael Payne",
+            "orcidid": "http://orcid.org/0000-0003-1911-7033"
+        },
+        {
+            "name": "Sandeep Kaur",
+            "orcidid": "http://orcid.org/0000-0003-0356-3151"
+        },
+        {
+            "name": "Vitali Sintchenko",
+            "orcidid": "http://orcid.org/0000-0002-9261-3650"
+        }
+    ],
+    "description": "A web service and database for studying the global and local genomic epidemiology of bacterial pathogens.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://mgt-docs.readthedocs.io/en/latest/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Multilocus sequence typing",
+                    "uri": "http://edamontology.org/operation_3840"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://microreact.org/project/bjdgCuWdVowtJHwq2npYxi-mgt5-st12-vibrio-cholerae",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T14:43:58.782218Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/LanLab/MGT"
+        }
+    ],
+    "name": "MGTdb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/database/baac094",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press.Multilevel genome typing (MGT) enables the genomic characterization of bacterial isolates and the relationships among them. The MGT system describes an isolate using multiple multilocus sequence typing (MLST) schemes, referred to as levels. Thus, for a new isolate, sequence types (STs) assigned at multiple precisely defined levels can be used to type isolates at multiple resolutions. The MGT designation for isolates is stable, and the assignment is faster than the existing approaches. MGT's utility has been demonstrated in multiple species. This paper presents a publicly accessible web service called MGTdb, which enables the assignment of MGT STs to isolates, along with their storage, retrieval and analysis. The MGTdb web service enables upload of genome data as sequence reads or alleles, which are processed and assigned MGT identifiers. Additionally, any newly sequenced isolates deposited in the National Center for Biotechnology Information's Sequence Read Archive are also regularly retrieved (currently daily), processed, assigned MGT identifiers and made publicly available in MGTdb. Interactive visualization tools are presented to assist analysis, along with capabilities to download publicly available isolates and assignments for use with external software. MGTdb is currently available for Salmonella enterica serovars Typhimurium and Enteritidis and Vibrio cholerae. We demonstrate the usability of MGTdb through three case studies-to study the long-Term national surveillance of S. Typhimurium, the local epidemiology and outbreaks of S. Typhimurium, and the global epidemiology of V. cholerae. Thus, MGTdb enables epidemiological and microbiological investigations at multiple levels of resolution for all publicly available isolates of these pathogens. Database URL: https://mgtdb.unsw.edu.au",
+                "authors": [
+                    {
+                        "name": "Kaur S."
+                    },
+                    {
+                        "name": "Lan R."
+                    },
+                    {
+                        "name": "Luo L."
+                    },
+                    {
+                        "name": "Octavia S."
+                    },
+                    {
+                        "name": "Payne M."
+                    },
+                    {
+                        "name": "Sintchenko V."
+                    },
+                    {
+                        "name": "Tanaka M.M."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Database",
+                "title": "MGTdb: A web service and database for studying the global and local genomic epidemiology of bacterial pathogens"
+            },
+            "pmcid": "PMC9650772",
+            "pmid": "36367311"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Microbiology",
+            "uri": "http://edamontology.org/topic_3301"
+        },
+        {
+            "term": "Phylogeny",
+            "uri": "http://edamontology.org/topic_0084"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/mha/mha.biotools.json b/data/mha/mha.biotools.json
new file mode 100644
index 0000000000000..2056ab73626d8
--- /dev/null
+++ b/data/mha/mha.biotools.json
@@ -0,0 +1,104 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T23:05:48.793378Z",
+    "biotoolsCURIE": "biotools:mha",
+    "biotoolsID": "mha",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zlyinwangyi@163.com",
+            "name": "LiangYu Zhao",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "ChenCheng Yao"
+        },
+        {
+            "name": "YiFan Zhao"
+        },
+        {
+            "name": "YuXin Tang"
+        }
+    ],
+    "description": "Interactive website for scRNA-seq data of male genitourinary development and disease.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://malehealthatlas.cn/",
+    "lastUpdate": "2023-02-27T23:05:48.796872Z",
+    "name": "MHA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1111/andr.13402",
+            "metadata": {
+                "abstract": "© 2023 American Society of Andrology and European Academy of Andrology.Background: The development of single-cell sequencing technology has expanded the understanding of cell heterogeneity and disease progression in the male genitourinary system. However, complex processing and unprofessional analytical annotations limit the daily use and widely sharing of published datasets. Objectives: Single-cell sequencing data of male-specific tissues and organs. Materials and methods: The data were downloaded from published studies and were processed based on the Seurat R package, including quality control, cell clustering, reduction and graph generation, and cell type annotation were differentiated by referring to the related paper or recognized cell markers. Input and visual results output through the Shiny package, which was loaded into the remote server. Results: The current version of the Male Health Atlas database includes two species (human and mouse), five male-specific tissues and organs (testis, epididymis, vas deferens, corpus cavernosum, and prostate), and eight major cell types, with a total of 57 samples and 258,428 single-cell profiles. The results were divided into two main parts: Cell Clustering and Gene Display. In Cell Clustering section, visitors are free to change cell dimensionality reduction (t-distributed stochastic neighbor embedding, or uniform manifold approximation and projection), color palette, and annotation (cell type or sample type). The Gene Display section includes a reduced dimension scatter plot, violin plot, and bubble plot. Visitors can easily view the expression characteristics of single or multiple genes, and compare the expression differences between different cell types or groups. Discussion and conclusion: Male Health Atlas is the first single-cell database website in the field of andrology and male reproduction, providing researchers with single-cell sequencing resources and an accessible tool. Male Health Atlas is freely available at http://malehealthatlas.cn/.",
+                "authors": [
+                    {
+                        "name": "Dai Y."
+                    },
+                    {
+                        "name": "Li Z."
+                    },
+                    {
+                        "name": "Tang Y."
+                    },
+                    {
+                        "name": "Yao C."
+                    },
+                    {
+                        "name": "Zhao L."
+                    },
+                    {
+                        "name": "Zhao Y."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Andrology",
+                "title": "MHA, an interactive website for scRNA-seq data of male genitourinary development and disease"
+            },
+            "pmid": "36710661"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Reproductive health",
+            "uri": "http://edamontology.org/topic_3420"
+        },
+        {
+            "term": "Urology and nephrology",
+            "uri": "http://edamontology.org/topic_3422"
+        }
+    ]
+}
diff --git a/data/mhc_motif_atlas/mhc_motif_atlas.biotools.json b/data/mhc_motif_atlas/mhc_motif_atlas.biotools.json
new file mode 100644
index 0000000000000..0cb5291950c56
--- /dev/null
+++ b/data/mhc_motif_atlas/mhc_motif_atlas.biotools.json
@@ -0,0 +1,113 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-08T00:03:44.043767Z",
+    "biotoolsCURIE": "biotools:mhc_motif_atlas",
+    "biotoolsID": "mhc_motif_atlas",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "david.gfeller@unil.ch",
+            "name": "David Gfeller",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Julien Racle"
+        },
+        {
+            "name": "Simon Eggenschwiler"
+        },
+        {
+            "name": "Daniel M Tadros",
+            "orcidid": "https://orcid.org/0000-0002-1271-6941"
+        }
+    ],
+    "description": "A database of MHC binding specificities and ligands.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deisotoping",
+                    "uri": "http://edamontology.org/operation_3629"
+                },
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://mhcmotifatlas.org/",
+    "lastUpdate": "2023-02-08T00:03:44.046225Z",
+    "name": "MHC Motif Atlas",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC965",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.The highly polymorphic Major Histocompatibility Complex (MHC) genes are responsible for the binding and cell surface presentation of pathogen or cancer specific T-cell epitopes. This process is fundamental for eliciting T-cell recognition of infected or malignant cells. Epitopes displayed on MHC molecules further provide therapeutic targets for personalized cancer vaccines or adoptive T-cell therapy. To help visualizing, analyzing and comparing the different binding specificities of MHC molecules, we developed the MHC Motif Atlas (http://mhcmotifatlas.org/). This database contains information about thousands of class I and class II MHC molecules, including binding motifs, peptide length distributions, motifs of phosphorylated ligands, multiple specificities or links to X-ray crystallography structures. The database further enables users to download curated datasets of MHC ligands. By combining intuitive visualization of the main binding properties of MHC molecules together with access to more than a million ligands, the MHC Motif Atlas provides a central resource to analyze and interpret the binding specificities of MHC molecules.",
+                "authors": [
+                    {
+                        "name": "Eggenschwiler S."
+                    },
+                    {
+                        "name": "Gfeller D."
+                    },
+                    {
+                        "name": "Racle J."
+                    },
+                    {
+                        "name": "Tadros D.M."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "The MHC Motif Atlas: a database of MHC binding specificities and ligands"
+            },
+            "pmcid": "PMC9825574",
+            "pmid": "36318236"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "X-ray diffraction",
+            "uri": "http://edamontology.org/topic_2828"
+        }
+    ]
+}
diff --git a/data/microbeseg/microbeseg.biotools.json b/data/microbeseg/microbeseg.biotools.json
new file mode 100644
index 0000000000000..ca9ab50fcee61
--- /dev/null
+++ b/data/microbeseg/microbeseg.biotools.json
@@ -0,0 +1,126 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-23T09:23:45.902940Z",
+    "biotoolsCURIE": "biotools:microbeseg",
+    "biotoolsID": "microbeseg",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ralf.mikut@kit.edu",
+            "name": "Ralf Mikut",
+            "orcidid": "http://orcid.org/0000-0001-9100-5496",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Johannes Seiffarth",
+            "orcidid": "http://orcid.org/0000-0002-2087-9847"
+        },
+        {
+            "name": "Katharina Nöh",
+            "orcidid": "http://orcid.org/0000-0002-5407-2275"
+        },
+        {
+            "name": "Tim Scherr",
+            "orcidid": "http://orcid.org/0000-0001-8755-2825"
+        }
+    ],
+    "description": "Accurate Cell Segmentation with OMERO Data Management.Wan-Microbi is used.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Image annotation",
+                    "uri": "http://edamontology.org/operation_3553"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/hip-satomi/microbeSEG",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-23T09:23:45.905435Z",
+    "license": "MIT",
+    "name": "microbeSEG",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/journal.pone.0277601",
+            "metadata": {
+                "abstract": "Copyright: © 2022 Scherr et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.",
+                "authors": [
+                    {
+                        "name": "Kohlheyer D."
+                    },
+                    {
+                        "name": "Mikut R."
+                    },
+                    {
+                        "name": "Neumann O."
+                    },
+                    {
+                        "name": "Noh K."
+                    },
+                    {
+                        "name": "Scharr H."
+                    },
+                    {
+                        "name": "Scherr T."
+                    },
+                    {
+                        "name": "Schilling M.P."
+                    },
+                    {
+                        "name": "Seiffarth J."
+                    },
+                    {
+                        "name": "Wollenhaupt B."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation"
+            },
+            "pmcid": "PMC9707790",
+            "pmid": "36445903"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biotechnology",
+            "uri": "http://edamontology.org/topic_3297"
+        },
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/microbiome_toolbox/microbiome_toolbox.biotools.json b/data/microbiome_toolbox/microbiome_toolbox.biotools.json
new file mode 100644
index 0000000000000..e8ff0e871b194
--- /dev/null
+++ b/data/microbiome_toolbox/microbiome_toolbox.biotools.json
@@ -0,0 +1,104 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-22T09:25:35.219704Z",
+    "biotoolsCURIE": "biotools:microbiome_toolbox",
+    "biotoolsID": "microbiome_toolbox",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ShaillayKumar.Dogra@rd.nestle.com",
+            "name": "Shaillay Kumar Dogra",
+            "orcidid": "https://orcid.org/0000-0002-2987-4313",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jelena Banjac",
+            "orcidid": "https://orcid.org/0000-0001-7373-4150"
+        },
+        {
+            "name": "Norbert Sprenger",
+            "orcidid": "https://orcid.org/0000-0003-4880-2750"
+        }
+    ],
+    "description": "Methodological approaches to derive and visualize microbiome trajectories.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                },
+                {
+                    "term": "Trajectory visualization",
+                    "uri": "http://edamontology.org/operation_3890"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://microbiome-toolbox.azurewebsites.net",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-22T09:25:35.222194Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/JelenaBanjac/microbiome-toolbox"
+        }
+    ],
+    "name": "Microbiome Toolbox",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC781",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The gut microbiome changes rapidly under the influence of different factors such as age, dietary changes or medications to name just a few. To analyze and understand such changes, we present a Microbiome Toolbox. We implemented several methods for analysis and exploration to provide interactive visualizations for easy comprehension and reporting of longitudinal microbiome data. RESULTS: Based on the abundance of microbiome features such as taxa as well as functional capacity modules, and with the corresponding metadata per sample, the Microbiome Toolbox includes methods for (i) data analysis and exploration, (ii) data preparation including dataset-specific preprocessing and transformation, (iii) best feature selection for log-ratio denominators, (iv) two-group analysis, (v) microbiome trajectory prediction with feature importance over time, (vi) spline and linear regression statistical analysis for testing universality across different groups and differentiation of two trajectories, (vii) longitudinal anomaly detection on the microbiome trajectory and (viii) simulated intervention to return anomaly back to a reference trajectory. AVAILABILITY AND IMPLEMENTATION: The software tools are open source and implemented in Python. For developers interested in additional functionality of the Microbiome Toolbox, it is modular allowing for further extension with custom methods and analysis. The code, python package and the link to the interactive dashboard of Microbiome Toolbox are available on GitHub https://github.com/JelenaBanjac/microbiome-toolbox. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Banjac J."
+                    },
+                    {
+                        "name": "Dogra S.K."
+                    },
+                    {
+                        "name": "Sprenger N."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Microbiome Toolbox: methodological approaches to derive and visualize microbiome trajectories"
+            },
+            "pmcid": "PMC9825749",
+            "pmid": "36469345"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        }
+    ]
+}
diff --git a/data/midas2/midas2.biotools.json b/data/midas2/midas2.biotools.json
new file mode 100644
index 0000000000000..eedb8c867938b
--- /dev/null
+++ b/data/midas2/midas2.biotools.json
@@ -0,0 +1,102 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T13:04:15.828043Z",
+    "biotoolsCURIE": "biotools:midas2",
+    "biotoolsID": "midas2",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "katherine.pollard@gladstone.ucsf.edu",
+            "name": "Katherine S. Pollard",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Boris Dimitrov"
+        },
+        {
+            "name": "Chunyu Zhao"
+        },
+        {
+            "name": "Miriam Goldman"
+        },
+        {
+            "name": "Stephen Nayfach",
+            "orcidid": "http://orcid.org/0000-0003-4625-4164"
+        }
+    ],
+    "description": "Metagenomic Intra-species Diversity Analysis System.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://midas2.readthedocs.io/en/latest/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Sample comparison",
+                    "uri": "http://edamontology.org/operation_3731"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/czbiohub/MIDAS2",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T13:04:15.830623Z",
+    "license": "MIT",
+    "name": "MIDAS2",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1101/2022.06.16.496510"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Copy number variation",
+            "uri": "http://edamontology.org/topic_3958"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Metagenomic sequencing",
+            "uri": "http://edamontology.org/topic_3837"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/mineprot/mineprot.biotools.json b/data/mineprot/mineprot.biotools.json
index e24f950ed8573..9863a5a09b198 100644
--- a/data/mineprot/mineprot.biotools.json
+++ b/data/mineprot/mineprot.biotools.json
@@ -15,10 +15,15 @@
         "type": "private"
     },
     "homepage": "https://github.com/huiwenke/MineProt",
-    "lastUpdate": "2022-11-29T00:20:37.613034Z",
+    "lastUpdate": "2023-01-02T02:02:59.046480Z",
     "name": "MineProt",
     "owner": "huiwenke",
+    "publication": [
+        {
+            "doi": "10.48550/arXiv.2212.07809"
+        }
+    ],
     "version": [
-        "0.2.2"
+        "0.2.3"
     ]
 }
diff --git a/data/mirbind/mirbind.biotools.json b/data/mirbind/mirbind.biotools.json
new file mode 100644
index 0000000000000..b4e8610952421
--- /dev/null
+++ b/data/mirbind/mirbind.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-22T09:19:21.064918Z",
+    "biotoolsCURIE": "biotools:mirbind",
+    "biotoolsID": "mirbind",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "igiassa@mail.muni.cz",
+            "name": "Ilektra-Chara Giassa",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Eva Klimentová"
+        },
+        {
+            "name": "Panagiotis Alexiou"
+        },
+        {
+            "name": "Katarína Grešová",
+            "orcidid": "https://orcid.org/0000-0002-1136-0832"
+        }
+    ],
+    "description": "A Deep Learning Method for miRNA Binding Classification.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "RNA sequence",
+                        "uri": "http://edamontology.org/data_3495"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Binding site prediction",
+                    "uri": "http://edamontology.org/operation_2575"
+                },
+                {
+                    "term": "RNA secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0278"
+                },
+                {
+                    "term": "miRNA target prediction",
+                    "uri": "http://edamontology.org/operation_0463"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ml-bioinfo-ceitec.github.io/miRBind/",
+    "lastUpdate": "2023-02-22T09:19:21.067435Z",
+    "name": "miRBind",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/GENES13122323",
+            "metadata": {
+                "abstract": "© 2022 by the authors.The binding of microRNAs (miRNAs) to their target sites is a complex process, mediated by the Argonaute (Ago) family of proteins. The prediction of miRNA:target site binding is an important first step for any miRNA target prediction algorithm. To date, the potential for miRNA:target site binding is evaluated using either co-folding free energy measures or heuristic approaches, based on the identification of binding ‘seeds’, i.e., continuous stretches of binding corresponding to specific parts of the miRNA. The limitations of both these families of methods have produced generations of miRNA target prediction algorithms that are primarily focused on ‘canonical’ seed targets, even though unbiased experimental methods have shown that only approximately half of in vivo miRNA targets are ‘canonical’. Herein, we present miRBind, a deep learning method and web server that can be used to accurately predict the potential of miRNA:target site binding. We trained our method using seed-agnostic experimental data and show that our method outperforms both seed-based approaches and co-fold free energy approaches. The full code for the development of miRBind and a freely accessible web server are freely available.",
+                "authors": [
+                    {
+                        "name": "Alexiou P."
+                    },
+                    {
+                        "name": "Giassa I.-C."
+                    },
+                    {
+                        "name": "Gresova K."
+                    },
+                    {
+                        "name": "Hejret V."
+                    },
+                    {
+                        "name": "Klimentova E."
+                    },
+                    {
+                        "name": "Krcmar J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Genes",
+                "title": "miRBind: A Deep Learning Method for miRNA Binding Classification"
+            },
+            "pmcid": "PMC9777820",
+            "pmid": "36553590"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein binding sites",
+            "uri": "http://edamontology.org/topic_3534"
+        },
+        {
+            "term": "RNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3794"
+        }
+    ]
+}
diff --git a/data/mirdip/mirdip.biotools.json b/data/mirdip/mirdip.biotools.json
new file mode 100644
index 0000000000000..110166ad5d4a5
--- /dev/null
+++ b/data/mirdip/mirdip.biotools.json
@@ -0,0 +1,150 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-22T00:28:29.278828Z",
+    "biotoolsCURIE": "biotools:mirdip",
+    "biotoolsID": "mirdip",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "juris@ai.utoronto.ca",
+            "name": "Igor Jurisica",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Anne-Christin Hauschild"
+        },
+        {
+            "name": "Richard Lu"
+        },
+        {
+            "name": "Chiara Pastrello",
+            "orcidid": "https://orcid.org/0000-0002-1934-7472"
+        }
+    ],
+    "description": "Tissue context annotation and novel microRNA curation.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene symbol",
+                        "uri": "http://edamontology.org/data_1026"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "miRNA expression analysis",
+                    "uri": "http://edamontology.org/operation_3792"
+                },
+                {
+                    "term": "miRNA target prediction",
+                    "uri": "http://edamontology.org/operation_0463"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ophid.utoronto.ca/mirDIP",
+    "lastUpdate": "2023-02-22T00:28:29.282125Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/ijlab/mirdip"
+        }
+    ],
+    "name": "mirDIP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1070",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.MirDIP is a well-established database that aggregates microRNA-gene human interactions from multiple databases to increase coverage, reduce bias, and improve usability by providing an integrated score proportional to the probability of the interaction occurring. In version 5.2, we removed eight outdated resources, added a new resource (miRNATIP), and ran five prediction algorithms for miRBase and mirGeneDB. In total, mirDIP 5.2 includes 46 364 047 predictions for 27 936 genes and 2734 microRNAs, making it the first database to provide interactions using data from mirGeneDB. Moreover, we curated and integrated 32 497 novel microRNAs from 14 publications to accelerate the use of these novel data. In this release, we also extend the content and functionality of mirDIP by associating contexts with microRNAs, genes, and microRNA-gene interactions. We collected and processed microRNA and gene expression data from 20 resources and acquired information on 330 tissue and disease contexts for 2657 microRNAs, 27 576 genes and 123 651 910 gene-microRNA-tissue interactions. Finally, we improved the usability of mirDIP by enabling the user to search the database using precursor IDs, and we integrated miRAnno, a network-based tool for identifying pathways linked to specific microRNAs. We also provide a mirDIP API to facilitate access to its integrated predictions. Updated mirDIP is available at https://ophid.utoronto.ca/mirDIP.",
+                "authors": [
+                    {
+                        "name": "Abovsky M."
+                    },
+                    {
+                        "name": "Ahmed Z."
+                    },
+                    {
+                        "name": "Bethune-Waddell D."
+                    },
+                    {
+                        "name": "Ekaputeri G.K.A."
+                    },
+                    {
+                        "name": "Hauschild A.-C."
+                    },
+                    {
+                        "name": "Jurisica I."
+                    },
+                    {
+                        "name": "Kotlyar M."
+                    },
+                    {
+                        "name": "Lu R."
+                    },
+                    {
+                        "name": "Pastrello C."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "MirDIP 5.2: tissue context annotation and novel microRNA curation"
+            },
+            "pmcid": "PMC9825511",
+            "pmid": "36453996"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ],
+    "version": [
+        "5.2"
+    ]
+}
diff --git a/data/mlago/mlago.biotools.json b/data/mlago/mlago.biotools.json
new file mode 100644
index 0000000000000..ca805be0d5db0
--- /dev/null
+++ b/data/mlago/mlago.biotools.json
@@ -0,0 +1,127 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-07T23:51:52.854041Z",
+    "biotoolsCURIE": "biotools:mlago",
+    "biotoolsID": "mlago",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "kmaeda@bio.kyutech.ac.jp",
+            "name": "Kazuhiro Maeda",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Aoi Hatae"
+        },
+        {
+            "name": "Fred C. Boogerd"
+        },
+        {
+            "name": "Hiroyuki Kurata"
+        },
+        {
+            "name": "Yukie Sakai"
+        }
+    ],
+    "description": "Machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Enzyme kinetics calculation",
+                    "uri": "http://edamontology.org/operation_0334"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps",
+    "language": [
+        "C",
+        "MATLAB",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-07T23:51:52.857355Z",
+    "license": "BSD-2-Clause",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/kmaeda16/MLAGO-data"
+        }
+    ],
+    "name": "MLAGO",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05009-X",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Kinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (Km), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem). Results: To solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for Km estimation of kinetic modeling. First, we use a machine learning-based Km predictor based only on three factors: EC number, KEGG Compound ID, and Organism ID, then conduct a constrained global optimization-based parameter estimation by using the machine learning-predicted Km values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R2 = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping Km values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated Km values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated Km values, which were close to the measured values. Conclusions: MLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based Km predictor is accessible at https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps, which helps modelers perform MLAGO on their own parameter estimation tasks.",
+                "authors": [
+                    {
+                        "name": "Boogerd F.C."
+                    },
+                    {
+                        "name": "Hatae A."
+                    },
+                    {
+                        "name": "Kurata H."
+                    },
+                    {
+                        "name": "Maeda K."
+                    },
+                    {
+                        "name": "Sakai Y."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling"
+            },
+            "pmcid": "PMC9624028",
+            "pmid": "36319952"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Enzymes",
+            "uri": "http://edamontology.org/topic_0821"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Systems biology",
+            "uri": "http://edamontology.org/topic_2259"
+        }
+    ]
+}
diff --git a/data/mobidb/mobidb.biotools.json b/data/mobidb/mobidb.biotools.json
index 2f6f913f67535..b1e06c50202ea 100644
--- a/data/mobidb/mobidb.biotools.json
+++ b/data/mobidb/mobidb.biotools.json
@@ -133,7 +133,7 @@
         }
     ],
     "homepage": "https://mobidb.org/",
-    "lastUpdate": "2021-07-05T12:13:15Z",
+    "lastUpdate": "2023-02-27T13:45:56.818258Z",
     "license": "CC-BY-4.0",
     "maturity": "Mature",
     "name": "MobiDB",
@@ -201,7 +201,7 @@
                         "name": "Vranken W.F."
                     }
                 ],
-                "citationCount": 3,
+                "citationCount": 84,
                 "date": "2021-01-08T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "MobiDB: Intrinsically disordered proteins in 2021"
@@ -231,7 +231,7 @@
                         "name": "Walsh I."
                     }
                 ],
-                "citationCount": 110,
+                "citationCount": 120,
                 "date": "2012-08-01T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "MobiDB: A comprehensive database of intrinsic protein disorder annotations"
@@ -260,7 +260,7 @@
                         "name": "Walsh I."
                     }
                 ],
-                "citationCount": 139,
+                "citationCount": 156,
                 "date": "2015-01-28T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "MobiDB 2.0: An improved database of intrinsically disordered and mobile proteins"
@@ -328,7 +328,7 @@
                         "name": "Vranken W.F."
                     }
                 ],
-                "citationCount": 109,
+                "citationCount": 139,
                 "date": "2018-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "MobiDB 3.0: More annotations for intrinsic disorder, conformational diversity and interactions in proteins"
@@ -357,6 +357,6 @@
     ],
     "validated": 1,
     "version": [
-        "4"
+        "5"
     ]
 }
diff --git a/data/modelarchive/modelarchive.biotools.json b/data/modelarchive/modelarchive.biotools.json
new file mode 100644
index 0000000000000..c20a32f099081
--- /dev/null
+++ b/data/modelarchive/modelarchive.biotools.json
@@ -0,0 +1,159 @@
+{
+    "additionDate": "2023-02-07T09:14:55.748977Z",
+    "biotoolsCURIE": "biotools:modelarchive",
+    "biotoolsID": "modelarchive",
+    "credit": [
+        {
+            "name": "SIB Swiss Institute of Bioinformatics",
+            "typeEntity": "Institute"
+        }
+    ],
+    "description": "ModelArchive is the archive for structural models which are not based on experimental data and complements the PDB archive for experimental structures and PDB-Dev for integrative structures. Any type of macromolecular structure which would otherwise be suitable for the PDB but whose coordinates are not based on experimental data can be deposited in ModelArchive. This includes single chains or complexes consisting of proteins, RNA, DNA, or carbohydrates including small molecules bound to them. The modelling methods can be pure in silico predictions as found in de novo models or based on experimental structures such as homology models or modified structures including docked ligands, modelled variants, post-translational modifications (e.g. glycosylated structures), etc. The main purpose of a deposited model is to supplement a manuscript for which the model was generated and to make the model accessible to the interested reader.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://www.modelarchive.org/help"
+        }
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+            "gerardo.tauriello"
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+        "Switzerland"
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+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.modelarchive.org/",
+    "lastUpdate": "2023-02-07T09:14:55.757356Z",
+    "name": "ModelArchive",
+    "owner": "sduvaud",
+    "publication": [
+        {
+            "doi": "10.1016/j.str.2008.12.014",
+            "metadata": {
+                "abstract": "We describe the proceedings and conclusions from the \"Workshop on Applications of Protein Models in Biomedical Research\" (the Workshop) that was held at the University of California, San Francisco on 11 and 12 July, 2008. At the Workshop, international scientists involved with structure modeling explored (i) how models are currently used in biomedical research, (ii) the requirements and challenges for different applications, and (iii) how the interaction between the computational and experimental research communities could be strengthened to advance the field.",
+                "authors": [
+                    {
+                        "name": "Berman H.M."
+                    },
+                    {
+                        "name": "Brenner S.E."
+                    },
+                    {
+                        "name": "Burley S.K."
+                    },
+                    {
+                        "name": "Das R."
+                    },
+                    {
+                        "name": "Dokholyan N.V."
+                    },
+                    {
+                        "name": "Dunbrack Jr. R.L."
+                    },
+                    {
+                        "name": "Fidelis K."
+                    },
+                    {
+                        "name": "Fiser A."
+                    },
+                    {
+                        "name": "Godzik A."
+                    },
+                    {
+                        "name": "Honig B."
+                    },
+                    {
+                        "name": "Huang Y.J."
+                    },
+                    {
+                        "name": "Humblet C."
+                    },
+                    {
+                        "name": "Jacobson M.P."
+                    },
+                    {
+                        "name": "Joachimiak A."
+                    },
+                    {
+                        "name": "Jones D."
+                    },
+                    {
+                        "name": "Kortemme T."
+                    },
+                    {
+                        "name": "Kryshtafovych A."
+                    },
+                    {
+                        "name": "Krystek Jr. S.R."
+                    },
+                    {
+                        "name": "Levitt M."
+                    },
+                    {
+                        "name": "Montelione G.T."
+                    },
+                    {
+                        "name": "Moult J."
+                    },
+                    {
+                        "name": "Murray D."
+                    },
+                    {
+                        "name": "Sali A."
+                    },
+                    {
+                        "name": "Sanchez R."
+                    },
+                    {
+                        "name": "Schwede T."
+                    },
+                    {
+                        "name": "Sosnick T.R."
+                    },
+                    {
+                        "name": "Standley D.M."
+                    },
+                    {
+                        "name": "Stouch T."
+                    },
+                    {
+                        "name": "Vajda S."
+                    },
+                    {
+                        "name": "Vasquez M."
+                    },
+                    {
+                        "name": "Westbrook J.D."
+                    },
+                    {
+                        "name": "Wilson I.A."
+                    }
+                ],
+                "citationCount": 105,
+                "date": "2009-02-13T00:00:00Z",
+                "journal": "Structure",
+                "title": "Outcome of a Workshop on Applications of Protein Models in Biomedical Research"
+            }
+        }
+    ],
+    "topic": [
+        {
+            "term": "Structural biology",
+            "uri": "http://edamontology.org/topic_1317"
+        }
+    ]
+}
diff --git a/data/modle/modle.biotools.json b/data/modle/modle.biotools.json
new file mode 100644
index 0000000000000..4ed3504a3bdab
--- /dev/null
+++ b/data/modle/modle.biotools.json
@@ -0,0 +1,132 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-22T00:19:30.432065Z",
+    "biotoolsCURIE": "biotools:modle",
+    "biotoolsID": "modle",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jonas.paulsen@ibv.uio.no",
+            "name": "Jonas Paulsen",
+            "orcidid": "https://orcid.org/0000-0002-7918-5495",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Anthony Mathelier"
+        },
+        {
+            "name": "Roberto Rossini"
+        },
+        {
+            "name": "Torbjørn Rognes"
+        },
+        {
+            "name": "Vipin Kumar"
+        }
+    ],
+    "description": "MoDLE is a computational tool for fast, stochastic modeling of molecular contacts from DNA loop extrusion capable of simulating realistic contact patterns genome wide in a few minutes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Loop modelling",
+                    "uri": "http://edamontology.org/operation_0481"
+                },
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/paulsengroup/modle",
+    "language": [
+        "C++",
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-22T00:19:30.434529Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/paulsengroup/2021-modle-paper-001-data-analysis"
+        }
+    ],
+    "name": "MoDLE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13059-022-02815-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).DNA loop extrusion emerges as a key process establishing genome structure and function. We introduce MoDLE, a computational tool for fast, stochastic modeling of molecular contacts from DNA loop extrusion capable of simulating realistic contact patterns genome wide in a few minutes. MoDLE accurately simulates contact maps in concordance with existing molecular dynamics approaches and with Micro-C data and does so orders of magnitude faster than existing approaches. MoDLE runs efficiently on machines ranging from laptops to high performance computing clusters and opens up for exploratory and predictive modeling of 3D genome structure in a wide range of settings.",
+                "authors": [
+                    {
+                        "name": "Kumar V."
+                    },
+                    {
+                        "name": "Mathelier A."
+                    },
+                    {
+                        "name": "Paulsen J."
+                    },
+                    {
+                        "name": "Rognes T."
+                    },
+                    {
+                        "name": "Rossini R."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Genome Biology",
+                "title": "MoDLE: high-performance stochastic modeling of DNA loop extrusion interactions"
+            },
+            "pmcid": "PMC9710047",
+            "pmid": "36451166"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Molecular dynamics",
+            "uri": "http://edamontology.org/topic_0176"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/moleculeace/moleculeace.biotools.json b/data/moleculeace/moleculeace.biotools.json
new file mode 100644
index 0000000000000..66fb63965d9be
--- /dev/null
+++ b/data/moleculeace/moleculeace.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-22T00:11:21.231324Z",
+    "biotoolsCURIE": "biotools:moleculeace",
+    "biotoolsID": "moleculeace",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "f.grisoni@tue.nl",
+            "name": "Francesca Grisoni",
+            "orcidid": "https://orcid.org/0000-0001-8552-6615",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alisa Alenicheva"
+        },
+        {
+            "name": "Derek van Tilborg"
+        }
+    ],
+    "description": "Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/molML/MoleculeACE",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-02-22T00:11:21.233757Z",
+    "license": "MIT",
+    "name": "MoleculeACE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCIM.2C01073",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society. All rights reserved.Machine learning has become a crucial tool in drug discovery and chemistry at large, e.g., to predict molecular properties, such as bioactivity, with high accuracy. However, activity cliffs-pairs of molecules that are highly similar in their structure but exhibit large differences in potency-have received limited attention for their effect on model performance. Not only are these edge cases informative for molecule discovery and optimization but also models that are well equipped to accurately predict the potency of activity cliffs have increased potential for prospective applications. Our work aims to fill the current knowledge gap on best-practice machine learning methods in the presence of activity cliffs. We benchmarked a total of 24 machine and deep learning approaches on curated bioactivity data from 30 macromolecular targets for their performance on activity cliff compounds. While all methods struggled in the presence of activity cliffs, machine learning approaches based on molecular descriptors outperformed more complex deep learning methods. Our findings highlight large case-by-case differences in performance, advocating for (a) the inclusion of dedicated \"activity-cliff-centered\" metrics during model development and evaluation and (b) the development of novel algorithms to better predict the properties of activity cliffs. To this end, the methods, metrics, and results of this study have been encapsulated into an open-access benchmarking platform named MoleculeACE (Activity Cliff Estimation, available on GitHub at: https://github.com/molML/MoleculeACE). MoleculeACE is designed to steer the community toward addressing the pressing but overlooked limitation of molecular machine learning models posed by activity cliffs.",
+                "authors": [
+                    {
+                        "name": "Alenicheva A."
+                    },
+                    {
+                        "name": "Grisoni F."
+                    },
+                    {
+                        "name": "Van Tilborg D."
+                    }
+                ],
+                "date": "2022-12-12T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "Exposing the Limitations of Molecular Machine Learning with Activity Cliffs"
+            },
+            "pmcid": "PMC9749029",
+            "pmid": "36456532"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Structural variation",
+            "uri": "http://edamontology.org/topic_3175"
+        }
+    ]
+}
diff --git a/data/mop2/mop2.biotools.json b/data/mop2/mop2.biotools.json
new file mode 100644
index 0000000000000..a2682e1db0f7d
--- /dev/null
+++ b/data/mop2/mop2.biotools.json
@@ -0,0 +1,128 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T23:59:45.965442Z",
+    "biotoolsCURIE": "biotools:mop2",
+    "biotoolsID": "mop2",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "eva.novoa@crg.eu",
+            "name": "Eva Maria Novoa",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "julia.ponomarenko@crg.eu",
+            "name": "Julia Ponomarenko",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Anna Delgado-Tejedor"
+        },
+        {
+            "name": "Luca Cozzuto"
+        },
+        {
+            "name": "Toni Hermoso Pulido"
+        }
+    ],
+    "description": "Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://biocorecrg.github.io/MOP2/docs/about.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Base-calling",
+                    "uri": "http://edamontology.org/operation_3185"
+                },
+                {
+                    "term": "Demultiplexing",
+                    "uri": "http://edamontology.org/operation_3933"
+                },
+                {
+                    "term": "Transcriptome assembly",
+                    "uri": "http://edamontology.org/operation_3258"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/biocorecrg/MOP2",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-03-18T23:59:45.971718Z",
+    "license": "MIT",
+    "name": "MoP2",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1007/978-1-0716-2962-8_13",
+            "metadata": {
+                "abstract": "This chapter describes MasterOfPores v.2 (MoP2), an open-source suite of pipelines for processing and analyzing direct RNA Oxford Nanopore sequencing data. The MoP2 relies on the Nextflow DSL2 framework and Linux containers, thus enabling reproducible data analysis in transcriptomic and epitranscriptomic studies. We introduce the key concepts of MoP2 and provide a step-by-step fully reproducible and complete example of how to use the workflow for the analysis of S. cerevisiae total RNA samples sequenced using MinION flowcells. The workflow starts with the pre-processing of raw FAST5 files, which includes basecalling, read quality control, demultiplexing, filtering, mapping, estimation of per-gene/transcript abundances, and transcriptome assembly, with support of the GPU computing for the basecalling and read demultiplexing steps. The secondary analyses of the workflow focus on the estimation of RNA poly(A) tail lengths and the identification of RNA modifications. The MoP2 code is available at https://github.com/biocorecrg/MOP2 and is distributed under the MIT license.",
+                "authors": [
+                    {
+                        "name": "Cozzuto L."
+                    },
+                    {
+                        "name": "Delgado-Tejedor A."
+                    },
+                    {
+                        "name": "Hermoso Pulido T."
+                    },
+                    {
+                        "name": "Novoa E.M."
+                    },
+                    {
+                        "name": "Ponomarenko J."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Methods in molecular biology (Clifton, N.J.)",
+                "title": "Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores"
+            },
+            "pmid": "36723817"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
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new file mode 100644
index 0000000000000..509b47e812d19
--- /dev/null
+++ b/data/mopower/mopower.biotools.json
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+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T00:52:21.692941Z",
+    "biotoolsCURIE": "biotools:mopower",
+    "biotoolsID": "mopower",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hsyed@ku.edu.tr",
+            "name": "Hamzah Syed",
+            "orcidid": "http://orcid.org/0000-0001-6981-6962",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Chiara Bacchelli"
+        },
+        {
+            "name": "Daniel Kelberman"
+        },
+        {
+            "name": "Georg W Otto"
+        },
+        {
+            "name": "Philip L Beales"
+        }
+    ],
+    "description": "R-shiny application for the simulation and power calculation of multi-omics studies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://hsyed.shinyapps.io/MOPower/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-27T00:52:21.695409Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/HSyed91/MOPower"
+        }
+    ],
+    "name": "MOPower",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1101/2021.12.19.473339"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        }
+    ]
+}
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new file mode 100644
index 0000000000000..de991cf0b7e26
--- /dev/null
+++ b/data/mosaics_software/mosaics_software.biotools.json
@@ -0,0 +1,90 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-04T01:56:20.706779Z",
+    "biotoolsCURIE": "biotools:mosaics_software",
+    "biotoolsID": "mosaics_software",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jose.faraldo@nih.gov",
+            "name": "José D. Faraldo-Gómez"
+        },
+        {
+            "email": "nathan.bernhardt@nih.gov",
+            "name": "Nathan Bernhardt"
+        }
+    ],
+    "description": "A software suite for analysis of membrane structure and dynamics in simulated trajectories.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Simulation analysis",
+                    "uri": "http://edamontology.org/operation_0244"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MOSAICS-NIH/MOSAICS",
+    "language": [
+        "C++"
+    ],
+    "lastUpdate": "2023-02-04T01:56:20.709247Z",
+    "license": "BSD-3-Clause",
+    "name": "MOSAICS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.BPJ.2022.11.005",
+            "metadata": {
+                "abstract": "© 2022Molecular dynamics (MD) simulations have become the predominant computational analysis method in membrane biophysics, as this technique is uniquely suited for investigations of complex molecular systems through the relevant physical principles. Owing to continued improvements in scope and performance, the trajectories generated through this approach contain ever-increasing amounts of information, which must be synthesized and simplified in post-analysis using tools that are not only mechanistically insightful but also computationally efficient and highly scalable. Here, we introduce MOSAICS, a self-contained high-performance suite of C++ software tools designed for advanced analyses of lipid bilayer structure and dynamics from MD trajectories. MOSAICS is to our knowledge the most comprehensive software suite of this kind, enabling analysis of a wide array of morphological and kinetic properties, for both simple and complex membranes, irrespective of system size or resolution. Importantly, MOSAICS is designed to provide spatial distributions of all computed quantities, with built-in masking tools, noise filtering, and statistical significance metrics to facilitate quantitative interpretations of the trajectory data; it is also fully parallelized and can therefore leverage the capabilities of supercomputing facilities. Despite its technical sophistication, MOSAICS is user-friendly and requires minimal computational expertise, making it accessible to researchers of all skill levels. This sofware suite can be freely downloaded at https://github.com/MOSAICS-NIH/.",
+                "authors": [
+                    {
+                        "name": "Bernhardt N."
+                    },
+                    {
+                        "name": "Faraldo-Gomez J.D."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Biophysical Journal",
+                "title": "MOSAICS: A software suite for analysis of membrane structure and dynamics in simulated trajectories"
+            },
+            "pmid": "36333911"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biophysics",
+            "uri": "http://edamontology.org/topic_3306"
+        },
+        {
+            "term": "Lipids",
+            "uri": "http://edamontology.org/topic_0153"
+        },
+        {
+            "term": "Molecular dynamics",
+            "uri": "http://edamontology.org/topic_0176"
+        }
+    ]
+}
diff --git a/data/mosdef-gomc/mosdef-gomc.biotools.json b/data/mosdef-gomc/mosdef-gomc.biotools.json
new file mode 100644
index 0000000000000..aea35a4fa5169
--- /dev/null
+++ b/data/mosdef-gomc/mosdef-gomc.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T23:55:48.205570Z",
+    "biotoolsCURIE": "biotools:mosdef-gomc",
+    "biotoolsID": "mosdef-gomc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Peter T. Cummings"
+        },
+        {
+            "name": "Umesh Timalsina"
+        },
+        {
+            "name": "Brad Crawford",
+            "orcidid": "https://orcid.org/0000-0003-0638-7333"
+        },
+        {
+            "name": "Jeffrey J. Potoff",
+            "orcidid": "https://orcid.org/0000-0002-4421-8787"
+        }
+    ],
+    "description": "Python Software for the Creation of Scientific Workflows for the Monte Carlo Simulation Engine GOMC.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Simulation analysis",
+                    "uri": "http://edamontology.org/operation_0244"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/GOMC-WSU/MoSDeF-GOMC",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T23:55:48.210073Z",
+    "license": "MIT",
+    "name": "MoSDeF-GOMC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCIM.2C01498",
+            "metadata": {
+                "abstract": "MoSDeF-GOMC is a python interface for the Monte Carlo software GOMC to the Molecular Simulation Design Framework (MoSDeF) ecosystem. MoSDeF-GOMC automates the process of generating initial coordinates, assigning force field parameters, and writing coordinate (PDB), connectivity (PSF), force field parameter, and simulation control files. The software lowers entry barriers for novice users while allowing advanced users to create complex workflows that encapsulate simulation setup, execution, and data analysis in a single script. All relevant simulation parameters are encoded within the workflow, ensuring reproducible simulations. MoSDeF-GOMC’s capabilities are illustrated through a number of examples, including prediction of the adsorption isotherm for CO2 in IRMOF-1, free energies of hydration for neon and radon over a broad temperature range, and the vapor-liquid coexistence curve of a four-component surrogate for the jet fuel S-8. The MoSDeF-GOMC software is available on GitHub at https://github.com/GOMC-WSU/MoSDeF-GOMC.",
+                "authors": [
+                    {
+                        "name": "Craven N.C."
+                    },
+                    {
+                        "name": "Crawford B."
+                    },
+                    {
+                        "name": "Cummings P.T."
+                    },
+                    {
+                        "name": "Gilmer J.B."
+                    },
+                    {
+                        "name": "McCabe C."
+                    },
+                    {
+                        "name": "Potoff J.J."
+                    },
+                    {
+                        "name": "Quach C.D."
+                    },
+                    {
+                        "name": "Timalsina U."
+                    }
+                ],
+                "date": "2023-02-27T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "MoSDeF-GOMC: Python Software for the Creation of Scientific Workflows for the Monte Carlo Simulation Engine GOMC"
+            },
+            "pmid": "36791286"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "Software engineering",
+            "uri": "http://edamontology.org/topic_3372"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/mouse-embeddings/mouse-embeddings.biotools.json b/data/mouse-embeddings/mouse-embeddings.biotools.json
new file mode 100644
index 0000000000000..b45e709e4b5b0
--- /dev/null
+++ b/data/mouse-embeddings/mouse-embeddings.biotools.json
@@ -0,0 +1,94 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T00:23:27.159303Z",
+    "biotoolsCURIE": "biotools:mouse-embeddings",
+    "biotoolsID": "mouse-embeddings",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "t.konopka@qmul.ac.uk",
+            "name": "Tomasz Konopka",
+            "orcidid": "https://orcid.org/0000-0003-3042-4712",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Damian Smedley"
+        },
+        {
+            "name": "Letizia Vestito"
+        }
+    ],
+    "description": "Dimensional reduction of phenotypes from 53 000 mouse models reveals a diverse landscape of gene function.",
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://zenodo.org/record/5493439"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Ontology visualisation",
+                    "uri": "http://edamontology.org/operation_3559"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/tkonopka/mouse-embeddings",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-27T00:23:27.161663Z",
+    "license": "MIT",
+    "name": "mouse-embeddings",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOADV/VBAB026",
+            "pmcid": "PMC8633315",
+            "pmid": "34870209"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/mousepost/mousepost.biotools.json b/data/mousepost/mousepost.biotools.json
new file mode 100644
index 0000000000000..2c23d18f460f0
--- /dev/null
+++ b/data/mousepost/mousepost.biotools.json
@@ -0,0 +1,96 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T23:50:14.160181Z",
+    "biotoolsCURIE": "biotools:mousepost",
+    "biotoolsID": "mousepost",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Claude.Libert@irc.vib-ugent.be",
+            "name": "Claude Libert",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jolien Vandewalle"
+        },
+        {
+            "name": "Steven Timmermans",
+            "orcidid": "https://orcid.org/0000-0002-5152-9620"
+        }
+    ],
+    "description": "Online search tool to search for variations in all protein-coding gene sequences of 36 sequenced mouse inbred strains, compared to the reference strain C57BL 6J, which could be linked to strain-specific phenotypes and modifier effects.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "Variant classification",
+                    "uri": "http://edamontology.org/operation_3225"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://mousepost.be",
+    "lastUpdate": "2023-03-18T23:50:14.165097Z",
+    "name": "Mousepost",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAD064",
+            "metadata": {
+                "abstract": "The Mousepost 1.0 online search tool, launched in 2017, allowed to search for variations in all protein-coding gene sequences of 36 sequenced mouse inbred strains, compared to the reference strain C57BL/6J, which could be linked to strain-specific phenotypes and modifier effects. Because recently these genome sequences have been significantly updated and sequences of 16 extra strains added by the Mouse Genomes Project, a profound update, correction and expansion of the Mousepost 1.0 database has been performed and is reported here. Moreover, we have added a new class of protein disturbing sequence polymorphisms (besides stop codon losses, stop codon gains, small insertions and deletions, and missense mutations), namely start codon mutations. The current version, Mousepost 2.0 (https://mousepost.be), therefore is a significantly updated and invaluable tool available to the community and is described here and foreseen by multiple examples.",
+                "authors": [
+                    {
+                        "name": "Libert C."
+                    },
+                    {
+                        "name": "Timmermans S."
+                    },
+                    {
+                        "name": "Vandewalle J."
+                    }
+                ],
+                "date": "2023-02-28T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "Mousepost 2.0, a major expansion of the resource"
+            },
+            "pmid": "36762471"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        }
+    ],
+    "version": [
+        "2.0"
+    ]
+}
diff --git a/data/mowl/mowl.biotools.json b/data/mowl/mowl.biotools.json
new file mode 100644
index 0000000000000..830a52d059245
--- /dev/null
+++ b/data/mowl/mowl.biotools.json
@@ -0,0 +1,114 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-22T00:05:08.273627Z",
+    "biotoolsCURIE": "biotools:mowl",
+    "biotoolsID": "mowl",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "robert.hoehndorf@kaust.edu.sa",
+            "name": "Robert Hoehndorf",
+            "orcidid": "https://orcid.org/0000-0001-8149-5890",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Fernando Zhapa-Camacho",
+            "orcidid": "https://orcid.org/0000-0002-0710-2259"
+        },
+        {
+            "name": "Maxat Kulmanov",
+            "orcidid": "https://orcid.org/0000-0003-1710-1820"
+        }
+    ],
+    "description": "Python library for machine learning with biomedical ontologies.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://mowl.readthedocs.io/en/latest/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Ontology comparison",
+                    "uri": "http://edamontology.org/operation_3352"
+                },
+                {
+                    "term": "Ontology visualisation",
+                    "uri": "http://edamontology.org/operation_3559"
+                },
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/bio-ontology-research-group/mowl",
+    "language": [
+        "Python",
+        "Scala"
+    ],
+    "lastUpdate": "2023-02-22T00:05:08.276068Z",
+    "license": "BSD-3-Clause",
+    "name": "mOWL",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC811",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Ontologies contain formal and structured information about a domain and are widely used in bioinformatics for annotation and integration of data. Several methods use ontologies to provide background knowledge in machine learning tasks, which is of particular importance in bioinformatics. These methods rely on a set of common primitives that are not readily available in a software library; a library providing these primitives would facilitate the use of current machine learning methods with ontologies and the development of novel methods for other ontology-based biomedical applications. RESULTS: We developed mOWL, a Python library for machine learning with ontologies formalized in the Web Ontology Language (OWL). mOWL implements ontology embedding methods that map information contained in formal knowledge bases and ontologies into vector spaces while preserving some of the properties and relations in ontologies, as well as methods to use these embeddings for similarity computation, deductive inference and zero-shot learning. We demonstrate mOWL on the knowledge-based prediction of protein-protein interactions using the gene ontology and gene-disease associations using phenotype ontologies. AVAILABILITY AND IMPLEMENTATION: mOWL is freely available on https://github.com/bio-ontology-research-group/mowl and as a Python package in PyPi. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Hoehndorf R."
+                    },
+                    {
+                        "name": "Kulmanov M."
+                    },
+                    {
+                        "name": "Zhapa-Camacho F."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "mOWL: Python library for machine learning with biomedical ontologies"
+            },
+            "pmcid": "PMC9848046",
+            "pmid": "36534832"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        }
+    ]
+}
diff --git a/data/mpass/mpass.biotools.json b/data/mpass/mpass.biotools.json
new file mode 100644
index 0000000000000..a07ff8acee52a
--- /dev/null
+++ b/data/mpass/mpass.biotools.json
@@ -0,0 +1,126 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T23:43:25.532765Z",
+    "biotoolsCURIE": "biotools:mpass",
+    "biotoolsID": "mpass",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "s-satoh@kpu.ac.jp",
+            "name": "Soichirou Satoh",
+            "orcidid": "https://orcid.org/0000-0002-7622-1868",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ayumi Tanaka"
+        },
+        {
+            "name": "Makio Yokono"
+        },
+        {
+            "name": "Rei Tanaka"
+        }
+    ],
+    "description": "Phylogeny analysis of whole protein-coding genes in metagenomic data detected an environmental gradient for the microbiota.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Expression analysis",
+                    "uri": "http://edamontology.org/operation_2495"
+                },
+                {
+                    "term": "Phylogenetic inference",
+                    "uri": "http://edamontology.org/operation_0323"
+                },
+                {
+                    "term": "Phylogenetic tree comparison",
+                    "uri": "http://edamontology.org/operation_0325"
+                },
+                {
+                    "term": "Phylogenetic tree editing",
+                    "uri": "http://edamontology.org/operation_0326"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/s0sat/MPASS",
+    "language": [
+        "Perl"
+    ],
+    "lastUpdate": "2023-03-18T23:43:25.538071Z",
+    "license": "MIT",
+    "name": "MPASS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PONE.0281288",
+            "metadata": {
+                "abstract": "Environmental factors affect the growth of microorganisms and therefore alter the composition of microbiota. Correlative analysis of the relationship between metagenomic composition and the environmental gradient can help elucidate key environmental factors and establishment principles for microbial communities. However, a reasonable method to quantitatively compare whole metagenomic data and identify the primary environmental factors for the establishment of microbiota has not been reported so far. In this study, we developed a method to compare whole proteomes deduced from metagenomic shotgun sequencing data, and quantitatively display their phylogenetic relationships as metagenomic trees. We called this method Metagenomic Phylogeny by Average Sequence Similarity (MPASS). We also compared one of the metagenomic trees with dendrograms of environmental factors using a comparison tool for phylogenetic trees. The MPASS method correctly constructed metagenomic trees of simulated metagenomes and soil and water samples. The topology of the metagenomic tree of samples from the Kirishima hot springs area in Japan was highly similarity to that of the dendrograms based on previously reported environmental factors for this area. The topology of the metagenomic tree also reflected the dynamics of microbiota at the taxonomic and functional levels. Our results strongly suggest that MPASS can successfully classify metagenomic shotgun sequencing data based on the similarity of whole protein-coding sequences, and will be useful for the identification of principal environmental factors for the establishment of microbial communities. Custom Perl script for the MPASS pipeline is available at https://github.com/s0sat/MPASS.",
+                "authors": [
+                    {
+                        "name": "Endoh D."
+                    },
+                    {
+                        "name": "Satoh S."
+                    },
+                    {
+                        "name": "Tanaka A."
+                    },
+                    {
+                        "name": "Tanaka R."
+                    },
+                    {
+                        "name": "Yabuki T."
+                    },
+                    {
+                        "name": "Yokono M."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "Phylogeny analysis of whole protein-coding genes in metagenomic data detected an environmental gradient for the microbiota"
+            },
+            "pmcid": "PMC9894459",
+            "pmid": "36730456"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Metagenomic sequencing",
+            "uri": "http://edamontology.org/topic_3837"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        },
+        {
+            "term": "Phylogeny",
+            "uri": "http://edamontology.org/topic_0084"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        }
+    ]
+}
diff --git a/data/mr-bias/mr-bias.biotools.json b/data/mr-bias/mr-bias.biotools.json
new file mode 100644
index 0000000000000..f1594e8f66274
--- /dev/null
+++ b/data/mr-bias/mr-bias.biotools.json
@@ -0,0 +1,78 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T23:40:19.239953Z",
+    "biotoolsCURIE": "biotools:mr-bias",
+    "biotoolsID": "mr-bias",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Rick Franich"
+        },
+        {
+            "name": "Zachary Chin"
+        },
+        {
+            "name": "James C Korte",
+            "orcidid": "https://orcid.org/0000-0001-9152-1319"
+        },
+        {
+            "name": "Lois Holloway",
+            "orcidid": "https://orcid.org/0000-0003-4337-2165"
+        },
+        {
+            "name": "Madeline Carr",
+            "orcidid": "https://orcid.org/0000-0002-4915-5076"
+        }
+    ],
+    "description": "An automated open-source tool for the ISMRM/NIST system phantom.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/JamesCKorte/mrbias",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T23:40:19.245469Z",
+    "license": "BSD-3-Clause",
+    "name": "MR-BIAS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1088/1361-6560/ACBCBB",
+            "pmid": "36796102"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        }
+    ]
+}
diff --git a/data/mr-kpa/mr-kpa.biotools.json b/data/mr-kpa/mr-kpa.biotools.json
new file mode 100644
index 0000000000000..9364bda3261dc
--- /dev/null
+++ b/data/mr-kpa/mr-kpa.biotools.json
@@ -0,0 +1,100 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-21T23:55:26.599963Z",
+    "biotoolsCURIE": "biotools:mr-kpa",
+    "biotoolsID": "mr-kpa",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chenjianhui@bjut.edu.cn",
+            "name": "Jianhui Chen",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Mengzhen Wang"
+        },
+        {
+            "name": "Qingcai Gao"
+        },
+        {
+            "name": "Shaofu Lin"
+        }
+    ],
+    "description": "Medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MengzhenWangmz/MR-KPA",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-21T23:55:38.736408Z",
+    "license": "Not licensed",
+    "name": "MR-KPA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05102-1",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Medication recommendation based on electronic medical record (EMR) is a research hot spot in smart healthcare. For developing computational medication recommendation methods based on EMR, an important challenge is the lack of a large number of longitudinal EMR data with time correlation. Faced with this challenge, this paper proposes a new EMR-based medication recommendation model called MR-KPA, which combines knowledge-enhanced pre-training with the deep adversarial network to improve medication recommendation from both feature representation and the fine-tuning process. Firstly, a knowledge-enhanced pre-training visit model is proposed to realize domain knowledge-based external feature fusion and pre-training-based internal feature mining for improving the feature representation. Secondly, a medication recommendation model based on the deep adversarial network is developed to optimize the fine-tuning process of pre-training visit model and alleviate over-fitting of model caused by the task gap between pre-training and recommendation. Result: The experimental results on EMRs from medical and health institutions in Hainan Province, China show that the proposed MR-KPA model can effectively improve the accuracy of medication recommendation on small-scale longitudinal EMR data compared with existing representative methods. Conclusion: The advantages of the proposed MR-KPA are mainly attributed to knowledge enhancement based on ontology embedding, the pre-training visit model and adversarial training. Each of these three optimizations is very effective for improving the capability of medication recommendation on small-scale longitudinal EMR data, and the pre-training visit model has the most significant improvement effect. These three optimizations are also complementary, and their integration makes the proposed MR-KPA model achieve the best recommendation effect.",
+                "authors": [
+                    {
+                        "name": "Chen J."
+                    },
+                    {
+                        "name": "Chen L."
+                    },
+                    {
+                        "name": "Gao Q."
+                    },
+                    {
+                        "name": "Lin S."
+                    },
+                    {
+                        "name": "Shi C."
+                    },
+                    {
+                        "name": "Wang M."
+                    },
+                    {
+                        "name": "Xu Z."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network"
+            },
+            "pmcid": "PMC9762031",
+            "pmid": "36536291"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Complementary medicine",
+            "uri": "http://edamontology.org/topic_3423"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        }
+    ]
+}
diff --git a/data/mr_vc_v2/mr_vc_v2.biotools.json b/data/mr_vc_v2/mr_vc_v2.biotools.json
new file mode 100644
index 0000000000000..a4b327d9513db
--- /dev/null
+++ b/data/mr_vc_v2/mr_vc_v2.biotools.json
@@ -0,0 +1,130 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-21T23:51:01.743402Z",
+    "biotoolsCURIE": "biotools:mr_vc_v2",
+    "biotoolsID": "mr_vc_v2",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhanghao@wnmc.edu.cn",
+            "name": "Hao Zhang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Guozhong Chen"
+        },
+        {
+            "name": "Zhiyuan Zhang"
+        },
+        {
+            "name": "Mingquan Ye",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An updated version of database with increased data of transcriptome and experimental validated interactions.",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "http://mrvcv2.biownmc.info/download/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                },
+                {
+                    "term": "RNA-Seq quantification",
+                    "uri": "http://edamontology.org/operation_3800"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://mrvcv2.biownmc.info",
+    "lastUpdate": "2023-02-21T23:51:01.746541Z",
+    "name": "Mr Vc v2",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FMICB.2022.1047259",
+            "metadata": {
+                "abstract": "Copyright © 2022 Zhang, Chen, Hussain, Qin, Liu, Su, Zhang and Ye.Mr.Vc is a database of curated Vibrio cholerae transcriptome data and annotated information. The main objective is to facilitate the accessibility and reusability of the rapidly growing Vibrio cholerae omics data and relevant annotation. To achieve these goals, we performed manual curation on the transcriptome data and organized the datasets in an experiment-centric manner. We collected unknown operons annotated through text-mining analysis that would provide more clues about how Vibrio cholerae modulates gene regulation. Meanwhile, to understand the relationship between genes or experiments, we performed gene co-expression analysis and experiment-experiment correlation analysis. In additional, functional module named “Interactions” which dedicates to collecting experimentally validated interactions about Vibrio cholerae from public databases, MEDLINE documents and literature in life science journals. To date, Mr.Vc v2, which is significantly increased from the previous version, contains 107 microarray experiments, 106 RNA-seq experiments, and 3 Tn-seq projects, covering 56,839 entries of DEGs (Differentially Expressed Genes) from transcriptomes and 7,463 related genes from Tn-seq, respectively. and a total of 270,129 gene co-expression entries and 11,990 entries of experiment-experiment correlation was obtained, in total 1,316 entries of interactions were collected, including 496 protein-chemical signaling molecule interactions, 472 protein–protein interactions, 306 TF (Transcription Factor)-gene interactions and 42 Vibrio cholerae-virus interactions, most of which obtained from 402 literature through text-mining analysis. To make the information easier to access, Mr.Vc v2 is equipped with a search widget, enabling users to query what they are interested in. Mr.Vc v2 is freely available at http://mrvcv2.biownmc.info.",
+                "authors": [
+                    {
+                        "name": "Chen G."
+                    },
+                    {
+                        "name": "Hussain W."
+                    },
+                    {
+                        "name": "Liu J."
+                    },
+                    {
+                        "name": "Qin Z."
+                    },
+                    {
+                        "name": "Su Y."
+                    },
+                    {
+                        "name": "Ye M."
+                    },
+                    {
+                        "name": "Zhang H."
+                    },
+                    {
+                        "name": "Zhang Z."
+                    }
+                ],
+                "date": "2022-11-22T00:00:00Z",
+                "journal": "Frontiers in Microbiology",
+                "title": "Mr.Vc v2: An updated version of database with increased data of transcriptome and experimental validated interactions"
+            },
+            "pmcid": "PMC9722733",
+            "pmid": "36483202"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Data submission, annotation and curation",
+            "uri": "http://edamontology.org/topic_0219"
+        },
+        {
+            "term": "Gene structure",
+            "uri": "http://edamontology.org/topic_0114"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/mrasleepnet/mrasleepnet.biotools.json b/data/mrasleepnet/mrasleepnet.biotools.json
new file mode 100644
index 0000000000000..4007341ede028
--- /dev/null
+++ b/data/mrasleepnet/mrasleepnet.biotools.json
@@ -0,0 +1,105 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-04T01:37:44.788586Z",
+    "biotoolsCURIE": "biotools:mrasleepnet",
+    "biotoolsID": "mrasleepnet",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Shuicai Wu"
+        },
+        {
+            "name": "Xiaorong Gao"
+        },
+        {
+            "name": "Guangyu Bin",
+            "orcidid": "https://orcid.org/0000-0002-0823-179X"
+        },
+        {
+            "name": "Rui Yu",
+            "orcidid": "https://orcid.org/0000-0001-9303-3570"
+        },
+        {
+            "name": "Zhuhuang Zhou",
+            "orcidid": "https://orcid.org/0000-0003-0570-8473"
+        }
+    ],
+    "description": "A multi-resolution attention network for sleep stage classification using single-channel EEG.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/YuRui8879/MRASleepNet",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-04T01:37:44.791128Z",
+    "license": "Not licensed",
+    "name": "MRASleepNet",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1088/1741-2552/ACA2DE",
+            "metadata": {
+                "abstract": "© 2022 IOP Publishing Ltd.Objective. Computerized classification of sleep stages based on single-lead electroencephalography (EEG) signals is important, but still challenging. In this paper, we proposed a deep neural network called MRASleepNet for automatic sleep stage classification using single-channel EEG signals. Approach. The proposed MRASleepNet model consisted of a feature extraction (FE) module, a multi-resolution attention (MRA) module, and a gated multilayer perceptron (gMLP) module, as well as a direct pathway for computing statistical features. The FE, MRA, and gMLP modules were used to extract features, establish feature attention, and obtain temporal relationships between features, respectively. EEG signals were normalized and cut into 30 s segments, and enhanced by incorporating contextual information from adjacent data segments. After data enhancement, the 40 s data segments were input to the MRASleepNet model. The model was evaluated on the SleepEDF and the cyclic alternating pattern (CAP) databases, using such metrics as the accuracy, Kappa, and macro-F1 (MF1). Main results. For the SleepEDF-20 database, the proposed model had an accuracy of 84.5%, an MF1 of 0.789, and a Kappa of 0.786. For the SleepEDF-78 database, the model had an accuracy of 81.4%, an MF1 of 0.754, and a Kappa of 0.743. For the CAP database, the model had an accuracy of 74.3%, an MF1 of 0.656, and a Kappa of 0.652. The proposed model achieved satisfactory performance in automatic sleep stage classification tasks. Significance. The time- and frequency-domain features extracted by the FE module and filtered by the MRA module, together with the temporal features extracted by the gMLP module and the statistical features extracted by the statistical highway, enabled the proposed model to obtain a satisfying performance in sleep staging. The proposed MRASleepNet model may be used as a new deep learning method for automatic sleep stage classification. The code of MRASleepNet will be made available publicly on https://github.com/YuRui8879/.",
+                "authors": [
+                    {
+                        "name": "Bin G."
+                    },
+                    {
+                        "name": "Gao X."
+                    },
+                    {
+                        "name": "Wu S."
+                    },
+                    {
+                        "name": "Yu R."
+                    },
+                    {
+                        "name": "Zhou Z."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Journal of Neural Engineering",
+                "title": "MRASleepNet: a multi-resolution attention network for sleep stage classification using single-channel EEG"
+            },
+            "pmid": "36379059"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/ms-tafi/ms-tafi.biotools.json b/data/ms-tafi/ms-tafi.biotools.json
new file mode 100644
index 0000000000000..06ebb3b9fc2a8
--- /dev/null
+++ b/data/ms-tafi/ms-tafi.biotools.json
@@ -0,0 +1,93 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T14:58:42.822635Z",
+    "biotoolsCURIE": "biotools:ms-tafi",
+    "biotoolsID": "ms-tafi",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Kyle J. Juetten"
+        },
+        {
+            "name": "Jennifer S. Brodbelt",
+            "orcidid": "https://orcid.org/0000-0003-3207-0217"
+        }
+    ],
+    "description": "A Tool for the Analysis of Fragment Ions Generated from Intact Proteins.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deisotoping",
+                    "uri": "http://edamontology.org/operation_3629"
+                },
+                {
+                    "term": "Protein identification",
+                    "uri": "http://edamontology.org/operation_3767"
+                },
+                {
+                    "term": "Spectrum calculation",
+                    "uri": "http://edamontology.org/operation_3860"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/kylejuetten/MSFIT",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-20T14:58:42.826757Z",
+    "license": "Not licensed",
+    "name": "MS-TAFI",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JPROTEOME.2C00594",
+            "metadata": {
+                "abstract": "© Tandem mass spectrometry (MS/MS) spectra of intact proteins can be difficult to interpret owing to the variety of fragment ion types and abundances. This information is crucial for maximizing the information derived from top-down mass spectrometry of proteins and protein complexes. MS-TAFI (Mass Spectrometry Tool for the Analysis of Fragment Ions) is a free Python-based program which offers a streamlined approach to the data analysis and visualization of deconvoluted MS/MS data of intact proteins. The application also contains tools for native mass spectrometry experiments with the ability to search for fragment ions that retain ligands (holo ions) as well as visualize the location of charge sites obtained from 193 nm ultraviolet photodissociation data. The source code and complete application for MS-TAFI is available for download at https://github.com/kylejuetten.",
+                "authors": [
+                    {
+                        "name": "Brodbelt J.S."
+                    },
+                    {
+                        "name": "Juetten K.J."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Journal of Proteome Research",
+                "title": "MS-TAFI: A Tool for the Analysis of Fragment Ions Generated from Intact Proteins"
+            },
+            "pmid": "36516971"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Proteins",
+            "uri": "http://edamontology.org/topic_0078"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
+}
diff --git a/data/msaligmap/msaligmap.biotools.json b/data/msaligmap/msaligmap.biotools.json
new file mode 100644
index 0000000000000..e3d3734064364
--- /dev/null
+++ b/data/msaligmap/msaligmap.biotools.json
@@ -0,0 +1,140 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T14:54:05.702896Z",
+    "biotoolsCURIE": "biotools:msaligmap",
+    "biotoolsID": "msaligmap",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "henrik.aronsson@bioenv.gu.se",
+            "name": "Henrik Aronsson",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Sameena Haleemath Sameer"
+        },
+        {
+            "name": "Mats Töpel",
+            "orcidid": "https://orcid.org/0000-0001-7989-696X"
+        },
+        {
+            "name": "Sameer Hassan",
+            "orcidid": "https://orcid.org/0000-0002-2327-2645"
+        }
+    ],
+    "description": "Tool for Mapping Active-Site Amino Acids in PDB Structures onto Known and Novel Unannotated Homologous Sequences with Similar Function.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "PDB ID",
+                        "uri": "http://edamontology.org/data_1127"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Binding site prediction",
+                    "uri": "http://edamontology.org/operation_2575"
+                },
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
+                },
+                {
+                    "term": "Multiple sequence alignment",
+                    "uri": "http://edamontology.org/operation_0492"
+                },
+                {
+                    "term": "Protein secondary structure comparison",
+                    "uri": "http://edamontology.org/operation_2488"
+                },
+                {
+                    "term": "Protein-ligand docking",
+                    "uri": "http://edamontology.org/operation_0482"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://albiorix.bioenv.gu.se/MSALigMap/HomePage.py",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-20T14:54:05.705447Z",
+    "name": "MSALigMap",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/LIFE12122082",
+            "metadata": {
+                "abstract": "© 2022 by the authors.MSALigMap (Multiple Sequence Alignment Ligand Mapping) is a tool for mapping active-site amino-acid residues that bind selected ligands on to target protein sequences of interest. Users can also provide novel sequences (unavailable in public databases) for analysis. MSALigMap is written in Python. There are several tools and servers available for comparing and mapping active-site amino-acid residues among protein structures. However, there has not previously been a tool for mapping ligand binding amino-acid residues onto protein sequences of interest. Using MSALigMap, users can compare multiple protein sequences, such as those from different organisms or clinical strains, with sequences of proteins with crystal structures in PDB that are bound with the ligand/drug and DNA of interest. This allows users to easily map the binding residues and to predict the consequences of different mutations observed in the binding site. The MSALigMap server can be accessed at https://albiorix.bioenv.gu.se/MSALigMap/HomePage.py.",
+                "authors": [
+                    {
+                        "name": "Aronsson H."
+                    },
+                    {
+                        "name": "Haleemath Sameer S."
+                    },
+                    {
+                        "name": "Hassan S."
+                    },
+                    {
+                        "name": "Topel M."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Life",
+                "title": "MSALigMap—A Tool for Mapping Active-Site Amino Acids in PDB Structures onto Known and Novel Unannotated Homologous Sequences with Similar Function"
+            },
+            "pmcid": "PMC9784966",
+            "pmid": "36556447"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "DNA binding sites",
+            "uri": "http://edamontology.org/topic_3125"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Protein binding sites",
+            "uri": "http://edamontology.org/topic_3534"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/msclustering/msclustering.biotools.json b/data/msclustering/msclustering.biotools.json
new file mode 100644
index 0000000000000..12f31fa57398d
--- /dev/null
+++ b/data/msclustering/msclustering.biotools.json
@@ -0,0 +1,116 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-04T01:27:39.397771Z",
+    "biotoolsCURIE": "biotools:msclustering",
+    "biotoolsID": "msclustering",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "cchen@phy.ntnu.edu.tw",
+            "name": "Chi-Ming Chen",
+            "orcidid": "https://orcid.org/0000-0003-2202-2318",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Bo-Kai Ge"
+        },
+        {
+            "name": "Geng-Ming Hu"
+        },
+        {
+            "name": "Rex Chen"
+        }
+    ],
+    "description": "A Cytoscape Tool for Multi-Level Clustering of Biological Networks.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://drive.google.com/file/d/1z84PAYm16-MRnJr8kPgeShAcEL0tw-fJ/view?usp=sharing"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Network visualisation",
+                    "uri": "http://edamontology.org/operation_3925"
+                },
+                {
+                    "term": "Phylogenetic inference",
+                    "uri": "http://edamontology.org/operation_0323"
+                },
+                {
+                    "term": "Phylogenetic tree editing",
+                    "uri": "http://edamontology.org/operation_0326"
+                },
+                {
+                    "term": "Phylogenetic tree visualisation",
+                    "uri": "http://edamontology.org/operation_0567"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://apps.cytoscape.org/apps/msclustering",
+    "lastUpdate": "2023-02-04T01:27:39.400317Z",
+    "name": "MSClustering",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/IJMS232214240",
+            "metadata": {
+                "abstract": "© 2022 by the authors.MSClustering is an efficient software package for visualizing and analyzing complex networks in Cytoscape. Based on the distance matrix of a network that it takes as input, MSClustering automatically displays the minimum span clustering (MSC) of the network at various characteristic levels. To produce a view of the overall network structure, the app then organizes the multi-level results into an MSC tree. Here, we demonstrate the package’s phylogenetic applications in studying the evolutionary relationships of complex systems, including 63 beta coronaviruses and 197 GPCRs. The validity of MSClustering for large systems has been verified by its clustering of 3481 enzymes. Through an experimental comparison, we show that MSClustering outperforms five different state-of-the-art methods in the efficiency and reliability of their clustering.",
+                "authors": [
+                    {
+                        "name": "Chen C.-M."
+                    },
+                    {
+                        "name": "Chen R."
+                    },
+                    {
+                        "name": "Ge B.-K."
+                    },
+                    {
+                        "name": "Hu G.-M."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "International Journal of Molecular Sciences",
+                "title": "MSClustering: A Cytoscape Tool for Multi-Level Clustering of Biological Networks"
+            },
+            "pmcid": "PMC9699063",
+            "pmid": "36430723"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Enzymes",
+            "uri": "http://edamontology.org/topic_0821"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        }
+    ]
+}
diff --git a/data/mssr/mssr.biotools.json b/data/mssr/mssr.biotools.json
new file mode 100644
index 0000000000000..2c8f1663ad71d
--- /dev/null
+++ b/data/mssr/mssr.biotools.json
@@ -0,0 +1,192 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T14:37:20.873327Z",
+    "biotoolsCURIE": "biotools:mssr",
+    "biotoolsID": "mssr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "adan.guerrero@ibt.unam.mx",
+            "name": "Adán Guerrero",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alejandro Linares"
+        },
+        {
+            "name": "Esley Torres-García",
+            "orcidid": "http://orcid.org/0000-0002-9301-6962"
+        },
+        {
+            "name": "Raúl Pinto-Cámara",
+            "orcidid": "http://orcid.org/0000-0001-7528-5758"
+        }
+    ],
+    "description": "Mean-shift super resolution ImageJ plugin",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/MSSRSupport/MSSR/blob/main/MSSR_User_Manual.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MSSRSupport/MSSR",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-02-20T14:37:44.081742Z",
+    "license": "Not licensed",
+    "name": "MSSR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41467-022-34693-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).The resolution of fluorescence microscopy images is limited by the physical properties of light. In the last decade, numerous super-resolution microscopy (SRM) approaches have been proposed to deal with such hindrance. Here we present Mean-Shift Super Resolution (MSSR), a new SRM algorithm based on the Mean Shift theory, which extends spatial resolution of single fluorescence images beyond the diffraction limit of light. MSSR works on low and high fluorophore densities, is not limited by the architecture of the optical setup and is applicable to single images as well as temporal series. The theoretical limit of spatial resolution, based on optimized real-world imaging conditions and analysis of temporal image stacks, has been measured to be 40 nm. Furthermore, MSSR has denoising capabilities that outperform other SRM approaches. Along with its wide accessibility, MSSR is a powerful, flexible, and generic tool for multidimensional and live cell imaging applications.",
+                "authors": [
+                    {
+                        "name": "Abonza V."
+                    },
+                    {
+                        "name": "Barchi M."
+                    },
+                    {
+                        "name": "Boskovic A."
+                    },
+                    {
+                        "name": "Brito-Alarcon E."
+                    },
+                    {
+                        "name": "Buffone M.G."
+                    },
+                    {
+                        "name": "Calcines-Cruz C."
+                    },
+                    {
+                        "name": "Crevenna A.H."
+                    },
+                    {
+                        "name": "D'Antuono R."
+                    },
+                    {
+                        "name": "Darszon A."
+                    },
+                    {
+                        "name": "Dubrovsky J.G."
+                    },
+                    {
+                        "name": "Garces Y."
+                    },
+                    {
+                        "name": "Guerrero A."
+                    },
+                    {
+                        "name": "Hernandez H.O."
+                    },
+                    {
+                        "name": "Hernandez-Garcia A."
+                    },
+                    {
+                        "name": "Jablonski M."
+                    },
+                    {
+                        "name": "Krapf D."
+                    },
+                    {
+                        "name": "Linares A."
+                    },
+                    {
+                        "name": "Martinez D."
+                    },
+                    {
+                        "name": "Martinez J.L."
+                    },
+                    {
+                        "name": "Morales R.R."
+                    },
+                    {
+                        "name": "Ocelotl-Oviedo J.P."
+                    },
+                    {
+                        "name": "Pinto-Camara R."
+                    },
+                    {
+                        "name": "Rendon-Mancha J.M."
+                    },
+                    {
+                        "name": "Torres D."
+                    },
+                    {
+                        "name": "Torres-Garcia E."
+                    },
+                    {
+                        "name": "Torres-Martinez H.H."
+                    },
+                    {
+                        "name": "Valdes-Galindo G."
+                    },
+                    {
+                        "name": "Wood C.D."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "Extending resolution within a single imaging frame"
+            },
+            "pmcid": "PMC9718789",
+            "pmid": "36460648"
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "imagej",
+            "type": "uses"
+        }
+    ],
+    "toolType": [
+        "Plug-in"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Physics",
+            "uri": "http://edamontology.org/topic_3318"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/mtaxi/mtaxi.biotools.json b/data/mtaxi/mtaxi.biotools.json
new file mode 100644
index 0000000000000..0fe321d083088
--- /dev/null
+++ b/data/mtaxi/mtaxi.biotools.json
@@ -0,0 +1,85 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T12:59:18.832781Z",
+    "biotoolsCURIE": "biotools:mtaxi",
+    "biotoolsID": "mtaxi",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "atag.gozde@gmail.com",
+            "name": "Gözde Atağ",
+            "orcidid": "http://orcid.org/0000-0001-6173-3126",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Füsun Özer"
+        },
+        {
+            "name": "Kıvılcım Başak Vural"
+        },
+        {
+            "name": "Mehmet Somel"
+        }
+    ],
+    "description": "A comparative tool for taxon identification of ultra low coverage ancient genomes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome alignment",
+                    "uri": "http://edamontology.org/operation_3182"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Pairwise sequence alignment",
+                    "uri": "http://edamontology.org/operation_0491"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/goztag/MTaxi",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T12:59:18.835437Z",
+    "license": "CC-BY-4.0",
+    "name": "MTaxi",
+    "operatingSystem": [
+        "Linux",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1101/2022.06.06.491147"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/mtsv/mtsv.biotools.json b/data/mtsv/mtsv.biotools.json
new file mode 100644
index 0000000000000..1d729332b08a9
--- /dev/null
+++ b/data/mtsv/mtsv.biotools.json
@@ -0,0 +1,126 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-04T00:56:49.134843Z",
+    "biotoolsCURIE": "biotools:mtsv",
+    "biotoolsID": "mtsv",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tara.furstenau@nau.edu",
+            "name": "Tara N. Furstenau",
+            "orcidid": "https://orcid.org/0000-0001-5233-7383",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jason Sahl"
+        },
+        {
+            "name": "Tsosie Schneider"
+        },
+        {
+            "name": "Viacheslav Fofanov"
+        }
+    ],
+    "description": "Rapid alignment-based taxonomic classification and high-confidence metagenomic analysis.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome indexing",
+                    "uri": "http://edamontology.org/operation_3211"
+                },
+                {
+                    "term": "Local alignment",
+                    "uri": "http://edamontology.org/operation_0495"
+                },
+                {
+                    "term": "Read binning",
+                    "uri": "http://edamontology.org/operation_3798"
+                },
+                {
+                    "term": "Read mapping",
+                    "uri": "http://edamontology.org/operation_3198"
+                },
+                {
+                    "term": "Taxonomic classification",
+                    "uri": "http://edamontology.org/operation_3460"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/FofanovLab/mtsv_tools",
+    "language": [
+        "C"
+    ],
+    "lastUpdate": "2023-02-04T00:56:49.137352Z",
+    "license": "MIT",
+    "name": "MTSv",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.7717/PEERJ.14292",
+            "metadata": {
+                "abstract": "Copyright © 2022 Furstenau et al.As the size of reference sequence databases and high-throughput sequencing datasets continue to grow, it is becoming computationally infeasible to use traditional alignment to large genome databases for taxonomic classification of metagenomic reads. Exact matching approaches can rapidly assign taxonomy and summarize the composition of microbial communities, but they sacrifice accuracy and can lead to false positives. Full alignment tools provide higher confidence assignments and can assign sequences from genomes that diverge from reference sequences; however, full alignment tools are computationally intensive. To address this, we designed MTSv specifically for alignment-based taxonomic assignment in metagenomic analysis. This tool implements an FM-index assisted q-gram filter and SIMD accelerated Smith-Waterman algorithm to find alignments. However, unlike traditional aligners, MTSv will not attempt to make additional alignments to a TaxID once an alignment of sufficient quality has been found. This improves efficiency when many reference sequences are available per taxon. MTSv was designed to be flexible and can be modified to run on either memory or processor constrained systems. Although MTSv cannot compete with the speeds of exact k-mer matching approaches, it is reasonably fast and has higher precision than popular exact matching approaches. Because MTSv performs a full alignment it can classify reads even when the genomes share low similarity with reference sequences and provides a tool for high confidence pathogen detection with low off-target assignments to near neighbor species.",
+                "authors": [
+                    {
+                        "name": "Fofanov V."
+                    },
+                    {
+                        "name": "Furstenau T.N."
+                    },
+                    {
+                        "name": "Sahl J."
+                    },
+                    {
+                        "name": "Schneider T."
+                    },
+                    {
+                        "name": "Shaffer I."
+                    },
+                    {
+                        "name": "Vazquez A.J."
+                    }
+                ],
+                "date": "2022-11-08T00:00:00Z",
+                "journal": "PeerJ",
+                "title": "MTSv: rapid alignment-based taxonomic classification and high-confidence metagenomic analysis"
+            },
+            "pmcid": "PMC9651046",
+            "pmid": "36389404"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Metagenomic sequencing",
+            "uri": "http://edamontology.org/topic_3837"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Taxonomy",
+            "uri": "http://edamontology.org/topic_0637"
+        }
+    ]
+}
diff --git a/data/mu3dsp/mu3dsp.biotools.json b/data/mu3dsp/mu3dsp.biotools.json
new file mode 100644
index 0000000000000..98ba371c7f02b
--- /dev/null
+++ b/data/mu3dsp/mu3dsp.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T14:27:41.341008Z",
+    "biotoolsCURIE": "biotools:mu3dsp",
+    "biotoolsID": "mu3dsp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xudong@missouri.edu",
+            "name": "Dong Xu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jianting Gong"
+        },
+        {
+            "name": "Juexin Wang"
+        },
+        {
+            "name": "Xizeng Zong"
+        },
+        {
+            "name": "Zhiqiang Ma",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Prediction of protein stability changes upon single-point variant using 3D structure profile.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein structure prediction",
+                    "uri": "http://edamontology.org/operation_0474"
+                },
+                {
+                    "term": "Structural motif discovery",
+                    "uri": "http://edamontology.org/operation_0245"
+                },
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/hurraygong/MU3DSP",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-20T14:27:41.343635Z",
+    "license": "Not licensed",
+    "name": "MU3DSP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.12.008",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsIdentifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of single-point variants, but the prediction accuracy is still far from satisfactory for practical applications. Herein, we review approaches and tools for predicting stability changes upon the single-point variant. Most of these methods require tertiary protein structure as input to achieve reliable predictions. However, the availability of protein structures limits the immediate application of these tools. To improve the performance of a computational prediction from a protein sequence without experimental structural information, we introduce a new computational framework: MU3DSP. This method assesses the effects of single-point variants on protein thermodynamic stability based on point mutated protein 3D structure profile. Given a protein sequence with a single variant as input, MU3DSP integrates both sequence-level features and averaged features of 3D structures obtained from sequence alignment to PDB to assess the change of thermodynamic stability induced by the substitution. MU3DSP outperforms existing methods on various benchmarks, making it a reliable tool to assess both somatic and germline substitution variants and assist in protein design. MU3DSP is available as an open-source tool at https://github.com/hurraygong/MU3DSP.",
+                "authors": [
+                    {
+                        "name": "Gong J."
+                    },
+                    {
+                        "name": "Ma Z."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Xu D."
+                    },
+                    {
+                        "name": "Zong X."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "Prediction of protein stability changes upon single-point variant using 3D structure profile"
+            },
+            "pmcid": "PMC9791599",
+            "pmid": "36582438"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Protein folding, stability and design",
+            "uri": "http://edamontology.org/topic_0130"
+        },
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        },
+        {
+            "term": "Protein structural motifs and surfaces",
+            "uri": "http://edamontology.org/topic_0166"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        }
+    ]
+}
diff --git a/data/multidataset/multidataset.biotools.json b/data/multidataset/multidataset.biotools.json
index 9e1759a75392a..91fb8fca949bf 100644
--- a/data/multidataset/multidataset.biotools.json
+++ b/data/multidataset/multidataset.biotools.json
@@ -6,13 +6,38 @@
         "BioConductor"
     ],
     "credit": [
+        {
+            "email": "carles.hernandez@isglobal.org",
+            "name": "Carles Hernandez-Ferrer",
+            "orcidid": "https://orcid.org/0000-0002-8029-7160",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Developer"
+            ],
+            "url": "http://www.carleshf.com"
+        },
         {
             "email": "carlos.ruiz@isglobal.org",
             "name": "Carlos Ruiz-Arenas",
             "orcidid": "http://orcid.org/0000-0002-6014-3498",
+            "typeRole": [
+                "Developer"
+            ]
+        },
+        {
+            "email": "juanr.gonzalez@isglobal.org",
+            "name": "Juan R Gonzalez",
             "typeEntity": "Person",
             "typeRole": [
                 "Primary contact"
+            ],
+            "url": "https://brge.isglobal.org/"
+        },
+        {
+            "name": "Alba Beltran-Gomila",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
             ]
         }
     ],
@@ -32,14 +57,17 @@
         }
     ],
     "editPermission": {
-        "type": "private"
+        "authors": [
+            "chernan3"
+        ],
+        "type": "group"
     },
     "function": [
         {
             "operation": [
                 {
-                    "term": "Methylation analysis",
-                    "uri": "http://edamontology.org/operation_3204"
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
                 }
             ]
         }
@@ -48,7 +76,15 @@
     "language": [
         "R"
     ],
-    "lastUpdate": "2019-01-13T18:34:15Z",
+    "lastUpdate": "2023-02-07T13:01:12.031379Z",
+    "link": [
+        {
+            "type": [
+                "Mirror"
+            ],
+            "url": "http://www.bioconductor.org/packages/release/bioc/html/MultiDataSet.html"
+        }
+    ],
     "name": "MultiDataSet",
     "operatingSystem": [
         "Linux",
@@ -75,7 +111,7 @@
                         "name": "Ruiz-Arenas C."
                     }
                 ],
-                "citationCount": 14,
+                "citationCount": 19,
                 "date": "2017-01-17T00:00:00Z",
                 "journal": "BMC Bioinformatics",
                 "title": "MultiDataSet: An R package for encapsulating multiple data sets with application to omic data integration"
@@ -87,6 +123,12 @@
             ]
         }
     ],
+    "relation": [
+        {
+            "biotoolsID": "rexposome",
+            "type": "uses"
+        }
+    ],
     "toolType": [
         "Command-line tool",
         "Library"
diff --git a/data/myops-net/myops-net.biotools.json b/data/myops-net/myops-net.biotools.json
new file mode 100644
index 0000000000000..9f7cd24d4ec24
--- /dev/null
+++ b/data/myops-net/myops-net.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T14:23:43.030542Z",
+    "biotoolsCURIE": "biotools:myops-net",
+    "biotoolsID": "myops-net",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Junyi Qiu"
+        },
+        {
+            "name": "Shan Yang"
+        },
+        {
+            "name": "Lei Li",
+            "orcidid": "https://orcid.org/0000-0003-1281-6472"
+        },
+        {
+            "name": "Xiahai Zhuang",
+            "orcidid": "https://orcid.org/0000-0003-4351-4979"
+        }
+    ],
+    "description": "Myocardial pathology segmentation with flexible combination of multi-sequence CMR images.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Information extraction",
+                    "uri": "http://edamontology.org/operation_3907"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/QJYBall/MyoPS-Net",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-20T14:23:43.033254Z",
+    "license": "MIT",
+    "name": "MyoPS-Net",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.MEDIA.2022.102694",
+            "metadata": {
+                "abstract": "© 2022 Elsevier B.V.Myocardial pathology segmentation (MyoPS) can be a prerequisite for the accurate diagnosis and treatment planning of myocardial infarction. However, achieving this segmentation is challenging, mainly due to the inadequate and indistinct information from an image. In this work, we develop an end-to-end deep neural network, referred to as MyoPS-Net, to flexibly combine five-sequence cardiac magnetic resonance (CMR) images for MyoPS. To extract precise and adequate information, we design an effective yet flexible architecture to extract and fuse cross-modal features. This architecture can tackle different numbers of CMR images and complex combinations of modalities, with output branches targeting specific pathologies. To impose anatomical knowledge on the segmentation results, we first propose a module to regularize myocardium consistency and localize the pathologies, and then introduce an inclusiveness loss to utilize relations between myocardial scars and edema. We evaluated the proposed MyoPS-Net on two datasets, i.e., a private one consisting of 50 paired multi-sequence CMR images and a public one from MICCAI2020 MyoPS Challenge. Experimental results showed that MyoPS-Net could achieve state-of-the-art performance in various scenarios. Note that in practical clinics, the subjects may not have full sequences, such as missing LGE CMR or mapping CMR scans. We therefore conducted extensive experiments to investigate the performance of the proposed method in dealing with such complex combinations of different CMR sequences. Results proved the superiority and generalizability of MyoPS-Net, and more importantly, indicated a practical clinical application. The code has been released via https://github.com/QJYBall/MyoPS-Net.",
+                "authors": [
+                    {
+                        "name": "Chen Y."
+                    },
+                    {
+                        "name": "Li L."
+                    },
+                    {
+                        "name": "Qiu J."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Yang S."
+                    },
+                    {
+                        "name": "Zhang K."
+                    },
+                    {
+                        "name": "Zhuang X."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "Medical Image Analysis",
+                "title": "MyoPS-Net: Myocardial pathology segmentation with flexible combination of multi-sequence CMR images"
+            },
+            "pmid": "36495601"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/myosothes/myosothes.biotools.json b/data/myosothes/myosothes.biotools.json
new file mode 100644
index 0000000000000..395d670df66ee
--- /dev/null
+++ b/data/myosothes/myosothes.biotools.json
@@ -0,0 +1,122 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-04T00:50:07.044979Z",
+    "biotoolsCURIE": "biotools:myosothes",
+    "biotoolsID": "myosothes",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "stockho@genethon.fr",
+            "name": "Daniel Stockholm",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Elisabeth Brunet"
+        },
+        {
+            "name": "Jérémie Cosette"
+        },
+        {
+            "name": "Marie Reinbigler"
+        }
+    ],
+    "description": "Artificial intelligence workflow quantifying muscle features on Hematoxylin-Eosin stained sections reveals dystrophic phenotype amelioration upon treatment.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/brunettsp/myosothes",
+    "language": [
+        "Groovy",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-04T00:50:07.047512Z",
+    "license": "GPL-3.0",
+    "name": "MyoSOTHES",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41598-022-24139-Z",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Cell segmentation is a key step for a wide variety of biological investigations, especially in the context of muscle science. Currently, automated methods still struggle to perform skeletal muscle fiber quantification on Hematoxylin-Eosin (HE) stained histopathological whole slide images due to low contrast. On the other hand, the Deep Learning algorithm Cellpose offers new perspectives considering its increasing adoption for segmentation of a wide range of cells. Combining two open-source tools, Cellpose and QuPath, we developed MyoSOTHES, an automated Myofibers Segmentation wOrkflow Tuned for HE Staining. MyoSOTHES enables solving segmentation inconsistencies encountered by default Cellpose model in presence of large range size cells and provides information related to muscle Feret’s diameter distribution and Centrally Nucleated Fibers, thus depicting muscle health and treatment effects. MyoSOTHES achieves high quality segmentation compared to baseline workflow with a detection F1-score increasing from 0.801 to 0.919 and a Root Mean Square Error (RMSE) on diameter improved by 31%. MyoSOTHES was validated on an animal study featuring gene transfer in γ-Sarcoglycanopathy, for which dose-response effect is visible and conclusions drawn are consistent with those previously published. MyoSOTHES thus paves the way for wide quantification of HE stained muscle sections and retrospective analysis of HE labeled slices used in laboratories for decades.",
+                "authors": [
+                    {
+                        "name": "Brunet E."
+                    },
+                    {
+                        "name": "Cosette J."
+                    },
+                    {
+                        "name": "Fetita C."
+                    },
+                    {
+                        "name": "Guesmia Z."
+                    },
+                    {
+                        "name": "Jimenez S."
+                    },
+                    {
+                        "name": "Reinbigler M."
+                    },
+                    {
+                        "name": "Stockholm D."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "Artificial intelligence workflow quantifying muscle features on Hematoxylin–Eosin stained sections reveals dystrophic phenotype amelioration upon treatment"
+            },
+            "pmcid": "PMC9675753",
+            "pmid": "36402802"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Bioimaging",
+            "uri": "http://edamontology.org/topic_3383"
+        },
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/mza/mza.biotools.json b/data/mza/mza.biotools.json
new file mode 100644
index 0000000000000..a01e71a48c247
--- /dev/null
+++ b/data/mza/mza.biotools.json
@@ -0,0 +1,116 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-04T00:44:09.069708Z",
+    "biotoolsCURIE": "biotools:mza",
+    "biotoolsID": "mza",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Aivett.Bilbao@pnnl.gov",
+            "name": "Aivett Bilbao",
+            "orcidid": "https://orcid.org/0000-0003-2985-8249"
+        },
+        {
+            "email": "Xueyun.Zheng@pnnl.gov",
+            "name": "Xueyun Zheng",
+            "orcidid": "https://orcid.org/0000-0001-9782-4521"
+        },
+        {
+            "name": "Dylan H. Ross"
+        },
+        {
+            "name": "Richard D. Smith"
+        }
+    ],
+    "description": "A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Formatting",
+                    "uri": "http://edamontology.org/operation_0335"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/PNNL-m-q/mza",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-02-04T00:44:09.072256Z",
+    "license": "BSD-2-Clause",
+    "name": "MZA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JPROTEOME.2C00313",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society.Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich spectra challenges existing data storage and access technologies, therefore precluding informatics advancements. We present MZA (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed of a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and cross-programming language access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable, example MS-data and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.",
+                "authors": [
+                    {
+                        "name": "Bilbao A."
+                    },
+                    {
+                        "name": "Donor M.T."
+                    },
+                    {
+                        "name": "Ibrahim Y.M."
+                    },
+                    {
+                        "name": "Lee J.-Y."
+                    },
+                    {
+                        "name": "Ross D.H."
+                    },
+                    {
+                        "name": "Smith R.D."
+                    },
+                    {
+                        "name": "Williams S.M."
+                    },
+                    {
+                        "name": "Zheng X."
+                    },
+                    {
+                        "name": "Zhu Y."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Journal of Proteome Research",
+                "title": "MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry"
+            },
+            "pmid": "36414245"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/nano3p-seq/nano3p-seq.biotools.json b/data/nano3p-seq/nano3p-seq.biotools.json
new file mode 100644
index 0000000000000..6793c6031be02
--- /dev/null
+++ b/data/nano3p-seq/nano3p-seq.biotools.json
@@ -0,0 +1,150 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T14:19:25.528414Z",
+    "biotoolsCURIE": "biotools:nano3p-seq",
+    "biotoolsID": "nano3p-seq",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "eva.novoa@crg.eu",
+            "name": "Eva Maria Novoa",
+            "orcidid": "https://orcid.org/0000-0002-9367-6311",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Gregor Diensthuber"
+        },
+        {
+            "name": "John S. Mattick",
+            "orcidid": "http://orcid.org/0000-0002-7680-7527"
+        },
+        {
+            "name": "Oguzhan Begik",
+            "orcidid": "http://orcid.org/0000-0002-8663-4586"
+        }
+    ],
+    "description": "Transcriptome-wide analysis of gene expression and tail dynamics using end-capture nanopore cDNA sequencing.",
+    "download": [
+        {
+            "type": "Other",
+            "url": "https://github.com/adnaniazi/tailfindr/tree/nano3p-seq"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Base-calling",
+                    "uri": "http://edamontology.org/operation_3185"
+                },
+                {
+                    "term": "Demultiplexing",
+                    "uri": "http://edamontology.org/operation_3933"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "PolyA signal detection",
+                    "uri": "http://edamontology.org/operation_0428"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/novoalab/Nano3P_Seq",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-02-20T14:19:25.530910Z",
+    "license": "MIT",
+    "name": "Nano3P-seq",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41592-022-01714-W",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).RNA polyadenylation plays a central role in RNA maturation, fate, and stability. In response to developmental cues, polyA tail lengths can vary, affecting the translation efficiency and stability of mRNAs. Here we develop Nanopore 3′ end-capture sequencing (Nano3P-seq), a method that relies on nanopore cDNA sequencing to simultaneously quantify RNA abundance, tail composition, and tail length dynamics at per-read resolution. By employing a template-switching-based sequencing protocol, Nano3P-seq can sequence RNA molecule from its 3′ end, regardless of its polyadenylation status, without the need for PCR amplification or ligation of RNA adapters. We demonstrate that Nano3P-seq provides quantitative estimates of RNA abundance and tail lengths, and captures a wide diversity of RNA biotypes. We find that, in addition to mRNA and long non-coding RNA, polyA tails can be identified in 16S mitochondrial ribosomal RNA in both mouse and zebrafish models. Moreover, we show that mRNA tail lengths are dynamically regulated during vertebrate embryogenesis at an isoform-specific level, correlating with mRNA decay. Finally, we demonstrate the ability of Nano3P-seq in capturing non-A bases within polyA tails of various lengths, and reveal their distribution during vertebrate embryogenesis. Overall, Nano3P-seq is a simple and robust method for accurately estimating transcript levels, tail lengths, and tail composition heterogeneity in individual reads, with minimal library preparation biases, both in the coding and non-coding transcriptome.",
+                "authors": [
+                    {
+                        "name": "Beaudoin J.-D."
+                    },
+                    {
+                        "name": "Begik O."
+                    },
+                    {
+                        "name": "Delgado-Tejedor A."
+                    },
+                    {
+                        "name": "Diensthuber G."
+                    },
+                    {
+                        "name": "Giraldez A.J."
+                    },
+                    {
+                        "name": "Kontur C."
+                    },
+                    {
+                        "name": "Liu H."
+                    },
+                    {
+                        "name": "Mattick J.S."
+                    },
+                    {
+                        "name": "Niazi A.M."
+                    },
+                    {
+                        "name": "Novoa E.M."
+                    },
+                    {
+                        "name": "Valen E."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Nature Methods",
+                "title": "Nano3P-seq: transcriptome-wide analysis of gene expression and tail dynamics using end-capture nanopore cDNA sequencing"
+            },
+            "pmcid": "PMC9834059",
+            "pmid": "36536091"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/nanomodeler/nanomodeler.biotools.json b/data/nanomodeler/nanomodeler.biotools.json
new file mode 100644
index 0000000000000..6b88f4f36f769
--- /dev/null
+++ b/data/nanomodeler/nanomodeler.biotools.json
@@ -0,0 +1,105 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T23:28:32.573106Z",
+    "biotoolsCURIE": "biotools:nanomodeler",
+    "biotoolsID": "nanomodeler",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Mattia Pini"
+        },
+        {
+            "name": "Laura Riccardi",
+            "orcidid": "https://orcid.org/0000-0002-5315-5140"
+        },
+        {
+            "name": "Marco De Vivo",
+            "orcidid": "https://orcid.org/0000-0003-4022-5661"
+        },
+        {
+            "name": "Sebastian Franco-Ulloa",
+            "orcidid": "https://orcid.org/0000-0001-6128-0630"
+        }
+    ],
+    "description": "A Tool for Modeling and Engineering Functional Nanoparticles at a Coarse-Grained Resolution.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.nanomodeler.it",
+    "lastUpdate": "2023-03-18T23:28:32.577701Z",
+    "name": "NanoModeler",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCTC.2C01029",
+            "metadata": {
+                "abstract": "Functionalized metal nanoparticles (NPs) are macromolecular assemblies with a tunable physicochemical profile that makes them interesting for biotechnology, materials science, and energy conversion. In this regard, molecular simulations offer a way to scrutinize the structural and dynamical features of monolayer-protected NPs and their interactions with relevant matrices. Previously, we developed NanoModeler, a webserver that automates the preparation of functionalized gold NPs for atomistic molecular dynamics (MD) simulations. Here, we present NanoModeler CG (www.nanomodeler.it), a new release of NanoModeler that now also allows the building and parametrizing of monolayer-protected metal NPs at a coarse-grained (CG) resolution. This new version extends our original methodology to NPs of eight different core shapes, conformed by up to 800,000 beads and coated by eight different monolayer morphologies. The resulting topologies are compatible with the Martini force field but are easily extendable to any other set of parameters parsed by the user. Finally, we demonstrate NanoModeler CG’s capabilities by reproducing experimental structural features of alkylthiolated NPs and rationalizing the brush-to-mushroom phase transition of PEGylated anionic NPs. By automating the construction and parametrization of functionalized NPs, the NanoModeler series offers a standardized way to computationally model monolayer-protected nanosized systems.",
+                "authors": [
+                    {
+                        "name": "De Vivo M."
+                    },
+                    {
+                        "name": "Franco-Ulloa S."
+                    },
+                    {
+                        "name": "Grottin E."
+                    },
+                    {
+                        "name": "Pini M."
+                    },
+                    {
+                        "name": "Riccardi L."
+                    },
+                    {
+                        "name": "Rimembrana F."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Journal of Chemical Theory and Computation",
+                "title": "NanoModeler CG: A Tool for Modeling and Engineering Functional Nanoparticles at a Coarse-Grained Resolution"
+            },
+            "pmid": "36795071"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biotechnology",
+            "uri": "http://edamontology.org/topic_3297"
+        },
+        {
+            "term": "Molecular dynamics",
+            "uri": "http://edamontology.org/topic_0176"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/nanopore_py/nanopore_py.biotools.json b/data/nanopore_py/nanopore_py.biotools.json
new file mode 100644
index 0000000000000..7d0f6ff1e6fe9
--- /dev/null
+++ b/data/nanopore_py/nanopore_py.biotools.json
@@ -0,0 +1,137 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T01:07:12.421744Z",
+    "biotoolsCURIE": "biotools:nanopore_py",
+    "biotoolsID": "nanopore_py",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "rbluo@cs.hku.hk",
+            "name": "Ruibang Luo",
+            "orcidid": "https://orcid.org/0000-0001-9711-6533",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "shoudongzhang@cuhk.edu.hk",
+            "name": "Shoudong Zhang",
+            "orcidid": "https://orcid.org/0000-0001-7332-7627",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xiaochuanle@126.com",
+            "name": "Chuanle Xiao",
+            "orcidid": "https://orcid.org/0000-0002-4680-0682",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "luoming@scbg.ac.cn",
+            "name": "Ming Luo",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Applications and potentials of nanopore sequencing in the (epi)genome and (epi)transcriptome era.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Base-calling",
+                    "uri": "http://edamontology.org/operation_3185"
+                },
+                {
+                    "term": "Indel detection",
+                    "uri": "http://edamontology.org/operation_0452"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/jts/nanopore-paper-analysis",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-01-27T01:07:12.424208Z",
+    "license": "Not licensed",
+    "name": "Nanopore",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.XINN.2021.100153",
+            "metadata": {
+                "abstract": "© 2021 The Author(s)The Human Genome Project opened an era of (epi)genomic research, and also provided a platform for the development of new sequencing technologies. During and after the project, several sequencing technologies continue to dominate nucleic acid sequencing markets. Currently, Illumina (short-read), PacBio (long-read), and Oxford Nanopore (long-read) are the most popular sequencing technologies. Unlike PacBio or the popular short-read sequencers before it, which, as examples of the second or so-called Next-Generation Sequencing platforms, need to synthesize when sequencing, nanopore technology directly sequences native DNA and RNA molecules. Nanopore sequencing, therefore, avoids converting mRNA into cDNA molecules, which not only allows for the sequencing of extremely long native DNA and full-length RNA molecules but also document modifications that have been made to those native DNA or RNA bases. In this review on direct DNA sequencing and direct RNA sequencing using Oxford Nanopore technology, we focus on their development and application achievements, discussing their challenges and future perspective. We also address the problems researchers may encounter applying these approaches in their research topics, and how to resolve them.",
+                "authors": [
+                    {
+                        "name": "Leung A.W.-S."
+                    },
+                    {
+                        "name": "Luo M."
+                    },
+                    {
+                        "name": "Luo R."
+                    },
+                    {
+                        "name": "Xiao C."
+                    },
+                    {
+                        "name": "Xie S."
+                    },
+                    {
+                        "name": "Zhang D."
+                    },
+                    {
+                        "name": "Zhang S."
+                    },
+                    {
+                        "name": "Zheng Z."
+                    }
+                ],
+                "citationCount": 11,
+                "date": "2021-11-28T00:00:00Z",
+                "journal": "The Innovation",
+                "title": "Applications and potentials of nanopore sequencing in the (epi)genome and (epi)transcriptome era"
+            },
+            "pmcid": "PMC8640597",
+            "pmid": "34901902"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Structural variation",
+            "uri": "http://edamontology.org/topic_3175"
+        }
+    ]
+}
diff --git a/data/nanosnp/nanosnp.biotools.json b/data/nanosnp/nanosnp.biotools.json
new file mode 100644
index 0000000000000..c543cf948311d
--- /dev/null
+++ b/data/nanosnp/nanosnp.biotools.json
@@ -0,0 +1,127 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T14:13:10.310917Z",
+    "biotoolsCURIE": "biotools:nanosnp",
+    "biotoolsID": "nanosnp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jxwang@mail.csu.edu.cn",
+            "name": "Jianxin Wang",
+            "orcidid": "https://orcid.org/0000-0003-1516-0480",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Minghua Xu"
+        },
+        {
+            "name": "Neng Huang"
+        },
+        {
+            "name": "Feng Luo",
+            "orcidid": "https://orcid.org/0000-0002-4813-2403"
+        }
+    ],
+    "description": "A progressive and haplotype-aware SNP caller on low-coverage nanopore sequencing data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Base-calling",
+                    "uri": "http://edamontology.org/operation_3185"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Haplotype mapping",
+                    "uri": "http://edamontology.org/operation_0487"
+                },
+                {
+                    "term": "SNP detection",
+                    "uri": "http://edamontology.org/operation_0484"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/huangnengCSU/NanoSNP.git",
+    "language": [
+        "C++",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-20T14:13:10.313480Z",
+    "license": "MIT",
+    "name": "NanoSNP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC824",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Oxford Nanopore sequencing has great potential and advantages in population-scale studies. Due to the cost of sequencing, the depth of whole-genome sequencing for per individual sample must be small. However, the existing single nucleotide polymorphism (SNP) callers are aimed at high-coverage Nanopore sequencing reads. Detecting the SNP variants on low-coverage Nanopore sequencing data is still a challenging problem. RESULTS: We developed a novel deep learning-based SNP calling method, NanoSNP, to identify the SNP sites (excluding short indels) based on low-coverage Nanopore sequencing reads. In this method, we design a multi-step, multi-scale and haplotype-aware SNP detection pipeline. First, the pileup model in NanoSNP utilizes the naive pileup feature to predict a subset of SNP sites with a Bi-long short-term memory (LSTM) network. These SNP sites are phased and used to divide the low-coverage Nanopore reads into different haplotypes. Finally, the long-range haplotype feature and short-range pileup feature are extracted from each haplotype. The haplotype model combines two features and predicts the genotype for the candidate site using a Bi-LSTM network. To evaluate the performance of NanoSNP, we compared NanoSNP with Clair, Clair3, Pepper-DeepVariant and NanoCaller on the low-coverage (∼16×) Nanopore sequencing reads. We also performed cross-genome testing on six human genomes HG002-HG007, respectively. Comprehensive experiments demonstrate that NanoSNP outperforms Clair, Pepper-DeepVariant and NanoCaller in identifying SNPs on low-coverage Nanopore sequencing data, including the difficult-to-map regions and major histocompatibility complex regions in the human genome. NanoSNP is comparable to Clair3 when the coverage exceeds 16×. AVAILABILITY AND IMPLEMENTATION: https://github.com/huangnengCSU/NanoSNP.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Huang N."
+                    },
+                    {
+                        "name": "Luo F."
+                    },
+                    {
+                        "name": "Ni P."
+                    },
+                    {
+                        "name": "Nie F."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Xiao C.-L."
+                    },
+                    {
+                        "name": "Xu M."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "NanoSNP: a progressive and haplotype-aware SNP caller on low-coverage nanopore sequencing data"
+            },
+            "pmcid": "PMC9822538",
+            "pmid": "36548365"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/nanostr/nanostr.biotools.json b/data/nanostr/nanostr.biotools.json
new file mode 100644
index 0000000000000..cbeabb668c100
--- /dev/null
+++ b/data/nanostr/nanostr.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T23:22:06.675164Z",
+    "biotoolsCURIE": "biotools:nanostr",
+    "biotoolsID": "nanostr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "langjidong@hotmail.com",
+            "name": "Jidong Lang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jiguo Sun"
+        },
+        {
+            "name": "Yue Wang"
+        },
+        {
+            "name": "Zhihua Xu"
+        }
+    ],
+    "description": "A method for detection of target short tandem repeats based on nanopore sequencing data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Indel detection",
+                    "uri": "http://edamontology.org/operation_0452"
+                },
+                {
+                    "term": "Repeat sequence detection",
+                    "uri": "http://edamontology.org/operation_0379"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/langjidong/NanoSTR",
+    "language": [
+        "Perl",
+        "Shell"
+    ],
+    "lastUpdate": "2023-03-18T23:22:06.680263Z",
+    "license": "Not licensed",
+    "name": "NanoSTR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FMOLB.2023.1093519",
+            "metadata": {
+                "abstract": "Short tandem repeats (STRs) are widely present in the human genome. Studies have confirmed that STRs are associated with more than 30 diseases, and they have also been used in forensic identification and paternity testing. However, there are few methods for STR detection based on nanopore sequencing due to the challenges posed by the sequencing principles and the data characteristics of nanopore sequencing. We developed NanoSTR for detection of target STR loci based on the length-number-rank (LNR) information of reads. NanoSTR can be used for STR detection and genotyping based on long-read data from nanopore sequencing with improved accuracy and efficiency compared with other existing methods, such as Tandem-Genotypes and TRiCoLOR. NanoSTR showed 100% concordance with the expected genotypes using error-free simulated data, and also achieved >85% concordance using the standard samples (containing autosomal and Y-chromosomal loci) with MinION sequencing platform, respectively. NanoSTR showed high performance for detection of target STR markers. Although NanoSTR needs further optimization and development, it is useful as an analytical method for the detection of STR loci by nanopore sequencing. This method adds to the toolbox for nanopore-based STR analysis and expands the applications of nanopore sequencing in scientific research and clinical scenarios. The main code and the data are available at https://github.com/langjidong/NanoSTR.",
+                "authors": [
+                    {
+                        "name": "Lang J."
+                    },
+                    {
+                        "name": "Sun J."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Xu Z."
+                    },
+                    {
+                        "name": "Yang Z."
+                    }
+                ],
+                "date": "2023-01-18T00:00:00Z",
+                "journal": "Frontiers in Molecular Biosciences",
+                "title": "NanoSTR: A method for detection of target short tandem repeats based on nanopore sequencing data"
+            },
+            "pmcid": "PMC9889824",
+            "pmid": "36743210"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/nanotube/nanotube.biotools.json b/data/nanotube/nanotube.biotools.json
new file mode 100644
index 0000000000000..c4b08ebcc219c
--- /dev/null
+++ b/data/nanotube/nanotube.biotools.json
@@ -0,0 +1,139 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-04T00:36:42.581613Z",
+    "biotoolsCURIE": "biotools:nanotube",
+    "biotoolsID": "nanotube",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "cclass@butler.edu",
+            "name": "Caleb A Class",
+            "orcidid": "https://orcid.org/0000-0003-3130-3613",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Caiden J Lukan"
+        },
+        {
+            "name": "Christopher A Bristow"
+        },
+        {
+            "name": "Kim-Anh Do"
+        }
+    ],
+    "description": "NanoTube performs data processing, quality control, normalization and analysis on NanoString gene expression data.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "http://www.bioconductor.org/packages/release/bioc/manuals/NanoTube/man/NanoTube.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://research.butler.edu/nanotube/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-04T00:36:42.584195Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/calebclass/Shiny-NanoTube"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://www.bioconductor.org/packages/NanoTube/"
+        }
+    ],
+    "name": "NanoTube",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC762",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: The NanoTube is an open-source pipeline that simplifies the processing, quality control, normalization and analysis of NanoString nCounter gene expression data. It is implemented in an extensible R library, which performs a variety of gene expression analysis techniques and contains additional functions for integration with other R libraries performing advanced NanoString analysis techniques. Additionally, the NanoTube web application is available as a simple tool for researchers without programming expertise. AVAILABILITY AND IMPLEMENTATION: The NanoTube R package is available on Bioconductor under the GPL-3 license (https://www.bioconductor.org/packages/NanoTube/). The R-Shiny application can be downloaded at https://github.com/calebclass/Shiny-NanoTube, or a simplified version of this application can be run on all major browsers, at https://research.butler.edu/nanotube/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Bristow C.A."
+                    },
+                    {
+                        "name": "Class C.A."
+                    },
+                    {
+                        "name": "Do K.-A."
+                    },
+                    {
+                        "name": "Lukan C.J."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Easy NanoString nCounter data analysis with the NanoTube"
+            },
+            "pmcid": "PMC9805552",
+            "pmid": "36440915"
+        }
+    ],
+    "toolType": [
+        "Library",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/ndnet/ndnet.biotools.json b/data/ndnet/ndnet.biotools.json
new file mode 100644
index 0000000000000..3c9cc0379fe3f
--- /dev/null
+++ b/data/ndnet/ndnet.biotools.json
@@ -0,0 +1,99 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-04T00:30:11.358628Z",
+    "biotoolsCURIE": "biotools:ndnet",
+    "biotoolsID": "ndnet",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Chengju Liu",
+            "orcidid": "https://orcid.org/0000-0001-7543-0855"
+        },
+        {
+            "name": "Qijun Chen",
+            "orcidid": "https://orcid.org/0000-0001-5644-1188"
+        },
+        {
+            "name": "Qingqing Yan",
+            "orcidid": "https://orcid.org/0000-0002-3304-1584"
+        },
+        {
+            "name": "Shu Li",
+            "orcidid": "https://orcid.org/0000-0001-8225-5426"
+        }
+    ],
+    "description": "Spacewise Multiscale Representation Learning via Neighbor Decoupling for Real-Time Driving Scene Parsing.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/LiShuTJ/NDNet",
+    "language": [
+        "C++",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-04T00:30:11.361037Z",
+    "license": "MIT",
+    "name": "NDNet",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1109/TNNLS.2022.3221745",
+            "metadata": {
+                "abstract": "IEEEAs a safety-critical application, autonomous driving requires high-quality semantic segmentation and real-time performance for deployment. Existing method commonly suffers from information loss and massive computational burden due to high-resolution input-output and multiscale learning scheme, which runs counter to the real-time requirements. In contrast to channelwise information modeling commonly adopted by modern networks, in this article, we propose a novel real-time driving scene parsing framework named NDNet from a novel perspective of spacewise neighbor decoupling (ND) and neighbor coupling (NC). We first define and implement the reversible operations called ND and NC, which realize lossless resolution conversion for complementary thumbnails sampling and collation to facilitate spatial modeling. Based on ND and NC, we further propose three modules, namely, local capturer and global dependence builder (LCGB), spacewise multiscale feature extractor (SMFE), and high-resolution semantic generator (HSG), which form the whole pipeline of NDNet. The LCGB serves as a stem block to preprocess the large-scale input for fast but lossless resolution reduction and extract initial features with global context. Then the SMFE is used for dense feature extraction and can obtain rich multiscale features in spatial dimension with less computational overhead. As for high-resolution semantic output, the HSG is designed for fast resolution reconstruction and adaptive semantic confusion amending. Experiments show the superiority of the proposed method. NDNet achieves the state-of-the-art performance on the Cityscapes dataset which reports 76.47% mIoU at 240<inline-formula> <tex-math notation=\"LaTeX\">$+$</tex-math> </inline-formula> frames/s and 78.8% mIoU at 150<inline-formula> <tex-math notation=\"LaTeX\">$+$</tex-math> </inline-formula> frames/s on the benchmark. Codes are available at https://github.com/LiShuTJ/NDNet.",
+                "authors": [
+                    {
+                        "name": "Chen Q."
+                    },
+                    {
+                        "name": "Li S."
+                    },
+                    {
+                        "name": "Liu C."
+                    },
+                    {
+                        "name": "Wang D."
+                    },
+                    {
+                        "name": "Yan Q."
+                    },
+                    {
+                        "name": "Zhou X."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "IEEE Transactions on Neural Networks and Learning Systems",
+                "title": "NDNet: Spacewise Multiscale Representation Learning via Neighbor Decoupling for Real-Time Driving Scene Parsing"
+            },
+            "pmid": "36409808"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        }
+    ]
+}
diff --git a/data/nemar/nemar.biotools.json b/data/nemar/nemar.biotools.json
new file mode 100644
index 0000000000000..3d81645c5e7fb
--- /dev/null
+++ b/data/nemar/nemar.biotools.json
@@ -0,0 +1,123 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-04T00:24:26.950635Z",
+    "biotoolsCURIE": "biotools:nemar",
+    "biotoolsID": "nemar",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "arnodelorme@gmail.com",
+            "name": "Arnaud Delorme",
+            "orcidid": "https://orcid.org/0000-0002-0799-3557",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Amitava Majumdar"
+        },
+        {
+            "name": "Dung Truong"
+        },
+        {
+            "name": "Scott Makeig"
+        }
+    ],
+    "description": "An open access data, tools and compute resource operating on neuroelectromagnetic data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://NEMAR.org",
+    "lastUpdate": "2023-02-04T00:24:26.953203Z",
+    "name": "NEMAR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/DATABASE/BAAC096",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press.To preserve scientific data created by publicly and/or philanthropically funded research projects and to make it ready for exploitation using recent and ongoing advances in advanced and large-scale computational modeling methods, publicly available data must use in common, now-evolving standards for formatting, identifying and annotating should share data. The OpenNeuro.org archive, built first as a repository for magnetic resonance imaging data based on the Brain Imaging Data Structure formatting standards, aims to house and share all types of human neuroimaging data. Here, we present NEMAR.org, a web gateway to OpenNeuro data for human neuroelectromagnetic data. NEMAR allows users to search through, visually explore and assess the quality of shared electroencephalography (EEG), magnetoencephalography and intracranial EEG data and then to directly process selected data using high-performance computing resources of the San Diego Supercomputer Center via the Neuroscience Gateway (nsgportal.org, NSG), a freely available web portal to high-performance computing serving a variety of neuroscientific analysis environments and tools. Combined, OpenNeuro, NEMAR and NSG form an efficient, integrated data, tools and compute resource for human neuroimaging data analysis and meta-Analysis. Database URL: https://nemar.org",
+                "authors": [
+                    {
+                        "name": "Delorme A."
+                    },
+                    {
+                        "name": "Majumdar A."
+                    },
+                    {
+                        "name": "Makeig S."
+                    },
+                    {
+                        "name": "Poldrack R.A."
+                    },
+                    {
+                        "name": "Sivagnanam S."
+                    },
+                    {
+                        "name": "Stirm C."
+                    },
+                    {
+                        "name": "Truong D."
+                    },
+                    {
+                        "name": "Yoshimoto K."
+                    },
+                    {
+                        "name": "Youn C."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Database",
+                "title": "NEMAR: An open access data, tools and compute resource operating on neuroelectromagnetic data"
+            },
+            "pmcid": "PMC9650770",
+            "pmid": "36367313"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Computer science",
+            "uri": "http://edamontology.org/topic_3316"
+        },
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        }
+    ]
+}
diff --git a/data/nervestitcher/nervestitcher.biotools.json b/data/nervestitcher/nervestitcher.biotools.json
new file mode 100644
index 0000000000000..69e11874f964e
--- /dev/null
+++ b/data/nervestitcher/nervestitcher.biotools.json
@@ -0,0 +1,96 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-04T00:19:06.021348Z",
+    "biotoolsCURIE": "biotools:nervestitcher",
+    "biotoolsID": "nervestitcher",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "liguangxu@tiangong.edu.cn",
+            "name": "Guangxu Li",
+            "orcidid": "https://orcid.org/0000-0002-3242-1673"
+        },
+        {
+            "email": "litianyu@tiangong.edu.cn",
+            "name": "Tianyu Li",
+            "orcidid": "https://orcid.org/0000-0001-9556-7787"
+        },
+        {
+            "name": "Chen Zhang"
+        },
+        {
+            "name": "Fangting Li"
+        }
+    ],
+    "description": "Corneal confocal microscope images stitching with neural networks.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/LiTianYu6/NerveStitcher",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-04T00:19:06.024519Z",
+    "license": "Not licensed",
+    "name": "NerveStitcher",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106303",
+            "metadata": {
+                "abstract": "© 2022Corneal nerves are of great interest to clinicians and scientists due to their potential for the diagnosis of early neurological disorders. In vivo confocal microscopy (IVCM) has been used as a novel and reliable tool for observing and quantifying corneal sub-basal nerves. Creating a wide-field montage of the nerve plexus from a large amount of IVCM images facilitates the measurement of corneal nerve morphology. In this paper, we propose a fully automatic image stitching method using neural networks. Firstly, we extend a self-supervised point detector to find the feature points on IVCM images. Then a flexible points correspondence based on the attention mechanism is developed for partial assignment of image pair. The scattered IVCM images are consequently integrated and fused according to the local offsets. We experimented with our method on 30 sets of IVCM images. Compared to conventional methods, our method improves matching accuracy and significantly reduces processing time. And by calculating the morphological parameters of the corneal nerve for both single images and stitched images, our method can evaluate the corneal nerve of patients more accurately and reliably. The implemented code is available at https://github.com/LiTianYu6/NerveStitcher.",
+                "authors": [
+                    {
+                        "name": "Li F."
+                    },
+                    {
+                        "name": "Li G."
+                    },
+                    {
+                        "name": "Li T."
+                    },
+                    {
+                        "name": "Zhang C."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "NerveStitcher: Corneal confocal microscope images stitching with neural networks"
+            },
+            "pmid": "36435056"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Neurology",
+            "uri": "http://edamontology.org/topic_3334"
+        }
+    ]
+}
diff --git a/data/netanova/netanova.biotools.json b/data/netanova/netanova.biotools.json
new file mode 100644
index 0000000000000..658fb89265dc7
--- /dev/null
+++ b/data/netanova/netanova.biotools.json
@@ -0,0 +1,88 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T21:00:46.960204Z",
+    "biotoolsCURIE": "biotools:netanova",
+    "biotoolsID": "netanova",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "diane.duroux@uliege.be",
+            "name": "Diane Duroux",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Kristel Van Steen"
+        }
+    ],
+    "description": "Novel graph clustering technique with significance assessment via hierarchical ANOVA.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Dendrogram visualisation",
+                    "uri": "http://edamontology.org/operation_2938"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/DianeDuroux/netANOVA",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-27T21:00:46.962780Z",
+    "license": "MIT",
+    "name": "netANOVA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bib/bbad029",
+            "pmid": "36738256"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/netshy/netshy.biotools.json b/data/netshy/netshy.biotools.json
new file mode 100644
index 0000000000000..e7e89b17ce0ab
--- /dev/null
+++ b/data/netshy/netshy.biotools.json
@@ -0,0 +1,131 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T14:09:51.547231Z",
+    "biotoolsCURIE": "biotools:netshy",
+    "biotoolsID": "netshy",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "katerina.kechris@cuanschutz.edu",
+            "name": "Katerina J Kechris",
+            "orcidid": "https://orcid.org/0000-0002-3725-5459",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "thao.3.vu@cuanschutz.edu",
+            "name": "Thao Vu",
+            "orcidid": "https://orcid.org/0000-0001-5252-0006",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Elizabeth M Litkowski"
+        },
+        {
+            "name": "Farnoush Banaei-Kashani"
+        }
+    ],
+    "description": "Network summarization via a hybrid approach leveraging topological properties.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Principal component analysis",
+                    "uri": "http://edamontology.org/operation_3960"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/thaovu1/NetSHy",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-20T14:09:51.550091Z",
+    "license": "Not licensed",
+    "name": "NetSHy",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC818",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information. RESULTS: In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation. AVAILABILITY AND IMPLEMENTATION: R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Banaei-Kashani F."
+                    },
+                    {
+                        "name": "Bowler R.P."
+                    },
+                    {
+                        "name": "Kechris K.J."
+                    },
+                    {
+                        "name": "Lange L."
+                    },
+                    {
+                        "name": "Litkowski E.M."
+                    },
+                    {
+                        "name": "Liu W."
+                    },
+                    {
+                        "name": "Pratte K.A."
+                    },
+                    {
+                        "name": "Vu T."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "NetSHy: network summarization via a hybrid approach leveraging topological properties"
+            },
+            "pmcid": "PMC9831052",
+            "pmid": "36548341"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        }
+    ]
+}
diff --git a/data/nettcr-2.1/nettcr-2.1.biotools.json b/data/nettcr-2.1/nettcr-2.1.biotools.json
new file mode 100644
index 0000000000000..45af08aae9883
--- /dev/null
+++ b/data/nettcr-2.1/nettcr-2.1.biotools.json
@@ -0,0 +1,109 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-20T14:05:59.019745Z",
+    "biotoolsCURIE": "biotools:nettcr-2.1",
+    "biotoolsID": "nettcr-2.1",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "morni@dtu.dk",
+            "name": "Morten Nielsen",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alessandro Montemurro"
+        },
+        {
+            "name": "Leon Eyrich Jessen"
+        }
+    ],
+    "description": "NetTCR-2.1 predicts binding probability between a T-cell receptor (TCR) CDR loops and MHC-I peptides.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence",
+                        "uri": "http://edamontology.org/data_2044"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Loop modelling",
+                    "uri": "http://edamontology.org/operation_0481"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://services.healthtech.dtu.dk/service.php?NetTCR-2.1",
+    "lastUpdate": "2023-02-20T14:05:59.022773Z",
+    "name": "NetTCR-2.1",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FIMMU.2022.1055151",
+            "metadata": {
+                "abstract": "Copyright © 2022 Montemurro, Jessen and Nielsen.T cell receptors (TCR) define the specificity of T cells and are responsible for their interaction with peptide antigen targets presented in complex with major histocompatibility complex (MHC) molecules. Understanding the rules underlying this interaction hence forms the foundation for our understanding of basic adaptive immunology. Over the last decade, efforts have been dedicated to developing assays for high throughput identification of peptide-specific TCRs. Based on such data, several computational methods have been proposed for predicting the TCR-pMHC interaction. The general conclusion from these studies is that the prediction of TCR interactions with MHC-peptide complexes remains highly challenging. Several reasons form the basis for this including scarcity and quality of data, and ill-defined modeling objectives imposed by the high redundancy of the available data. In this work, we propose a framework for dealing with this redundancy, allowing us to address essential questions related to the modeling of TCR specificity including the use of peptide- versus pan-specific models, how to best define negative data, and the performance impact of integrating of CDR1 and 2 loops. Further, we illustrate how and why it is strongly recommended to include simple similarity-based modeling approaches when validating an improved predictive power of machine learning models, and that such validation should include a performance evaluation as a function of “distance” to the training data, to quantify the potential for generalization of the proposed model. The conclusion of the work is that, given current data, TCR specificity is best modeled using peptide-specific approaches, integrating information from all 6 CDR loops, and with negative data constructed from a combination of true and mislabeled negatives. Comparing such machine learning models to similarity-based approaches demonstrated an increased performance gain of the former as the “distance” to the training data was increased; thus demonstrating an improved generalization ability of the machine learning-based approaches. We believe these results demonstrate that the outlined modeling framework and proposed evaluation strategy form a solid basis for investigating the modeling of TCR specificities and that adhering to such a framework will allow for faster progress within the field. The final devolved model, NetTCR-2.1, is available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.1.",
+                "authors": [
+                    {
+                        "name": "Jessen L.E."
+                    },
+                    {
+                        "name": "Montemurro A."
+                    },
+                    {
+                        "name": "Nielsen M."
+                    }
+                ],
+                "date": "2022-12-06T00:00:00Z",
+                "journal": "Frontiers in Immunology",
+                "title": "NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions"
+            },
+            "pmcid": "PMC9763291",
+            "pmid": "36561755"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Immunogenetics",
+            "uri": "http://edamontology.org/topic_3930"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/neuroppred-svm/neuroppred-svm.biotools.json b/data/neuroppred-svm/neuroppred-svm.biotools.json
new file mode 100644
index 0000000000000..2477b8c371282
--- /dev/null
+++ b/data/neuroppred-svm/neuroppred-svm.biotools.json
@@ -0,0 +1,93 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T23:04:07.885124Z",
+    "biotoolsCURIE": "biotools:neuroppred-svm",
+    "biotoolsID": "neuroppred-svm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Shuyu Wang"
+        },
+        {
+            "name": "Xiang Li"
+        },
+        {
+            "name": "Yinbo Liu"
+        },
+        {
+            "name": "Yufeng Liu"
+        },
+        {
+            "name": "Xiaolei Zhu",
+            "orcidid": "https://orcid.org/0000-0002-1967-2806"
+        }
+    ],
+    "description": "A New Model for Predicting Neuropeptides Based on Embeddings of BERT.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/liuyf-a/NeuroPpred-SVM",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T23:04:07.925917Z",
+    "license": "Not licensed",
+    "name": "NeuroPpred-SVM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JPROTEOME.2C00363",
+            "metadata": {
+                "abstract": "Neuropeptides play pivotal roles in different physiological processes and are related to different kinds of diseases. Identification of neuropeptides is of great benefit for studying the mechanism of these physiological processes and the treatment of neurological disorders. Several state-of-the-art neuropeptide predictors have been developed by using a two-layer stacking ensemble algorithm. Although the two-layer stacking ensemble algorithm can improve the feature representability, these models are complex, which are not as efficient as the models based on one classifier. In this study, we proposed a new model, NeuroPpred-SVM, to predict neuropeptides based on the embeddings of Bidirectional Encoder Representations from Transformers and other sequential features by using a support vector machine (SVM). The experimental results indicate that our model achieved a cross-validation area under the receiver operating characteristic (AUROC) curve of 0.969 on the training data set and an AUROC of 0.966 on the independent test set. By comparing our model with the other four state-of-the-art models including NeuroPIpred, PredNeuroP, NeuroPpred-Fuse, and NeuroPpred-FRL on the independent test set, our model achieved the highest AUROC, Matthews correlation coefficient, accuracy, and specificity, which indicate that our model outperforms the existing models. We believed that NeuroPpred-SVM could be a useful tool for identifying neuropeptides with high accuracy and low cost. The data sets and Python code are available at https://github.com/liuyf-a/NeuroPpred-SVM.",
+                "authors": [
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Zhu X."
+                    }
+                ],
+                "date": "2023-03-03T00:00:00Z",
+                "journal": "Journal of proteome research",
+                "title": "NeuroPpred-SVM: A New Model for Predicting Neuropeptides Based on Embeddings of BERT"
+            },
+            "pmid": "36749151"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Neurology",
+            "uri": "http://edamontology.org/topic_3334"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/niapu/niapu.biotools.json b/data/niapu/niapu.biotools.json
new file mode 100644
index 0000000000000..159d04bc7ab8e
--- /dev/null
+++ b/data/niapu/niapu.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T22:57:25.269347Z",
+    "biotoolsCURIE": "biotools:niapu",
+    "biotoolsID": "niapu",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mastropietro@diag.uniroma1.it",
+            "name": "Andrea Mastropietro",
+            "orcidid": "https://orcid.org/0000-0002-3456-9428",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "davide.vergni@cnr.it",
+            "name": "Davide Vergni",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Giuseppe Pasculli"
+        },
+        {
+            "name": "Paola Stolfi"
+        },
+        {
+            "name": "Paolo Tieri",
+            "orcidid": "https://orcid.org/0000-0002-3635-7664"
+        }
+    ],
+    "description": "Network-informed adaptive positive-unlabeled learning for disease gene identification.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Enrichment analysis",
+                    "uri": "http://edamontology.org/operation_3501"
+                },
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Gene prediction",
+                    "uri": "http://edamontology.org/operation_2454"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/AndMastro/NIAPU",
+    "language": [
+        "C",
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T22:57:25.274645Z",
+    "license": "MIT",
+    "name": "NIAPU",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC848",
+            "metadata": {
+                "abstract": "MOTIVATION: Gene-disease associations are fundamental for understanding disease etiology and developing effective interventions and treatments. Identifying genes not yet associated with a disease due to a lack of studies is a challenging task in which prioritization based on prior knowledge is an important element. The computational search for new candidate disease genes may be eased by positive-unlabeled learning, the machine learning (ML) setting in which only a subset of instances are labeled as positive while the rest of the dataset is unlabeled. In this work, we propose a set of effective network-based features to be used in a novel Markov diffusion-based multi-class labeling strategy for putative disease gene discovery. RESULTS: The performances of the new labeling algorithm and the effectiveness of the proposed features have been tested on 10 different disease datasets using three ML algorithms. The new features have been compared against classical topological and functional/ontological features and a set of network- and biological-derived features already used in gene discovery tasks. The predictive power of the integrated methodology in searching for new disease genes has been found to be competitive against state-of-the-art algorithms. AVAILABILITY AND IMPLEMENTATION: The source code of NIAPU can be accessed at https://github.com/AndMastro/NIAPU. The source data used in this study are available online on the respective websites. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Mastropietro A."
+                    },
+                    {
+                        "name": "Pasculli G."
+                    },
+                    {
+                        "name": "Stolfi P."
+                    },
+                    {
+                        "name": "Tieri P."
+                    },
+                    {
+                        "name": "Vergni D."
+                    }
+                ],
+                "date": "2023-02-03T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification"
+            },
+            "pmcid": "PMC9933847",
+            "pmid": "36727493"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/nlm-chem-bc7/nlm-chem-bc7.biotools.json b/data/nlm-chem-bc7/nlm-chem-bc7.biotools.json
new file mode 100644
index 0000000000000..095ce417b9353
--- /dev/null
+++ b/data/nlm-chem-bc7/nlm-chem-bc7.biotools.json
@@ -0,0 +1,146 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-17T13:17:27.058896Z",
+    "biotoolsCURIE": "biotools:nlm-chem-bc7",
+    "biotoolsID": "nlm-chem-bc7",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhiyong.lu@nih.gov",
+            "name": "Zhiyong Lu",
+            "orcidid": "https://orcid.org/0000-0001-9998-916X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Robert Leaman"
+        },
+        {
+            "name": "Susan Schmidt"
+        },
+        {
+            "name": "Rezarta Islamaj",
+            "orcidid": "https://orcid.org/0000-0001-5651-1860"
+        }
+    ],
+    "description": "Manually annotated full-text resources for chemical entity annotation and indexing in biomedical articles.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Named-entity and concept recognition",
+                    "uri": "http://edamontology.org/operation_3280"
+                },
+                {
+                    "term": "Text annotation",
+                    "uri": "http://edamontology.org/operation_3778"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ftp.ncbi.nlm.nih.gov/pub/lu/NLM-Chem-BC7-corpus/",
+    "lastUpdate": "2023-02-17T13:17:27.061532Z",
+    "name": "NLM-Chem-BC7",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/DATABASE/BAAC102",
+            "metadata": {
+                "abstract": "© 2022 Published by Oxford University Press. This work is written by (a) US Government employee(s) and is in the public domain in the US.The automatic recognition of chemical names and their corresponding database identifiers in biomedical text is an important first step for many downstream text-mining applications. The task is even more challenging when considering the identification of these entities in the article's full text and, furthermore, the identification of candidate substances for that article's metadata [Medical Subject Heading (MeSH) article indexing]. The National Library of Medicine (NLM)-Chem track at BioCreative VII aimed to foster the development of algorithms that can predict with high quality the chemical entities in the biomedical literature and further identify the chemical substances that are candidates for article indexing. As a result of this challenge, the NLM-Chem track produced two comprehensive, manually curated corpora annotated with chemical entities and indexed with chemical substances: The chemical identification corpus and the chemical indexing corpus. The NLM-Chem BioCreative VII (NLM-Chem-BC7) Chemical Identification corpus consists of 204 full-Text PubMed Central (PMC) articles, fully annotated for chemical entities by 12 NLM indexers for both span (i.e. named entity recognition) and normalization (i.e. entity linking) using MeSH. This resource was used for the training and testing of the Chemical Identification task to evaluate the accuracy of algorithms in predicting chemicals mentioned in recently published full-Text articles. The NLM-Chem-BC7 Chemical Indexing corpus consists of 1333 recently published PMC articles, equipped with chemical substance indexing by manual experts at the NLM. This resource was used for the evaluation of the Chemical Indexing task, which evaluated the accuracy of algorithms in predicting the chemicals that should be indexed, i.e. appear in the listing of MeSH terms for the document. This set was further enriched after the challenge in two ways: (i) 11 NLM indexers manually verified each of the candidate terms appearing in the prediction results of the challenge participants, but not in the MeSH indexing, and the chemical indexing terms appearing in the MeSH indexing list, but not in the prediction results, and (ii) the challenge organizers algorithmically merged the chemical entity annotations in the full text for all predicted chemical entities and used a statistical approach to keep those with the highest degree of confidence. As a result, the NLM-Chem-BC7 Chemical Indexing corpus is a gold-standard corpus for chemical indexing of journal articles and a silver-standard corpus for chemical entity identification in full-Text journal articles. Together, these resources are currently the most comprehensive resources for chemical entity recognition, and we demonstrate improvements in the chemical entity recognition algorithms. We detail the characteristics of these novel resources and make them available for the community. Database URL: https://ftp.ncbi.nlm.nih.gov/pub/lu/NLM-Chem-BC7-corpus/",
+                "authors": [
+                    {
+                        "name": "Cissel D."
+                    },
+                    {
+                        "name": "Coss C."
+                    },
+                    {
+                        "name": "Denicola J."
+                    },
+                    {
+                        "name": "Fisher C."
+                    },
+                    {
+                        "name": "Guzman R."
+                    },
+                    {
+                        "name": "Islamaj R."
+                    },
+                    {
+                        "name": "Kochar P.G."
+                    },
+                    {
+                        "name": "Leaman R."
+                    },
+                    {
+                        "name": "Lu Z."
+                    },
+                    {
+                        "name": "Miliaras N."
+                    },
+                    {
+                        "name": "Punske Z."
+                    },
+                    {
+                        "name": "Schmidt S."
+                    },
+                    {
+                        "name": "Sekiya K."
+                    },
+                    {
+                        "name": "Trinh D."
+                    },
+                    {
+                        "name": "Whitman D."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Database",
+                "title": "NLM-Chem-BC7: Manually annotated full-Text resources for chemical entity annotation and indexing in biomedical articles"
+            },
+            "pmcid": "PMC9716560",
+            "pmid": "36458799"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Biochemistry",
+            "uri": "http://edamontology.org/topic_3292"
+        },
+        {
+            "term": "Compound libraries and screening",
+            "uri": "http://edamontology.org/topic_3343"
+        },
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Toxicology",
+            "uri": "http://edamontology.org/topic_2840"
+        }
+    ]
+}
diff --git a/data/nlrscape/nlrscape.biotools.json b/data/nlrscape/nlrscape.biotools.json
new file mode 100644
index 0000000000000..ba3f8599a0193
--- /dev/null
+++ b/data/nlrscape/nlrscape.biotools.json
@@ -0,0 +1,93 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-03T00:21:02.052207Z",
+    "biotoolsCURIE": "biotools:nlrscape",
+    "biotoolsID": "nlrscape",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "andrei.petrescu@biochim.ro",
+            "name": "Andrei-J Petrescu",
+            "orcidid": "https://orcid.org/0000-0002-4478-3946",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Catalin F Ion"
+        },
+        {
+            "name": "Eliza C Martin"
+        },
+        {
+            "name": "Aska Goverse",
+            "orcidid": "https://orcid.org/0000-0002-7399-8743"
+        }
+    ],
+    "description": "NLRscape is an interactive atlas of plant NLRs equipped with a collection of easy, ready to use bioinformatic tools aimed for the exploration of the complex sequence landscape of this class of receptors in plants.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "Sequence clustering",
+                    "uri": "http://edamontology.org/operation_0291"
+                },
+                {
+                    "term": "Structure clustering",
+                    "uri": "http://edamontology.org/operation_2844"
+                },
+                {
+                    "term": "Taxonomic classification",
+                    "uri": "http://edamontology.org/operation_3460"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://nlrscape.biochim.ro/",
+    "lastUpdate": "2023-02-03T00:21:02.054627Z",
+    "name": "NLRscape",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1014",
+            "pmcid": "PMC9825502",
+            "pmid": "36350627"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene and protein families",
+            "uri": "http://edamontology.org/topic_0623"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Taxonomy",
+            "uri": "http://edamontology.org/topic_0637"
+        }
+    ]
+}
diff --git a/data/nmrtist/nmrtist.biotools.json b/data/nmrtist/nmrtist.biotools.json
new file mode 100644
index 0000000000000..05b02bba1ec3d
--- /dev/null
+++ b/data/nmrtist/nmrtist.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T22:44:07.783355Z",
+    "biotoolsCURIE": "biotools:nmrtist",
+    "biotoolsID": "nmrtist",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "peter.guentert@phys.chem.ethz.ch",
+            "name": "Peter Güntert",
+            "orcidid": "https://orcid.org/0000-0002-2911-7574",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "piotr.klukowski@phys.chem.ethz.ch",
+            "name": "Piotr Klukowski",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "roland.riek@phys.chem.ethz.ch",
+            "name": "Roland Riek",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An online platform for automated biomolecular NMR spectra analysis.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Peak detection",
+                    "uri": "http://edamontology.org/operation_3215"
+                },
+                {
+                    "term": "Protein structure assignment",
+                    "uri": "http://edamontology.org/operation_0320"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://nmrtist.org",
+    "lastUpdate": "2023-03-18T22:44:07.788692Z",
+    "name": "NMRtist",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD066",
+            "metadata": {
+                "abstract": "SUMMARY: We present NMRtist, an online platform that combines deep learning, large-scale optimization and cloud computing to automate protein NMR spectra analysis. Our website provides virtual storage for NMR spectra deposition together with a set of applications designed for automated peak picking, chemical shift assignment and protein structure determination. The system can be used by non-experts and allows protein assignments and structures to be determined within hours after the measurements, strictly without any human intervention. AVAILABILITY AND IMPLEMENTATION: NMRtist is freely available to non-commercial users at https://nmrtist.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Guntert P."
+                    },
+                    {
+                        "name": "Klukowski P."
+                    },
+                    {
+                        "name": "Riek R."
+                    }
+                ],
+                "date": "2023-02-03T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "NMRtist: an online platform for automated biomolecular NMR spectra analysis"
+            },
+            "pmcid": "PMC9913044",
+            "pmid": "36723167"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        },
+        {
+            "term": "Protein structure analysis",
+            "uri": "http://edamontology.org/topic_2814"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/norfs/norfs.biotools.json b/data/norfs/norfs.biotools.json
new file mode 100644
index 0000000000000..ae6cf278c44f8
--- /dev/null
+++ b/data/norfs/norfs.biotools.json
@@ -0,0 +1,131 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T01:16:29.490166Z",
+    "biotoolsCURIE": "biotools:norfs",
+    "biotoolsID": "norfs",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Matthew Mort"
+        },
+        {
+            "name": "Sudhakaran Prabakaran"
+        },
+        {
+            "name": "Matthew D.C. Neville",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Robin Kohze",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A platform for curated products from novel open reading frames prompts reinterpretation of disease variants.",
+    "documentation": [
+        {
+            "type": [
+                "API documentation"
+            ],
+            "url": "https://norfs.org/api"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Coding region prediction",
+                    "uri": "http://edamontology.org/operation_0436"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "Variant filtering",
+                    "uri": "http://edamontology.org/operation_3675"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://nORFs.org",
+    "lastUpdate": "2023-01-27T01:16:29.492820Z",
+    "name": "nORFs",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1101/GR.263202.120",
+            "metadata": {
+                "abstract": "© 2021 Neville et al.Recent evidence from proteomics and deep massively parallel sequencing studies have revealed that eukaryotic genomes contain substantial numbers of as-yet-uncharacterized open reading frames (ORFs). We define these uncharacterized ORFs as novel ORFs (nORFs). nORFs in humans are mostly under 100 codons and are found in diverse regions of the genome, including in long noncoding RNAs, pseudogenes, 3′ UTRs, 5′ UTRs, and alternative reading frames of canonical protein coding exons. There is therefore a pressing need to evaluate the potential functional importance of these unannotated transcripts and proteins in biological pathways and human disease on a larger scale, rather than one at a time. In this study, we outline the creation of a valuable nORFs data set with experimental evidence of translation for the community, use measures of heritability and selection that reveal signals for functional importance, and show the potential implications for functional interpretation of genetic variants in nORFs. Our results indicate that some variants that were previously classified as being benign or of uncertain significance may have to be reinterpreted.",
+                "authors": [
+                    {
+                        "name": "Cooper D.N."
+                    },
+                    {
+                        "name": "Erady C."
+                    },
+                    {
+                        "name": "Hayden M."
+                    },
+                    {
+                        "name": "Kohze R."
+                    },
+                    {
+                        "name": "Matthew D.C. Neville"
+                    },
+                    {
+                        "name": "Meena N."
+                    },
+                    {
+                        "name": "Mort M."
+                    },
+                    {
+                        "name": "Prabakaran S."
+                    }
+                ],
+                "citationCount": 8,
+                "date": "2021-01-01T00:00:00Z",
+                "journal": "Genome Research",
+                "title": "A platform for curated products from novel open reading frames prompts reinterpretation of disease variants"
+            },
+            "pmcid": "PMC7849405",
+            "pmid": "33468550"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        }
+    ]
+}
diff --git a/data/npgreat/npgreat.biotools.json b/data/npgreat/npgreat.biotools.json
new file mode 100644
index 0000000000000..724e539b71a05
--- /dev/null
+++ b/data/npgreat/npgreat.biotools.json
@@ -0,0 +1,105 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-17T13:10:47.533024Z",
+    "biotoolsCURIE": "biotools:npgreat",
+    "biotoolsID": "npgreat",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "eadam002@odu.edu",
+            "name": "Eleni Adam",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Desh Ranjan"
+        },
+        {
+            "name": "Harold Riethman"
+        }
+    ],
+    "description": "Assembly of human subtelomere regions with the use of ultralong nanopore reads and linked-reads.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/eleniadam/npgreat/blob/main/manual_npgreat.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/eleniadam/npgreat",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-17T13:10:47.535527Z",
+    "license": "Not licensed",
+    "name": "NPGREAT",
+    "operatingSystem": [
+        "Linux",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05081-3",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Human subtelomeric DNA regulates the length and stability of adjacent telomeres that are critical for cellular function, and contains many gene/pseudogene families. Large evolutionarily recent segmental duplications and associated structural variation in human subtelomeres has made complete sequencing and assembly of these regions difficult to impossible for many loci, complicating or precluding a wide range of genetic analyses to investigate their function. Results: We present a hybrid assembly method, NanoPore Guided REgional Assembly Tool (NPGREAT), which combines Linked-Read data with mapped ultralong nanopore reads spanning subtelomeric segmental duplications to potentially overcome these difficulties. Linked-Read sets of DNA sequences identified by matches with 1-copy subtelomere sequence adjacent to segmental duplications are assembled and extended into the segmental duplication regions using Regional Extension of Assemblies using Linked-Reads (REXTAL). Mapped telomere-containing ultralong nanopore reads are then used to provide contiguity and correct orientation for matching REXTAL sequence contigs as well as identification/correction of any misassemblies. Our method was tested for a subset of representative subtelomeres with ultralong nanopore read coverage in the haploid human cell line CHM13. A 10X Linked-Read dataset from CHM13 was combined with ultralong nanopore reads from the same genome to provide improved subtelomere assemblies. Comparison of Nanopore-only assemblies using SHASTA with our NPGREAT assemblies in the distal-most subtelomere regions showed that NPGREAT produced higher-quality and more complete assemblies than SHASTA alone when these regions had low ultralong nanopore coverage (such as cases where large segmental duplications were immediately adjacent to (TTAGGG) tracts). Conclusion: In genomic regions with large segmental duplications adjacent to telomeres, NPGREAT offers an alternative economical approach to improving assembly accuracy and coverage using linked-read datasets when more expensive HiFi datasets of 10–20 kb reads are unavailable.",
+                "authors": [
+                    {
+                        "name": "Adam E."
+                    },
+                    {
+                        "name": "Ranjan D."
+                    },
+                    {
+                        "name": "Riethman H."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "NPGREAT: assembly of human subtelomere regions with the use of ultralong nanopore reads and linked-reads"
+            },
+            "pmcid": "PMC9758922",
+            "pmid": "36526983"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/nrn-ez/nrn-ez.biotools.json b/data/nrn-ez/nrn-ez.biotools.json
new file mode 100644
index 0000000000000..16b087ca41369
--- /dev/null
+++ b/data/nrn-ez/nrn-ez.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T20:55:30.101433Z",
+    "biotoolsCURIE": "biotools:nrn-ez",
+    "biotoolsID": "nrn-ez",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ascimemi@albany.edu",
+            "name": "Annalisa Scimemi",
+            "orcidid": "http://orcid.org/0000-0003-4975-093X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Evan A. Cobb"
+        },
+        {
+            "name": "Maurice A. Petroccione"
+        }
+    ],
+    "description": "An application to streamline biophysical modeling of synaptic integration using NEURON.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Neurite measurement",
+                    "uri": "http://edamontology.org/operation_3450"
+                },
+                {
+                    "term": "Statistical calculation",
+                    "uri": "http://edamontology.org/operation_2238"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/scimemia/NRN-EZ",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-27T20:55:30.104132Z",
+    "license": "GPL-3.0",
+    "name": "NRN-EZ",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/s41598-022-27302-8",
+            "metadata": {
+                "abstract": "© 2023, The Author(s).One of the fundamental goals in neuroscience is to determine how the brain processes information and ultimately controls the execution of complex behaviors. Over the past four decades, there has been a steady growth in our knowledge of the morphological and functional diversity of neurons, the building blocks of the brain. These cells clearly differ not only for their anatomy and ion channel distribution, but also for the type, strength, location, and temporal pattern of activity of the many synaptic inputs they receive. Compartmental modeling programs like NEURON have become widely used in the neuroscience community to address a broad range of research questions, including how neurons integrate synaptic inputs and propagate information through complex neural networks. One of the main strengths of NEURON is its ability to incorporate user-defined information about the realistic morphology and biophysical properties of different cell types. Although the graphical user interface of the program can be used to run initial exploratory simulations, introducing a stochastic representation of synaptic weights, locations and activation times typically requires users to develop their own codes, a task that can be overwhelming for some beginner users. Here we describe NRN-EZ, an interactive application that allows users to specify complex patterns of synaptic input activity that can be integrated as part of NEURON simulations. Through its graphical user interface, NRN-EZ aims to ease the learning curve to run computational models in NEURON, for users that do not necessarily have a computer science background.",
+                "authors": [
+                    {
+                        "name": "Cobb E.A.W."
+                    },
+                    {
+                        "name": "Petroccione M.A."
+                    },
+                    {
+                        "name": "Scimemi A."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "NRN-EZ: an application to streamline biophysical modeling of synaptic integration using NEURON"
+            },
+            "pmcid": "PMC9832141",
+            "pmid": "36627356"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Anatomy",
+            "uri": "http://edamontology.org/topic_3067"
+        },
+        {
+            "term": "Biophysics",
+            "uri": "http://edamontology.org/topic_3306"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        }
+    ]
+}
diff --git a/data/nspa/nspa.biotools.json b/data/nspa/nspa.biotools.json
new file mode 100644
index 0000000000000..4cfbd0f84e1fa
--- /dev/null
+++ b/data/nspa/nspa.biotools.json
@@ -0,0 +1,94 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T22:40:43.663215Z",
+    "biotoolsCURIE": "biotools:nspa",
+    "biotoolsID": "nspa",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ting.hu@queensu.ca",
+            "name": "Ting Hu",
+            "orcidid": "https://orcid.org/0000-0001-6382-0602",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Yuanzhu Chen"
+        },
+        {
+            "name": "Zhendong Sha",
+            "orcidid": "https://orcid.org/0000-0002-7694-1099"
+        }
+    ],
+    "description": "Characterizing the disease association of multiple genetic interactions at single-subject resolution.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Genetic mapping",
+                    "uri": "http://edamontology.org/operation_0282"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/MIB-Lab/Network-based-Subject-Portrait-Approach",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T22:40:43.668376Z",
+    "license": "MIT",
+    "name": "NSPA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOADV/VBAD010",
+            "pmcid": "PMC9927570",
+            "pmid": "36818729"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Epistasis",
+            "uri": "http://edamontology.org/topic_3974"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/ntd_health/ntd_health.biotools.json b/data/ntd_health/ntd_health.biotools.json
new file mode 100644
index 0000000000000..94f8cfe7cca15
--- /dev/null
+++ b/data/ntd_health/ntd_health.biotools.json
@@ -0,0 +1,103 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-17T13:04:17.920559Z",
+    "biotoolsCURIE": "biotools:ntd_health",
+    "biotoolsID": "ntd_health",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "rodrigo.ochoa@udea.edu.co",
+            "name": "Rodrigo Ochoa",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alessa Álvarez"
+        },
+        {
+            "name": "Iván D Vélez"
+        },
+        {
+            "name": "Jordan Freitas"
+        },
+        {
+            "name": "Saptarshi Purkayastha"
+        }
+    ],
+    "description": "An electronic medical record system for neglected tropical diseases.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "http://ubmc-pecet.udea.edu.co/ntdhealth/",
+    "lastUpdate": "2023-02-17T13:04:17.923298Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/rochoa85/NTDHealth_Forms"
+        }
+    ],
+    "name": "NTD Health",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.7705/BIOMEDICA.6269",
+            "metadata": {
+                "abstract": "© 2022, Biomedica. All Rights Reserved.Introduction: The use of technological resources to support processes in health systems has generated robust, interoperable and dynamic platforms. In the case of institutions working with neglected tropical diseases (NTD), there is a need for NTD-specific customizations. Objectives: To establish a medical records platform, specialized for NTD, which would facilitate the analysis of treatment evolution in patients, as well as generate more accurate data about various clinical aspects. Materials and methods: Here we developed a customized electronic medical record system based on OpenMRS for multiple NTDs. A set of forms and functionalities was developed under the OpenMRS guidelines, using shared community modules. Results: All the customized information was packaged in a distribution called NTD Health. The platform is web-based and can be upgraded and improved by users without technological barriers. Conclusions: The EMR system can become a useful tool for other institutions to improve their health practices as well as the quality of life for NTD patients, simplifying the customization of healthcare systems able to interoperate with other platforms.",
+                "authors": [
+                    {
+                        "name": "Alvarez A."
+                    },
+                    {
+                        "name": "Freitas J."
+                    },
+                    {
+                        "name": "Ochoa R."
+                    },
+                    {
+                        "name": "Purkayastha S."
+                    },
+                    {
+                        "name": "Velez I.D."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Biomedica",
+                "title": "NTD Health: an electronic medical record system for neglected tropical diseases NTD Health: un sistema de historias clínicas electrónicas para enfermedades tropicales desatendidas"
+            },
+            "pmcid": "PMC9788840",
+            "pmid": "36511677"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Medical informatics",
+            "uri": "http://edamontology.org/topic_3063"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        },
+        {
+            "term": "Tropical medicine",
+            "uri": "http://edamontology.org/topic_3575"
+        }
+    ]
+}
diff --git a/data/numpy/numpy.biotools.json b/data/numpy/numpy.biotools.json
new file mode 100644
index 0000000000000..60855e7cdeeed
--- /dev/null
+++ b/data/numpy/numpy.biotools.json
@@ -0,0 +1,66 @@
+{
+    "additionDate": "2023-01-31T07:50:31.650658Z",
+    "biotoolsCURIE": "biotools:numpy",
+    "biotoolsID": "numpy",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "url": "https://numpy.org/about/"
+        }
+    ],
+    "description": "The fundamental package for scientific computing with Python",
+    "documentation": [
+        {
+            "type": [
+                "Installation instructions",
+                "Release notes",
+                "User manual"
+            ],
+            "url": "https://numpy.org/doc/stable/user/absolute_beginners.html"
+        }
+    ],
+    "download": [
+        {
+            "type": "API specification",
+            "url": "https://numpy.org/doc/stable/reference/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Calculation",
+                    "uri": "http://edamontology.org/operation_3438"
+                },
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://numpy.org/",
+    "lastUpdate": "2023-02-01T12:54:07.870232Z",
+    "license": "BSD-3-Clause",
+    "link": [
+        {
+            "type": [
+                "Software catalogue"
+            ],
+            "url": "https://numpy.org/"
+        }
+    ],
+    "name": "NumPy",
+    "owner": "iacs-biocomputacion",
+    "toolType": [
+        "Library"
+    ],
+    "version": [
+        "1.24.0"
+    ]
+}
diff --git a/data/oakrootrnadb/oakrootrnadb.biotools.json b/data/oakrootrnadb/oakrootrnadb.biotools.json
new file mode 100644
index 0000000000000..e27e54a27d591
--- /dev/null
+++ b/data/oakrootrnadb/oakrootrnadb.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-03T00:13:37.404983Z",
+    "biotoolsCURIE": "biotools:oakrootrnadb",
+    "biotoolsID": "oakrootrnadb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "paulina.glazinska@umk.pl",
+            "name": "Paulina Kościelniak",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Marcin Zadworny"
+        },
+        {
+            "name": "Paulina Glazińska",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "The Pedunculate oak (Quercus robur) root database (OakRootRNADB) consolidates information currently available on RNA-seq research conducted on both coding and non-coding RNA. The database contains the sequences of genes, transcripts, proteins, and microRNA obtained from the meristematic and elongation zones of both taproot and lateral roots of Q. robur.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene name",
+                        "uri": "http://edamontology.org/data_2299"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "PCR primer design",
+                    "uri": "http://edamontology.org/operation_0308"
+                },
+                {
+                    "term": "miRNA expression analysis",
+                    "uri": "http://edamontology.org/operation_3792"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://oakrootrnadb.idpan.poznan.pl/",
+    "lastUpdate": "2023-02-03T00:13:37.407535Z",
+    "name": "OakRootRNADB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/DATABASE/BAAC097",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press.The degree to which roots elongate is determined by the expression of genes that regulate root growth in each developmental zone of a root. Most studies have, however, focused on the molecular factors that regulate primary root growth in annual plants. In contrast, the relationship between gene expression and a specific pattern of taproot development and growth in trees is poorly understood. However, the presence of a deeply located taproot, with branching lateral roots, can especially mitigate the effect of insufficient water availability in long-lived trees, such as pedunculated oak. In the present article, we integrated the ribonucleic acid (RNA) sequencing data on roots of oak trees into a single comprehensive database, named OakRootRNADB that contains information on both coding and noncoding RNAs. The sequences in the database also enclose information pertaining to transcription factors, transcriptional regulators and chromatin regulators, as well as a prediction of the cellular localization of a transcript. OakRootRNADB has a user-friendly interface and functional tools that increase access to genomic information. Integrated knowledge of molecular patterns of expression, specifically occurring within and between root zones and within root types, can elucidate the molecular mechanisms regulating taproot growth and enhanced root soil exploration. Database URL https://oakrootrnadb.idpan.poznan.pl/",
+                "authors": [
+                    {
+                        "name": "Glazinska P."
+                    },
+                    {
+                        "name": "Koscielniak P."
+                    },
+                    {
+                        "name": "Zadworny M."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Database",
+                "title": "OakRootRNADB-a consolidated RNA-seq database for coding and noncoding RNA in roots of pedunculate oak (Quercus robur)"
+            },
+            "pmcid": "PMC9670740",
+            "pmid": "36394419"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/octave/octave.biotools.json b/data/octave/octave.biotools.json
new file mode 100644
index 0000000000000..4ef5459e990e2
--- /dev/null
+++ b/data/octave/octave.biotools.json
@@ -0,0 +1,62 @@
+{
+    "additionDate": "2023-01-31T07:19:20.639286Z",
+    "biotoolsCURIE": "biotools:octave",
+    "biotoolsID": "octave",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "James B. Rawling,  John G. Ekerdt",
+            "url": "https://github.com/gnu-octave/gnu-octave.github.io"
+        }
+    ],
+    "description": "Scientific Programming Language\n\nPowerful mathematics-oriented syntax with built-in 2D/3D plotting and visualization tools\nFree software, runs on GNU/Linux, macOS, BSD, and Microsoft Windows\nDrop-in compatible with many Matlab scripts",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://docs.octave.org/latest/"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://octave.org/download"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                },
+                {
+                    "term": "Statistical calculation",
+                    "uri": "http://edamontology.org/operation_2238"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://octave.org/",
+    "lastUpdate": "2023-02-01T13:06:45.692602Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://octave.org/#"
+        }
+    ],
+    "name": "Octave",
+    "owner": "iacs-biocomputacion",
+    "version": [
+        "7.3.0 Nov 2, 2022"
+    ]
+}
diff --git a/data/odamnet/odamnet.biotools.json b/data/odamnet/odamnet.biotools.json
new file mode 100644
index 0000000000000..e3cf167b68d58
--- /dev/null
+++ b/data/odamnet/odamnet.biotools.json
@@ -0,0 +1,87 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-08T09:19:09.284884Z",
+    "biotoolsCURIE": "biotools:odamnet",
+    "biotoolsID": "odamnet",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "morgane.terezol@univ-amu.fr",
+            "name": "Morgane Térézol",
+            "orcidid": "https://orcid.org/0000-0002-4090-2573"
+        },
+        {
+            "name": "Anaïs Baudot",
+            "orcidid": "https://orcid.org/0000-0003-0885-7933"
+        },
+        {
+            "name": "Ozan Ozisik",
+            "orcidid": "https://orcid.org/0000-0001-5980-8002"
+        }
+    ],
+    "description": "A Python package to study molecular relationship between environmental factors and rare diseases.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://odamnet.readthedocs.io/en/latest/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "elixirCommunity": [
+        "Rare Diseases"
+    ],
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Protein interaction network analysis",
+                    "uri": "http://edamontology.org/operation_0276"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pypi.org/project/ODAMNet/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-08T10:21:51.610711Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/MOohTus/ODAMNet"
+        }
+    ],
+    "name": "ODAMNet",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "mterezol",
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Rare diseases",
+            "uri": "http://edamontology.org/topic_3325"
+        }
+    ]
+}
diff --git a/data/omicrexposome/omicrexposome.biotools.json b/data/omicrexposome/omicrexposome.biotools.json
index ef178f6144eca..06c353a768e09 100644
--- a/data/omicrexposome/omicrexposome.biotools.json
+++ b/data/omicrexposome/omicrexposome.biotools.json
@@ -11,10 +11,25 @@
         {
             "email": "carles.hernandez@isglobal.org",
             "name": "Carles Hernandez-Ferrer",
+            "orcidid": "https://orcid.org/0000-0002-8029-7160",
             "typeEntity": "Person",
             "typeRole": [
-                "Primary contact"
+                "Developer"
+            ],
+            "url": "http://www.carleshf.com"
+        },
+        {
+            "email": "xavier.escriba@isglobal.org",
+            "name": "Xavier Escribà Montagut",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Maintainer"
             ]
+        },
+        {
+            "email": "juanr.gonzalez@isglobal.org",
+            "name": "Juan R Gonzalez",
+            "url": "https://brge.isglobal.org/"
         }
     ],
     "description": "It systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA).",
@@ -34,6 +49,7 @@
     ],
     "editPermission": {
         "authors": [
+            "chernan3",
             "proteomics.bio.tools"
         ],
         "type": "group"
@@ -84,7 +100,7 @@
     "language": [
         "R"
     ],
-    "lastUpdate": "2019-03-26T08:28:33Z",
+    "lastUpdate": "2023-02-07T13:00:57.518759Z",
     "license": "MIT",
     "link": [
         {
@@ -101,6 +117,178 @@
         "Windows"
     ],
     "owner": "shadi.m",
+    "publication": [
+        {
+            "doi": "10.1038/s41467-022-34422-2",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Environmental exposures during early life play a critical role in life-course health, yet the molecular phenotypes underlying environmental effects on health are poorly understood. In the Human Early Life Exposome (HELIX) project, a multi-centre cohort of 1301 mother-child pairs, we associate individual exposomes consisting of >100 chemical, outdoor, social and lifestyle exposures assessed in pregnancy and childhood, with multi-omics profiles (methylome, transcriptome, proteins and metabolites) in childhood. We identify 1170 associations, 249 in pregnancy and 921 in childhood, which reveal potential biological responses and sources of exposure. Pregnancy exposures, including maternal smoking, cadmium and molybdenum, are predominantly associated with child DNA methylation changes. In contrast, childhood exposures are associated with features across all omics layers, most frequently the serum metabolome, revealing signatures for diet, toxic chemical compounds, essential trace elements, and weather conditions, among others. Our comprehensive and unique resource of all associations (https://helixomics.isglobal.org/) will serve to guide future investigation into the biological imprints of the early life exposome.",
+                "authors": [
+                    {
+                        "name": "Andrusaityte S."
+                    },
+                    {
+                        "name": "Borras E."
+                    },
+                    {
+                        "name": "Bustamante M."
+                    },
+                    {
+                        "name": "Cadiou S."
+                    },
+                    {
+                        "name": "Carracedo A."
+                    },
+                    {
+                        "name": "Casas M."
+                    },
+                    {
+                        "name": "Chatzi L."
+                    },
+                    {
+                        "name": "Coen M."
+                    },
+                    {
+                        "name": "Estivill X."
+                    },
+                    {
+                        "name": "Gonzalez J.R."
+                    },
+                    {
+                        "name": "Grazuleviciene R."
+                    },
+                    {
+                        "name": "Gutzkow K.B."
+                    },
+                    {
+                        "name": "Hernandez-Ferrer C."
+                    },
+                    {
+                        "name": "Heude B."
+                    },
+                    {
+                        "name": "Keun H.C."
+                    },
+                    {
+                        "name": "Lau C.-H.E."
+                    },
+                    {
+                        "name": "Maitre L."
+                    },
+                    {
+                        "name": "Mason D."
+                    },
+                    {
+                        "name": "Nieuwenhuijsen M."
+                    },
+                    {
+                        "name": "Papadopoulou E.Z."
+                    },
+                    {
+                        "name": "Pelegri-Siso D."
+                    },
+                    {
+                        "name": "Quintela I."
+                    },
+                    {
+                        "name": "Robinson O."
+                    },
+                    {
+                        "name": "Ruiz-Arenas C."
+                    },
+                    {
+                        "name": "Sabido E."
+                    },
+                    {
+                        "name": "Sakhi A.K."
+                    },
+                    {
+                        "name": "Siskos A.P."
+                    },
+                    {
+                        "name": "Slama R."
+                    },
+                    {
+                        "name": "Sunyer J."
+                    },
+                    {
+                        "name": "Tamayo I."
+                    },
+                    {
+                        "name": "Thiel D."
+                    },
+                    {
+                        "name": "Thomsen C."
+                    },
+                    {
+                        "name": "Urquiza J."
+                    },
+                    {
+                        "name": "Vafeiadi M."
+                    },
+                    {
+                        "name": "Vives-Usano M."
+                    },
+                    {
+                        "name": "Vrijheid M."
+                    },
+                    {
+                        "name": "Wright J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "Multi-omics signatures of the human early life exposome"
+            },
+            "pmcid": "PMC9678903",
+            "pmid": "36411288",
+            "type": [
+                "Usage"
+            ]
+        },
+        {
+            "doi": "10.1093/bioinformatics/btz526",
+            "metadata": {
+                "abstract": "© 2019 The Author(s). Published by Oxford University Press. All rights reserved.Summary: Genomics has dramatically improved our understanding of the molecular origins of certain human diseases. Nonetheless, our health is also influenced by the cumulative impact of exposures experienced across the life course (termed 'exposome'). The study of the high-dimensional exposome offers a new paradigm for investigating environmental contributions to disease etiology. However, there is a lack of bioinformatics tools for managing, visualizing and analyzing the exposome. The analysis data should include both association with health outcomes and integration with omic layers. We provide a generic framework called rexposome project, developed in the R/Bioconductor architecture that includes object-oriented classes and methods to leverage high-dimensional exposome data in disease association studies including its integration with a variety of high-throughput data types. The usefulness of the package is illustrated by analyzing a real dataset including exposome data, three health outcomes related to respiratory diseases and its integration with the transcriptome and methylome.",
+                "authors": [
+                    {
+                        "name": "Basagana X."
+                    },
+                    {
+                        "name": "Gonzalez J.R."
+                    },
+                    {
+                        "name": "Hernandez-Ferrer C."
+                    },
+                    {
+                        "name": "Sunyer J."
+                    },
+                    {
+                        "name": "Tamayo I."
+                    },
+                    {
+                        "name": "Vrijheid M."
+                    },
+                    {
+                        "name": "Wellenius G.A."
+                    }
+                ],
+                "citationCount": 10,
+                "date": "2019-12-15T00:00:00Z",
+                "journal": "Bioinformatics",
+                "title": "Comprehensive study of the exposome and omic data using rexposome Bioconductor Packages"
+            },
+            "pmid": "31243429",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "rexposome",
+            "type": "uses"
+        }
+    ],
     "toolType": [
         "Library"
     ],
diff --git a/data/omicsgat/omicsgat.biotools.json b/data/omicsgat/omicsgat.biotools.json
new file mode 100644
index 0000000000000..6a77dc74108f0
--- /dev/null
+++ b/data/omicsgat/omicsgat.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T12:50:13.399292Z",
+    "biotoolsCURIE": "biotools:omicsgat",
+    "biotoolsID": "omicsgat",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "wzhang.cs@ucf.edu",
+            "name": "Wei Zhang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Joseph Filipek"
+        },
+        {
+            "name": "Khandakar Tanvir Ahmed"
+        },
+        {
+            "name": "Sudipto Baul"
+        }
+    ],
+    "description": "omicsGAT is a graph attention network based framework for cancer subtype analysis. It performs the task of classification or clustering of patient/cell samples based on the gene expression. It strives to secure important information while discarding the rest by assigning different attention coefficients to the neighbors of a sample in a network/graph.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Expression profile clustering",
+                    "uri": "http://edamontology.org/operation_0313"
+                },
+                {
+                    "term": "RNA-Seq quantification",
+                    "uri": "http://edamontology.org/operation_3800"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/CompbioLabUCF/omicsGAT",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T12:50:13.401752Z",
+    "license": "Not licensed",
+    "name": "omicsGAT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/ijms231810220",
+            "metadata": {
+                "abstract": "© 2022 by the authors.The use of high-throughput omics technologies is becoming increasingly popular in all facets of biomedical science. The mRNA sequencing (RNA-seq) method reports quantitative measures of more than tens of thousands of biological features. It provides a more comprehensive molecular perspective of studied cancer mechanisms compared to traditional approaches. Graph-based learning models have been proposed to learn important hidden representations from gene expression data and network structure to improve cancer outcome prediction, patient stratification, and cell clustering. However, these graph-based methods cannot rank the importance of the different neighbors for a particular sample in the downstream cancer subtype analyses. In this study, we introduce omicsGAT, a graph attention network (GAT) model to integrate graph-based learning with an attention mechanism for RNA-seq data analysis. The multi-head attention mechanism in omicsGAT can more effectively secure information of a particular sample by assigning different attention coefficients to its neighbors. Comprehensive experiments on The Cancer Genome Atlas (TCGA) breast cancer and bladder cancer bulk RNA-seq data and two single-cell RNA-seq datasets validate that (1) the proposed model can effectively integrate neighborhood information of a sample and learn an embedding vector to improve disease phenotype prediction, cancer patient stratification, and cell clustering of the sample and (2) the attention matrix generated from the multi-head attention coefficients provides more useful information compared to the sample correlation-based adjacency matrix. From the results, we can conclude that some neighbors play a more important role than others in cancer subtype analyses of a particular sample based on the attention coefficient.",
+                "authors": [
+                    {
+                        "name": "Ahmed K.T."
+                    },
+                    {
+                        "name": "Baul S."
+                    },
+                    {
+                        "name": "Filipek J."
+                    },
+                    {
+                        "name": "Zhang W."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-09-01T00:00:00Z",
+                "journal": "International Journal of Molecular Sciences",
+                "title": "omicsGAT: Graph Attention Network for Cancer Subtype Analyses"
+            },
+            "pmcid": "PMC9499656",
+            "pmid": "36142140"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/oncopubminer/oncopubminer.biotools.json b/data/oncopubminer/oncopubminer.biotools.json
new file mode 100644
index 0000000000000..357e067b2a390
--- /dev/null
+++ b/data/oncopubminer/oncopubminer.biotools.json
@@ -0,0 +1,133 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T21:44:01.378518Z",
+    "biotoolsCURIE": "biotools:oncopubminer",
+    "biotoolsID": "oncopubminer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "niubf@cnic.cn",
+            "name": "Qiming Zhou",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "qimingzhou@chosenmedtech.com",
+            "name": "Beifang Niu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Quan Xu"
+        },
+        {
+            "name": "Yueyue Liu"
+        }
+    ],
+    "description": "A platform for oncology publication mining.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Literature search",
+                    "uri": "http://edamontology.org/operation_0305"
+                },
+                {
+                    "term": "Text annotation",
+                    "uri": "http://edamontology.org/operation_3778"
+                },
+                {
+                    "term": "Text mining",
+                    "uri": "http://edamontology.org/operation_0306"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://oncopubminer.chosenmedinfo.com",
+    "lastUpdate": "2023-01-17T21:44:01.380985Z",
+    "name": "OncoPubMiner",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bib/bbac383",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in-knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.",
+                "authors": [
+                    {
+                        "name": "Chen F."
+                    },
+                    {
+                        "name": "Duan X."
+                    },
+                    {
+                        "name": "Guo Z."
+                    },
+                    {
+                        "name": "Hu J."
+                    },
+                    {
+                        "name": "Li H."
+                    },
+                    {
+                        "name": "Liu S."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Niu B."
+                    },
+                    {
+                        "name": "Song N."
+                    },
+                    {
+                        "name": "Su J."
+                    },
+                    {
+                        "name": "Xu Q."
+                    },
+                    {
+                        "name": "Zhai J."
+                    },
+                    {
+                        "name": "Zheng W."
+                    },
+                    {
+                        "name": "Zhou J."
+                    },
+                    {
+                        "name": "Zhou Q."
+                    }
+                ],
+                "date": "2022-09-01T00:00:00Z",
+                "journal": "Briefings in Bioinformatics",
+                "title": "OncoPubMiner: a platform for mining oncology publications"
+            },
+            "pmid": "36058206"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/ontoparon/ontoparon.biotools.json b/data/ontoparon/ontoparon.biotools.json
new file mode 100644
index 0000000000000..487f5c1cd8f1d
--- /dev/null
+++ b/data/ontoparon/ontoparon.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T01:28:13.765448Z",
+    "biotoolsCURIE": "biotools:ontoparon",
+    "biotoolsID": "ontoparon",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jean.charlet@sorbonne-universite.fr",
+            "name": "Jean Charlet",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Use of a modular ontology and a semantic annotation tool to describe the care pathway of patients with amyotrophic lateral sclerosis in a coordination network.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Named-entity and concept recognition",
+                    "uri": "http://edamontology.org/operation_3280"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Text annotation",
+                    "uri": "http://edamontology.org/operation_3778"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://bioportal.bioontology.org/ontologies/ONTOPARON",
+    "lastUpdate": "2023-01-27T01:28:13.767929Z",
+    "name": "OntoPaRON",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PONE.0244604",
+            "metadata": {
+                "abstract": "Copyright: © 2021 Cardoso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The objective of this study was to describe the care pathway of patients with amyotrophic lateral sclerosis (ALS) based on real-life textual data from a regional coordination network, the Ile-de-France ALS network. This coordination network provides care for 92% of patients diagnosed with ALS living in Ile-de-France. We developed a modular ontology (OntoPaRON) for the automatic processing of these unstructured textual data. OntoPaRON has different modules: the core, medical, socio-environmental, coordination, and consolidation modules. Our approach was unique in its creation of fully defined concepts at different levels of the modular ontology to address specific topics relating to healthcare trajectories. We also created a semantic annotation tool specific to the French language and the specificities of our corpus, the Ontology-Based Semantic Annotation Module (OnBaSAM), using the OntoPaRON ontology as a reference. We used these tools to annotate the records of 928 patients automatically. The semantic (qualitative) annotations of the concepts were transformed into quantitative data. By using these pipelines we were able to transform unstructured textual data into structured quantitative data. Based on data processing, semantic annotations, sociodemographic data for the patient and clinical variables, we found that the need and demand for human and technical assistance depend on the initial form of the disease, the motor state, and the patient age. The presence of exhaustion in care management, is related to the patient’s motor and cognitive state.",
+                "authors": [
+                    {
+                        "name": "Aime X."
+                    },
+                    {
+                        "name": "Cardoso S."
+                    },
+                    {
+                        "name": "Charlet J."
+                    },
+                    {
+                        "name": "Grabli D."
+                    },
+                    {
+                        "name": "Guezennec G."
+                    },
+                    {
+                        "name": "Meininger V."
+                    },
+                    {
+                        "name": "Meneton P."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2021-01-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "Use of a modular ontology and a semantic annotation tool to describe the care pathway of patients with amyotrophic lateral sclerosis in a coordination network"
+            },
+            "pmcid": "PMC7787442",
+            "pmid": "33406098"
+        }
+    ],
+    "toolType": [
+        "Ontology"
+    ],
+    "topic": [
+        {
+            "term": "Medical informatics",
+            "uri": "http://edamontology.org/topic_3063"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/open_targets_platform/open_targets_platform.biotools.json b/data/open_targets_platform/open_targets_platform.biotools.json
index 62a61be18b548..fbef78780af7a 100644
--- a/data/open_targets_platform/open_targets_platform.biotools.json
+++ b/data/open_targets_platform/open_targets_platform.biotools.json
@@ -64,7 +64,7 @@
         "UK"
     ],
     "homepage": "https://platform.opentargets.org",
-    "lastUpdate": "2022-01-14T12:27:10.469607Z",
+    "lastUpdate": "2023-02-04T00:10:58.321772Z",
     "license": "Apache-2.0",
     "link": [
         {
@@ -97,6 +97,121 @@
     "name": "Open Targets Platform",
     "owner": "opentargets",
     "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1046",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.The Open Targets Platform (https://platform.opentargets.org/) is an open source resource to systematically assist drug target identification and prioritisation using publicly available data. Since our last update, we have reimagined, redesigned, and rebuilt the Platform in order to streamline data integration and harmonisation, expand the ways in which users can explore the data, and improve the user experience. The gene-disease causal evidence has been enhanced and expanded to better capture disease causality across rare, common, and somatic diseases. For target and drug annotations, we have incorporated new features that help assess target safety and tractability, including genetic constraint, PROTACtability assessments, and AlphaFold structure predictions. We have also introduced new machine learning applications for knowledge extraction from the published literature, clinical trial information, and drug labels. The new technologies and frameworks introduced since the last update will ease the introduction of new features and the creation of separate instances of the Platform adapted to user requirements. Our new Community forum, expanded training materials, and outreach programme support our users in a range of use cases.",
+                "authors": [
+                    {
+                        "name": "Ariano B."
+                    },
+                    {
+                        "name": "Baker J."
+                    },
+                    {
+                        "name": "Bernal-Llinares M."
+                    },
+                    {
+                        "name": "Buniello A."
+                    },
+                    {
+                        "name": "Carmona M."
+                    },
+                    {
+                        "name": "Cornu H."
+                    },
+                    {
+                        "name": "Cruz-Castillo C."
+                    },
+                    {
+                        "name": "Dunham I."
+                    },
+                    {
+                        "name": "Ferrer J."
+                    },
+                    {
+                        "name": "Fumis L."
+                    },
+                    {
+                        "name": "Ge X."
+                    },
+                    {
+                        "name": "Ghoussaini M."
+                    },
+                    {
+                        "name": "Gonzalez-Uriarte A."
+                    },
+                    {
+                        "name": "Hercules A."
+                    },
+                    {
+                        "name": "Horswell S."
+                    },
+                    {
+                        "name": "Hulcoop D.G."
+                    },
+                    {
+                        "name": "Karim M."
+                    },
+                    {
+                        "name": "Lopez I."
+                    },
+                    {
+                        "name": "Machlitt-Northen S."
+                    },
+                    {
+                        "name": "Malangone C."
+                    },
+                    {
+                        "name": "Martinez Osorio R.E."
+                    },
+                    {
+                        "name": "McDonagh E.M."
+                    },
+                    {
+                        "name": "Mehta C."
+                    },
+                    {
+                        "name": "Miranda A."
+                    },
+                    {
+                        "name": "Ochoa D."
+                    },
+                    {
+                        "name": "Razuvayevskaya O."
+                    },
+                    {
+                        "name": "Roldan-Romero J.M."
+                    },
+                    {
+                        "name": "Saha S."
+                    },
+                    {
+                        "name": "Schwartzentruber J."
+                    },
+                    {
+                        "name": "Suveges D."
+                    },
+                    {
+                        "name": "Tirunagari S."
+                    },
+                    {
+                        "name": "Tsirigos K."
+                    },
+                    {
+                        "name": "Tsukanov K."
+                    },
+                    {
+                        "name": "Young S."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "The next-generation Open Targets Platform: reimagined, redesigned, rebuilt"
+            },
+            "pmcid": "PMC9825572",
+            "pmid": "36399499"
+        },
         {
             "doi": "10.1093/nar/gkw1055",
             "metadata": {
@@ -271,7 +386,7 @@
                         "name": "Watkins X."
                     }
                 ],
-                "citationCount": 194,
+                "citationCount": 241,
                 "date": "2017-01-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "Open Targets: A platform for therapeutic target identification and Validation"
@@ -381,7 +496,7 @@
                         "name": "Suveges D."
                     }
                 ],
-                "citationCount": 26,
+                "citationCount": 100,
                 "date": "2021-01-08T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "Open Targets Platform: Supporting systematic drug-target identification and prioritisation"
@@ -452,7 +567,7 @@
                         "name": "Spitzer M."
                     }
                 ],
-                "citationCount": 158,
+                "citationCount": 233,
                 "date": "2019-01-08T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "Open Targets Platform: New developments and updates two years on"
diff --git a/data/openedc/openedc.biotools.json b/data/openedc/openedc.biotools.json
new file mode 100644
index 0000000000000..496faa51ed8f5
--- /dev/null
+++ b/data/openedc/openedc.biotools.json
@@ -0,0 +1,103 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-27T01:39:52.853100Z",
+    "biotoolsCURIE": "biotools:openedc",
+    "biotoolsID": "openedc",
+    "collectionID": [
+        "RD-Candidate"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "leonard.greulich@uni-muenster.de",
+            "name": "Leonard Greulich",
+            "orcidid": "https://orcid.org/0000-0003-3148-2105",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Martin Dugas",
+            "orcidid": "https://orcid.org/0000-0001-9740-0788"
+        },
+        {
+            "name": "Stefan Hegselmann",
+            "orcidid": "https://orcid.org/0000-0002-2145-3258"
+        }
+    ],
+    "description": "An Open-Source, Standard-Compliant, and Mobile Electronic Data Capture System for Medical Research (OpenEDC).",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://openedc.org",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-01-27T01:39:52.855875Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/imi-muenster/OpenEDC"
+        }
+    ],
+    "name": "OpenEDC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.2196/29176",
+            "metadata": {
+                "abstract": "© 2021 Eesti Rakenduslingvistika Uhingu Aastaraamat. All rights reserved.Background: Medical research and machine learning for health care depend on high-quality data. Electronic data capture (EDC) systems have been widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, the configuration and financial requirements of typical EDC systems frequently prevent small-scale studies from benefiting from their inherent advantages. Objective: The aim of this study is to develop and publish an open-source EDC system that addresses these issues. We aim to plan a system that is applicable to a wide range of research projects. Methods: We conducted a literature-based requirements analysis to identify the academic and regulatory demands for digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users. Results: We identified 20 frequently stated requirements for EDC. According to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license. Conclusions: Adopting an established standard without modifications supports metadata reuse and clinical data exchange, but it limits item layouts. OpenEDC is a stand-alone web app that can be used without a setup or configuration. This should foster compatibility between medical research and open science. OpenEDC is targeted at observational and translational research studies by clinicians.",
+                "authors": [
+                    {
+                        "name": "Dugas M."
+                    },
+                    {
+                        "name": "Greulich L."
+                    },
+                    {
+                        "name": "Hegselmann S."
+                    }
+                ],
+                "date": "2021-11-01T00:00:00Z",
+                "journal": "JMIR Medical Informatics",
+                "title": "An open-source, standard-compliant, and mobile electronic data capture system for medical research (openedc): Design and evaluation study"
+            },
+            "pmcid": "PMC8663450",
+            "pmid": "34806987"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicines research and development",
+            "uri": "http://edamontology.org/topic_3376"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        }
+    ]
+}
diff --git a/data/openehr-to-fhir/openehr-to-fhir.biotools.json b/data/openehr-to-fhir/openehr-to-fhir.biotools.json
index 225a7d7d36743..7f7a7e844254f 100644
--- a/data/openehr-to-fhir/openehr-to-fhir.biotools.json
+++ b/data/openehr-to-fhir/openehr-to-fhir.biotools.json
@@ -3,6 +3,9 @@
     "additionDate": "2022-06-15T09:07:55.578072Z",
     "biotoolsCURIE": "biotools:openehr-to-fhir",
     "biotoolsID": "openehr-to-fhir",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
     "confidence_flag": "tool",
     "cost": "Free of charge",
     "credit": [
@@ -21,7 +24,10 @@
     ],
     "description": "Converting openEHR Compositions to Fast Healthcare Interoperability Resources (FHIR) for the German Corona Consensus Dataset (GECCO).",
     "editPermission": {
-        "type": "private"
+        "authors": [
+            "iacs-biocomputacion"
+        ],
+        "type": "group"
     },
     "function": [
         {
@@ -41,7 +47,7 @@
     "language": [
         "Java"
     ],
-    "lastUpdate": "2022-06-15T09:07:55.580726Z",
+    "lastUpdate": "2023-02-01T13:03:56.123078Z",
     "license": "Not licensed",
     "name": "openEHR-to-FHIR",
     "operatingSystem": [
@@ -103,5 +109,6 @@
             "term": "Medical informatics",
             "uri": "http://edamontology.org/topic_3063"
         }
-    ]
+    ],
+    "validated": 1
 }
diff --git a/data/opengenomebrowser/opengenomebrowser.biotools.json b/data/opengenomebrowser/opengenomebrowser.biotools.json
new file mode 100644
index 0000000000000..1399a3bf86539
--- /dev/null
+++ b/data/opengenomebrowser/opengenomebrowser.biotools.json
@@ -0,0 +1,140 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-17T12:51:55.974252Z",
+    "biotoolsCURIE": "biotools:opengenomebrowser",
+    "biotoolsID": "opengenomebrowser",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "remy.bruggmann@bioinformatics.unibe.ch",
+            "name": "Rémy Bruggmann",
+            "orcidid": "https://orcid.org/0000-0001-5629-6363",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Noam Shani"
+        },
+        {
+            "name": "Simone Oberhänsli"
+        },
+        {
+            "name": "Thomas Roder"
+        }
+    ],
+    "description": "A versatile, dataset-independent and scalable web platform for genome data management and comparative genomics.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://opengenomebrowser.github.io/documentation/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Anonymisation",
+                    "uri": "http://edamontology.org/operation_3283"
+                },
+                {
+                    "term": "Dot plot plotting",
+                    "uri": "http://edamontology.org/operation_0490"
+                },
+                {
+                    "term": "Genome visualisation",
+                    "uri": "http://edamontology.org/operation_3208"
+                },
+                {
+                    "term": "Phylogenetic inference",
+                    "uri": "http://edamontology.org/operation_0323"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://opengenomebrowser.bioinformatics.unibe.ch",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-02-17T12:51:55.976859Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/opengenomebrowser/opengenomebrowser"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://opengenomebrowser.github.io"
+        }
+    ],
+    "name": "OpenGenomeBrowser",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12864-022-09086-3",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: As the amount of genomic data continues to grow, there is an increasing need for systematic ways to organize, explore, compare, analyze and share this data. Despite this, there is a lack of suitable platforms to meet this need. Results: OpenGenomeBrowser is a self-hostable, open-source platform to manage access to genomic data and drastically simplifying comparative genomics analyses. It enables users to interactively generate phylogenetic trees, compare gene loci, browse biochemical pathways, perform gene trait matching, create dot plots, execute BLAST searches, and access the data. It features a flexible user management system, and its modular folder structure enables the organization of genomic data and metadata, and to automate analyses. We tested OpenGenomeBrowser with bacterial, archaeal and yeast genomes. We provide a docker container to make installation and hosting simple. The source code, documentation, tutorials for OpenGenomeBrowser are available at opengenomebrowser.github.io and a demo server is freely accessible at opengenomebrowser.bioinformatics.unibe.ch. Conclusions: To our knowledge, OpenGenomeBrowser is the first self-hostable, database-independent comparative genome browser. It drastically simplifies commonly used bioinformatics workflows and enables convenient as well as fast data exploration.",
+                "authors": [
+                    {
+                        "name": "Bruggmann R."
+                    },
+                    {
+                        "name": "Oberhansli S."
+                    },
+                    {
+                        "name": "Roder T."
+                    },
+                    {
+                        "name": "Shani N."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Genomics",
+                "title": "OpenGenomeBrowser: a versatile, dataset-independent and scalable web platform for genome data management and comparative genomics"
+            },
+            "pmcid": "PMC9795662",
+            "pmid": "36575383"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Comparative genomics",
+            "uri": "http://edamontology.org/topic_0797"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/organoid/organoid.biotools.json b/data/organoid/organoid.biotools.json
new file mode 100644
index 0000000000000..b457156cb34fd
--- /dev/null
+++ b/data/organoid/organoid.biotools.json
@@ -0,0 +1,139 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-03T00:05:19.604833Z",
+    "biotoolsCURIE": "biotools:organoid",
+    "biotoolsID": "organoid",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tays@uchicago.edu",
+            "name": "Savaş Tay",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Brooke Schuster"
+        },
+        {
+            "name": "Jonathan M Matthews"
+        },
+        {
+            "name": "Sara Saheb Kashaf"
+        }
+    ],
+    "description": "A versatile deep learning platform for tracking and analysis of single-organoid dynamics.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/jono-m/OrganoID",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-03T00:05:19.607393Z",
+    "license": "Not licensed",
+    "name": "organoid",
+    "operatingSystem": [
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PCBI.1010584",
+            "metadata": {
+                "abstract": "© 2022 Matthews et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Organoids have immense potential as ex vivo disease models for drug discovery and personalized drug screening. Dynamic changes in individual organoid morphology, number, and size can indicate important drug responses. However, these metrics are difficult and labor-intensive to obtain for high-throughput image datasets. Here, we present OrganoID, a robust image analysis platform that automatically recognizes, labels, and tracks single organoids, pixel-by-pixel, in brightfield and phase-contrast microscopy experiments. The platform was trained on images of pancreatic cancer organoids and validated on separate images of pancreatic, lung, colon, and adenoid cystic carcinoma organoids, which showed excellent agreement with manual measurements of organoid count (95%) and size (97%) without any parameter adjustments. Single-organoid tracking accuracy remained above 89% over a four-day time-lapse microscopy study. Automated single-organoid morphology analysis of a chemotherapy dose-response experiment identified strong dose effect sizes on organoid circularity, solidity, and eccentricity. OrganoID enables straightforward, detailed, and accurate image analysis to accelerate the use of organoids in high-throughput, data-intensive biomedical applications.",
+                "authors": [
+                    {
+                        "name": "Ben-Yishay R."
+                    },
+                    {
+                        "name": "Bielski M."
+                    },
+                    {
+                        "name": "Bilgic M."
+                    },
+                    {
+                        "name": "Ishay-Ronen D."
+                    },
+                    {
+                        "name": "Izumchenko E."
+                    },
+                    {
+                        "name": "Kashaf S.S."
+                    },
+                    {
+                        "name": "Kupfer S.S."
+                    },
+                    {
+                        "name": "Liu P."
+                    },
+                    {
+                        "name": "Matthews J.M."
+                    },
+                    {
+                        "name": "Rzhetsky A."
+                    },
+                    {
+                        "name": "Schuster B."
+                    },
+                    {
+                        "name": "Shen L."
+                    },
+                    {
+                        "name": "Tay S."
+                    },
+                    {
+                        "name": "Weber C.R."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics"
+            },
+            "pmcid": "PMC9645660",
+            "pmid": "36350878"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/osadhi/osadhi.biotools.json b/data/osadhi/osadhi.biotools.json
new file mode 100644
index 0000000000000..4836887fa9d2f
--- /dev/null
+++ b/data/osadhi/osadhi.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-17T12:43:16.543537Z",
+    "biotoolsCURIE": "biotools:osadhi",
+    "biotoolsID": "osadhi",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Dipshikha Gogoi"
+        },
+        {
+            "name": "Esther Jamir"
+        },
+        {
+            "name": "G. Narahari Sastry"
+        },
+        {
+            "name": "Kikrusenuo Kiewhuo"
+        }
+    ],
+    "description": "An online structural and analytics based database for herbs of India.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://neist.res.in/osadhi/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-17T12:43:16.547334Z",
+    "name": "OSADHI",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOLCHEM.2022.107799",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdThe current study aims to develop a PAN India database of medicinal plants along with their phytochemicals and geographical availability. The database consists of 6959 unique medicinal plants belonging to 348 families which are available across 28 states and 8 union territories of India. The database sources the information on four different sections – traditional knowledge, geographical indications, phytochemicals, and chemoinformatics. The traditional knowledge reports the plant taxonomy with their vernacular names. A total of 27,440 unique phytochemicals associated with these plants were curated from various sources in this study. However, due to the non-availability of general information like IUPAC names, InChI key, etc. from reliable sources, only 22,314 phytochemicals have been currently reported in the database. Various analyses have been performed for the phytochemicals which include analysis of physicochemical and ADMET properties calculated from open-source web servers using in-house python scripts. The phytochemical data set has also been classified based on the class, superclass, and pathways respectively using NPClassifier, a deep learning framework. Additionally, the antiviral potency of the phytochemicals was also predicted using two machine learning models – Random Forest and XGBoost. The database aims to provide accurate and exhaustive data of the traditional practice of medicinal plants in India in a single platform integrating and analyzing the rich customary practices and facilitating the development and identification of plant-based therapeutics for a variety of diseases. The database can be accessed at https://neist.res.in/osadhi/.",
+                "authors": [
+                    {
+                        "name": "Das D."
+                    },
+                    {
+                        "name": "Gogoi D."
+                    },
+                    {
+                        "name": "Jamir E."
+                    },
+                    {
+                        "name": "Kiewhuo K."
+                    },
+                    {
+                        "name": "Mahanta H.J."
+                    },
+                    {
+                        "name": "Rawal R.K."
+                    },
+                    {
+                        "name": "S V."
+                    },
+                    {
+                        "name": "Sastry G.N."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "Computational Biology and Chemistry",
+                "title": "OSADHI – An online structural and analytics based database for herbs of India"
+            },
+            "pmid": "36512929"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Biological databases",
+            "uri": "http://edamontology.org/topic_3071"
+        },
+        {
+            "term": "Cheminformatics",
+            "uri": "http://edamontology.org/topic_2258"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Taxonomy",
+            "uri": "http://edamontology.org/topic_0637"
+        }
+    ]
+}
diff --git a/data/osteodip/osteodip.biotools.json b/data/osteodip/osteodip.biotools.json
new file mode 100644
index 0000000000000..a3583310548b1
--- /dev/null
+++ b/data/osteodip/osteodip.biotools.json
@@ -0,0 +1,92 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-17T12:39:31.792363Z",
+    "biotoolsCURIE": "biotools:osteodip",
+    "biotoolsID": "osteodip",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "juris@ai.utoronto.ca",
+            "name": "Igor Jurisica",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Christian Veillette"
+        },
+        {
+            "name": "Mark Abovsky"
+        },
+        {
+            "name": "Chiara Pastrello",
+            "orcidid": "https://orcid.org/0000-0002-1934-7472"
+        }
+    ],
+    "description": "A web-based gene and non-coding RNA expression database.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "miRNA expression analysis",
+                    "uri": "http://edamontology.org/operation_3792"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://ophid.utoronto.ca/OsteoDIP",
+    "lastUpdate": "2023-02-17T12:39:31.794803Z",
+    "name": "OsteoDIP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.OCARTO.2022.100237",
+            "pmcid": "PMC9718079",
+            "pmid": "36474475"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/palm/palm.biotools.json b/data/palm/palm.biotools.json
new file mode 100644
index 0000000000000..6d7764786957c
--- /dev/null
+++ b/data/palm/palm.biotools.json
@@ -0,0 +1,130 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T22:34:02.533019Z",
+    "biotoolsCURIE": "biotools:palm",
+    "biotoolsID": "palm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jin.liu@duke-nus.edu.sg",
+            "name": "Jin Liu",
+            "orcidid": "https://orcid.org/0000-0002-5707-2078",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "macyang@ust.hk",
+            "name": "Can Yang",
+            "orcidid": "https://orcid.org/0000-0002-4407-3055",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "wanxiang@sribd.cn",
+            "name": "Xiang Wan",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Xinyi Yu"
+        }
+    ],
+    "description": "A powerful and adaptive latent model for prioritizing risk variants with functional annotations.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "SNP annotation",
+                    "uri": "http://edamontology.org/operation_3661"
+                },
+                {
+                    "term": "Variant prioritisation",
+                    "uri": "http://edamontology.org/operation_3226"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/YangLabHKUST/PALM",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T22:34:02.538276Z",
+    "license": "MIT",
+    "name": "PALM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD068",
+            "metadata": {
+                "abstract": "MOTIVATION: The findings from genome-wide association studies (GWASs) have greatly helped us to understand the genetic basis of human complex traits and diseases. Despite the tremendous progress, much effects are still needed to address several major challenges arising in GWAS. First, most GWAS hits are located in the non-coding region of human genome, and thus their biological functions largely remain unknown. Second, due to the polygenicity of human complex traits and diseases, many genetic risk variants with weak or moderate effects have not been identified yet. RESULTS: To address the above challenges, we propose a powerful and adaptive latent model (PALM) to integrate cell-type/tissue-specific functional annotations with GWAS summary statistics. Unlike existing methods, which are mainly based on linear models, PALM leverages a tree ensemble to adaptively characterize non-linear relationship between functional annotations and the association status of genetic variants. To make PALM scalable to millions of variants and hundreds of functional annotations, we develop a functional gradient-based expectation-maximization algorithm, to fit the tree-based non-linear model in a stable manner. Through comprehensive simulation studies, we show that PALM not only controls false discovery rate well, but also improves statistical power of identifying risk variants. We also apply PALM to integrate summary statistics of 30 GWASs with 127 cell type/tissue-specific functional annotations. The results indicate that PALM can identify more risk variants as well as rank the importance of functional annotations, yielding better interpretation of GWAS results. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/YangLabHKUST/PALM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Cai M."
+                    },
+                    {
+                        "name": "Jiao Y."
+                    },
+                    {
+                        "name": "Liu J."
+                    },
+                    {
+                        "name": "Wan X."
+                    },
+                    {
+                        "name": "Xiao J."
+                    },
+                    {
+                        "name": "Yang C."
+                    },
+                    {
+                        "name": "Yu X."
+                    }
+                ],
+                "date": "2023-02-03T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "PALM: a powerful and adaptive latent model for prioritizing risk variants with functional annotations"
+            },
+            "pmcid": "PMC9950853",
+            "pmid": "36744920"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/palo/palo.biotools.json b/data/palo/palo.biotools.json
new file mode 100644
index 0000000000000..56f3f36316b30
--- /dev/null
+++ b/data/palo/palo.biotools.json
@@ -0,0 +1,79 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T21:55:43.617068Z",
+    "biotoolsCURIE": "biotools:palo",
+    "biotoolsID": "palo",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Wenpin Hou"
+        },
+        {
+            "name": "Zhicheng Ji"
+        }
+    ],
+    "description": "Spatially-aware color palette optimization for single-cell and spatial data.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://winnie09.github.io/Wenpin_Hou/pages/Palo.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Winnie09/Palo",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-17T21:55:43.619658Z",
+    "license": "MIT",
+    "name": "Palo",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac368",
+            "metadata": {
+                "abstract": "© 2022 The Author(s).In the exploratory data analysis of single-cell or spatial genomic data, single-cells or spatial spots are often visualized using a two-dimensional plot where cell clusters or spot clusters are marked with different colors. With tens of clusters, current visualization methods often assign visually similar colors to spatially neighboring clusters, making it hard to identify the distinction between clusters. To address this issue, we developed Palo that optimizes the color palette assignment for single-cell and spatial data in a spatially aware manner. Palo identifies pairs of clusters that are spatially neighboring to each other and assigns visually distinct colors to those neighboring pairs. We demonstrate that Palo leads to improved visualization in real single-cell and spatial genomic datasets.",
+                "authors": [
+                    {
+                        "name": "Hou W."
+                    },
+                    {
+                        "name": "Ji Z."
+                    }
+                ],
+                "date": "2022-07-15T00:00:00Z",
+                "journal": "Bioinformatics",
+                "title": "Palo: spatially aware color palette optimization for single-cell and spatial data"
+            },
+            "pmcid": "PMC9272793",
+            "pmid": "35642896"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ]
+}
diff --git a/data/pandaomics/pandaomics.biotools.json b/data/pandaomics/pandaomics.biotools.json
new file mode 100644
index 0000000000000..9740805478836
--- /dev/null
+++ b/data/pandaomics/pandaomics.biotools.json
@@ -0,0 +1,123 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-02T23:58:42.106186Z",
+    "biotoolsCURIE": "biotools:pandaomics",
+    "biotoolsID": "pandaomics",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mscheibye@sund.ku.dk",
+            "name": "Morten Scheibye-Knudsen",
+            "orcidid": "https://orcid.org/0000-0002-6637-1280",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alexander Veviorskiy"
+        },
+        {
+            "name": "Evgeny Izumchenko"
+        },
+        {
+            "name": "Garik V. Mkrtchyan"
+        }
+    ],
+    "description": "PandaOmics provides a unique opportunity to both explore the unknown of OMICs data and interpret it in the context of all the scientific data generated by the scientific community.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://insilico.com/pandaomics",
+    "lastUpdate": "2023-02-02T23:58:42.108690Z",
+    "name": "PandaOmics",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41419-022-05437-W",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorigenesis and significant intra- and inter-tumor heterogeneity. Identification of novel molecular biomarkers stratifying cancer patients with different survival outcomes may provide new opportunities for target discovery and subsequent development of tailored therapies. Here, we applied the artificial intelligence-driven PandaOmics platform (https://pandaomics.com/) to explore gene expression changes in rare DNA repair-deficient disorders and identify novel cancer targets. Our analysis revealed that CEP135, a scaffolding protein associated with early centriole biogenesis, is commonly downregulated in DNA repair diseases with high cancer predisposition. Further screening of survival data in 33 cancers available at TCGA database identified sarcoma as a cancer type where lower survival was significantly associated with high CEP135 expression. Stratification of cancer patients based on CEP135 expression enabled us to examine therapeutic targets that could be used for the improvement of existing therapies against sarcoma. The latter was based on application of the PandaOmics target-ID algorithm coupled with in vitro studies that revealed polo-like kinase 1 (PLK1) as a potential therapeutic candidate in sarcoma patients with high CEP135 levels and poor survival. While further target validation is required, this study demonstrated the potential of in silico-based studies for a rapid biomarker discovery and target characterization.",
+                "authors": [
+                    {
+                        "name": "Aliper A."
+                    },
+                    {
+                        "name": "Izumchenko E."
+                    },
+                    {
+                        "name": "Mkrtchyan G.V."
+                    },
+                    {
+                        "name": "Ozerov I.V."
+                    },
+                    {
+                        "name": "Pun F.W."
+                    },
+                    {
+                        "name": "Scheibye-Knudsen M."
+                    },
+                    {
+                        "name": "Shneyderman A."
+                    },
+                    {
+                        "name": "Veviorskiy A."
+                    },
+                    {
+                        "name": "Zhavoronkov A."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Cell Death and Disease",
+                "title": "High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders"
+            },
+            "pmcid": "PMC9701218",
+            "pmid": "36435816"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        }
+    ]
+}
diff --git a/data/pandas/pandas.biotools.json b/data/pandas/pandas.biotools.json
new file mode 100644
index 0000000000000..6fc83b8fc4604
--- /dev/null
+++ b/data/pandas/pandas.biotools.json
@@ -0,0 +1,76 @@
+{
+    "additionDate": "2023-01-31T07:43:36.144076Z",
+    "biotoolsCURIE": "biotools:pandas",
+    "biotoolsID": "pandas",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "email": "coc@pandas.pydata.org",
+            "url": "https://pandas.pydata.org/about/team.html"
+        }
+    ],
+    "description": "Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.",
+    "documentation": [
+        {
+            "type": [
+                "Installation instructions"
+            ],
+            "url": "https://pandas.pydata.org/docs/getting_started/index.html"
+        },
+        {
+            "type": [
+                "Release notes"
+            ],
+            "url": "https://pandas.pydata.org/docs/whatsnew/index.html"
+        }
+    ],
+    "download": [
+        {
+            "type": "API specification",
+            "url": "https://pandas.pydata.org/docs/reference/index.html"
+        },
+        {
+            "type": "Downloads page",
+            "url": "https://pandas.pydata.org/getting_started.html"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Calculation",
+                    "uri": "http://edamontology.org/operation_3438"
+                },
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pandas.pydata.org/",
+    "lastUpdate": "2023-02-01T12:51:51.868447Z",
+    "license": "BSD-3-Clause",
+    "link": [
+        {
+            "note": "User Guide",
+            "type": [
+                "Other"
+            ],
+            "url": "https://pandas.pydata.org/docs/user_guide/index.html"
+        }
+    ],
+    "name": "Pandas",
+    "owner": "iacs-biocomputacion",
+    "toolType": [
+        "Library"
+    ],
+    "version": [
+        "1.5.3"
+    ]
+}
diff --git a/data/parp1pred/parp1pred.biotools.json b/data/parp1pred/parp1pred.biotools.json
new file mode 100644
index 0000000000000..d485039aaa339
--- /dev/null
+++ b/data/parp1pred/parp1pred.biotools.json
@@ -0,0 +1,136 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T22:28:45.317847Z",
+    "biotoolsCURIE": "biotools:parp1pred",
+    "biotoolsID": "parp1pred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tassanee.ler@mahidol.ac.th",
+            "name": "Tassanee Lerksuthirat",
+            "orcidid": "https://orcid.org/0000-0001-9526-951X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Aijaz Ahmad Malik",
+            "orcidid": "https://orcid.org/0000-0001-5132-1574"
+        },
+        {
+            "name": "Chanin Nantasenamat",
+            "orcidid": "https://orcid.org/0000-0003-1040-663X"
+        },
+        {
+            "name": "Sermsiri Chitphuk",
+            "orcidid": "https://orcid.org/0000-0002-8149-0341"
+        }
+    ],
+    "description": "A web server for screening the bioactivity of inhibitors against DNA repair enzyme PARP-1.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "SMILES string",
+                        "uri": "http://edamontology.org/data_2301"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://parp1pred.streamlit.app",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T22:28:45.322309Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/dataprofessor/parp1"
+        }
+    ],
+    "name": "PARP1pred",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.17179/EXCLI2022-5602",
+            "metadata": {
+                "abstract": "Cancer is the leading cause of death worldwide, resulting in the mortality of more than 10 million people in 2020, according to Global Cancer Statistics 2020. A potential cancer therapy involves targeting the DNA repair process by inhibiting PARP-1. In this study, classification models were constructed using a non-redundant set of 2018 PARP-1 inhibitors. Briefly, compounds were described by 12 fingerprint types and built using the random forest algorithm concomitant with various sampling approaches. Results indicated that PubChem with an oversampling approach yielded the best performance, with a Matthews correlation coefficient > 0.7 while also affording inter-pretable molecular features. Moreover, feature importance, as determined from the Gini index, revealed that the aromatic/cyclic/heterocyclic moiety, nitrogen-containing fingerprints, and the ether/aldehyde/alcohol moiety were important for PARP-1 inhibition. Finally, our predictive model was deployed as a web application called PARP1pred and is publicly available at https://parp1pred.streamlitapp.com, allowing users to predict the biological activity of query compounds using their SMILES notation as the input. It is anticipated that the model described herein will aid in the discovery of effective PARP-1 inhibitors.",
+                "authors": [
+                    {
+                        "name": "Chitphuk S."
+                    },
+                    {
+                        "name": "Dejsuphong D."
+                    },
+                    {
+                        "name": "Lerksuthirat T."
+                    },
+                    {
+                        "name": "Malik A.A."
+                    },
+                    {
+                        "name": "Nantasenamat C."
+                    },
+                    {
+                        "name": "Stitchantrakul W."
+                    }
+                ],
+                "date": "2023-01-02T00:00:00Z",
+                "journal": "EXCLI Journal",
+                "title": "PARP1PRED: A WEB SERVER FOR SCREENING THE BIOACTIVITY OF INHIBITORS AGAINST DNA REPAIR ENZYME PARP-1"
+            },
+            "pmcid": "PMC9939779",
+            "pmid": "36814851"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cheminformatics",
+            "uri": "http://edamontology.org/topic_2258"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Enzymes",
+            "uri": "http://edamontology.org/topic_0821"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/parsecnv2/parsecnv2.biotools.json b/data/parsecnv2/parsecnv2.biotools.json
new file mode 100644
index 0000000000000..11d597265bed9
--- /dev/null
+++ b/data/parsecnv2/parsecnv2.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-02T23:50:34.980229Z",
+    "biotoolsCURIE": "biotools:parsecnv2",
+    "biotoolsID": "parsecnv2",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Jin Li"
+        },
+        {
+            "name": "Yichuan Liu"
+        },
+        {
+            "name": "Hakon Hakonarson",
+            "orcidid": "http://orcid.org/0000-0003-2814-7461"
+        },
+        {
+            "name": "Joseph T. Glessner",
+            "orcidid": "http://orcid.org/0000-0001-5131-2811"
+        }
+    ],
+    "description": "Efficient sequencing tool for copy number variation genome-wide association studies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Copy number variation detection",
+                    "uri": "http://edamontology.org/operation_3961"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "SNP detection",
+                    "uri": "http://edamontology.org/operation_0484"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/CAG-CNV/ParseCNV2",
+    "language": [
+        "Perl",
+        "R"
+    ],
+    "lastUpdate": "2023-02-02T23:50:34.982591Z",
+    "license": "GPL-3.0",
+    "name": "ParseCNV2",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41431-022-01222-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s), under exclusive licence to European Society of Human Genetics.Improved copy number variation (CNV) detection remains an area of heavy emphasis for algorithm development; however, both CNV curation and disease association approaches remain in its infancy. The current practice of focusing on candidate CNVs, where researchers study specific CNVs they believe to be pathological while discarding others, refrains from considering the full spectrum of CNVs in a hypothesis-free GWAS. To address this, we present a next-generation approach to CNV association by natively supporting the popular VCF specification for sequencing-derived variants as well as SNP array calls using a PennCNV format. The code is fast and efficient, allowing for the analysis of large (>100,000 sample) cohorts without dividing up the data on a compute cluster. The scripts are condensed into a single tool to promote simplicity and best practices. CNV curation pre and post-association is rigorously supported and emphasized to yield reliable results of highest quality. We benchmarked two large datasets, including the UK Biobank (n > 450,000) and CAG Biobank (n > 350,000) both of which are genotyped at >0.5 M probes, for our input files. ParseCNV has been actively supported and developed since 2008. ParseCNV2 presents a critical addition to formalizing CNV association for inclusion with SNP associations in GWAS Catalog. Clinical CNV prioritization, interactive quality control (QC), and adjustment for covariates are revolutionary new features of ParseCNV2 vs. ParseCNV. The software is freely available at: https://github.com/CAG-CNV/ParseCNV2.",
+                "authors": [
+                    {
+                        "name": "Chang X."
+                    },
+                    {
+                        "name": "Glessner J.T."
+                    },
+                    {
+                        "name": "Hakonarson H."
+                    },
+                    {
+                        "name": "Khan M."
+                    },
+                    {
+                        "name": "Li J."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Sleiman P.M.A."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "European Journal of Human Genetics",
+                "title": "ParseCNV2: efficient sequencing tool for copy number variation genome-wide association studies"
+            },
+            "pmid": "36316489"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "Copy number variation",
+            "uri": "http://edamontology.org/topic_3958"
+        },
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/partea/partea.biotools.json b/data/partea/partea.biotools.json
new file mode 100644
index 0000000000000..c50c386689a1a
--- /dev/null
+++ b/data/partea/partea.biotools.json
@@ -0,0 +1,96 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T22:22:26.339832Z",
+    "biotoolsCURIE": "biotools:partea",
+    "biotoolsID": "partea",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "julian.alexander.spaeth@uni-hamburg.de",
+            "name": "Julian Späth",
+            "orcidid": "https://orcid.org/0000-0003-4562-5816",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Gabriele Buchholtz"
+        },
+        {
+            "name": "Jan Baumbach"
+        },
+        {
+            "name": "Julian Matschinske"
+        }
+    ],
+    "description": "Privacy-aware multi-institutional time-to-event studies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Anonymisation",
+                    "uri": "http://edamontology.org/operation_3283"
+                },
+                {
+                    "term": "Incident curve plotting",
+                    "uri": "http://edamontology.org/operation_3503"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://partea.zbh.uni-hamburg.de",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T22:22:26.344200Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/federated-partea"
+        }
+    ],
+    "name": "Partea",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PDIG.0000101",
+            "pmcid": "PMC9931301",
+            "pmid": "36812603"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        }
+    ]
+}
diff --git a/data/pathml/pathml.biotools.json b/data/pathml/pathml.biotools.json
index 87b15e4da30b0..5ae73cce9aff1 100644
--- a/data/pathml/pathml.biotools.json
+++ b/data/pathml/pathml.biotools.json
@@ -1,26 +1,54 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2022-01-10T14:20:08.576959Z",
     "biotoolsCURIE": "biotools:pathml",
     "biotoolsID": "pathml",
     "confidence_flag": "tool",
     "credit": [
         {
-            "email": "florian.markowetz@cruk.cam.ac.uk",
-            "name": "Florian Markowetz",
-            "orcidid": "https://orcid.org/0000-0002-2784-5308",
+            "email": "mloda@med.cornell.edu",
+            "name": "Massimo Loda",
+            "note": "David D. Thompson Professor\nWeill Cornell Medical College\n\nChairman of Pathology and Laboratory Medicine\nWeill Cornell Medicine\n\nPathologist-in-Chief \nNew York-Presbyterian-Weill Cornell Medical Center",
             "typeEntity": "Person",
             "typeRole": [
                 "Primary contact"
             ]
+        },
+        {
+            "email": "pathml@dfci.harvard.edu",
+            "name": "PathML People",
+            "typeEntity": "Consortium",
+            "typeRole": [
+                "Support"
+            ]
         }
     ],
-    "description": "PathML is a unified framework for whole-slide image analysis with deep learning. The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these tasks, and have the potential to substantially reduce the workload of pathologists. However, successful implementation of deep learning for WSI analysis is complex and requires careful consideration of model hyperparameters, slide and image artefacts, and data augmentation. Here we introduce PathML, a Python library for performing preand post-processing of WSIs, which has been designed to interact with the most widely used deep learning libraries, PyTorch and TensorFlow, thus allowing seamless integration into deep learning workflows",
+    "description": "Tools for computational pathology. PathML objective is to lower the barrier to entry to digital pathology.\n\nImaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use cases. PathML is publicly available at www.pathml.org --\n \n🚀 The fastest way to get started? docker pull pathml/pathml && docker run -it -p 8888:8888 pathml/pathml",
     "documentation": [
+        {
+            "type": [
+                "Citation instructions"
+            ],
+            "url": "https://github.com/Dana-Farber-AIOS/pathml#citing"
+        },
+        {
+            "type": [
+                "Installation instructions"
+            ],
+            "url": "https://github.com/Dana-Farber-AIOS/pathml#installation"
+        },
         {
             "type": [
                 "Training material"
             ],
-            "url": "https://github.com/markowetzlab/pathml-tutorial"
+            "url": "https://pathml.readthedocs.io"
+        }
+    ],
+    "download": [
+        {
+            "note": "docker pull pathml/pathml",
+            "type": "Container file",
+            "url": "https://hub.docker.com/r/pathml/pathml"
         }
     ],
     "editPermission": {
@@ -30,49 +58,169 @@
         {
             "operation": [
                 {
-                    "term": "Deisotoping",
-                    "uri": "http://edamontology.org/operation_3629"
+                    "term": "Analysis",
+                    "uri": "http://edamontology.org/operation_2945"
+                },
+                {
+                    "term": "Calculation",
+                    "uri": "http://edamontology.org/operation_3438"
+                },
+                {
+                    "term": "Classification",
+                    "uri": "http://edamontology.org/operation_2990"
+                },
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                },
+                {
+                    "term": "Generation",
+                    "uri": "http://edamontology.org/operation_3429"
+                },
+                {
+                    "term": "Indexing",
+                    "uri": "http://edamontology.org/operation_0227"
+                },
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
                 },
                 {
-                    "term": "Image analysis",
-                    "uri": "http://edamontology.org/operation_3443"
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
                 }
             ]
         }
     ],
-    "homepage": "https://github.com/markowetzlab/pathml",
+    "homepage": "http://pathml.org",
     "language": [
-        "Python"
+        "Python",
+        "R"
     ],
-    "lastUpdate": "2022-01-10T14:20:08.580502Z",
-    "license": "GPL-3.0",
+    "lastUpdate": "2023-02-08T15:37:09.289175Z",
+    "license": "GPL-2.0",
     "link": [
+        {
+            "note": "Manuscripts that used it",
+            "type": [
+                "Discussion forum"
+            ],
+            "url": "https://scholar.google.com/scholar?cites=1157052756975292108&as_sdt=40000005&sciodt=0,22&hl=en&inst=7575085548378563675"
+        },
+        {
+            "note": "People who used it",
+            "type": [
+                "Technical monitoring"
+            ],
+            "url": "https://ossinsight.io/analyze/Dana-Farber-AIOS/pathml#people"
+        },
         {
             "type": [
-                "Issue tracker"
+                "Repository"
             ],
-            "url": "https://github.com/markowetzlab/pathml/issues"
+            "url": "https://github.com/Dana-Farber-AIOS/pathml"
         }
     ],
+    "maturity": "Mature",
     "name": "PathML",
-    "owner": "Kigaard",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "renato_umeton",
     "publication": [
         {
-            "doi": "10.1101/2021.07.07.21260138"
+            "doi": "10.1158/1541-7786.MCR-21-0665",
+            "metadata": {
+                "abstract": "© 2021 The Authors.Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use cases. PathML is publicly available at www.pathml.com.",
+                "authors": [
+                    {
+                        "name": "Brundage D."
+                    },
+                    {
+                        "name": "Carelli R."
+                    },
+                    {
+                        "name": "Halbert E."
+                    },
+                    {
+                        "name": "Hari S.N."
+                    },
+                    {
+                        "name": "Loda M."
+                    },
+                    {
+                        "name": "Marchionni L."
+                    },
+                    {
+                        "name": "Nyman J."
+                    },
+                    {
+                        "name": "Omar M."
+                    },
+                    {
+                        "name": "Rosenthal J."
+                    },
+                    {
+                        "name": "Umeton R."
+                    },
+                    {
+                        "name": "van Allen E.M."
+                    }
+                ],
+                "citationCount": 4,
+                "date": "2022-02-01T00:00:00Z",
+                "journal": "Molecular Cancer Research",
+                "title": "Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology"
+            },
+            "pmcid": "PMC9127877",
+            "pmid": "34880124",
+            "type": [
+                "Primary"
+            ]
         }
     ],
     "toolType": [
         "Library"
     ],
     "topic": [
+        {
+            "term": "Computer science",
+            "uri": "http://edamontology.org/topic_3316"
+        },
+        {
+            "term": "Data mining",
+            "uri": "http://edamontology.org/topic_3473"
+        },
         {
             "term": "Imaging",
             "uri": "http://edamontology.org/topic_3382"
         },
+        {
+            "term": "Informatics",
+            "uri": "http://edamontology.org/topic_0605"
+        },
         {
             "term": "Machine learning",
             "uri": "http://edamontology.org/topic_3474"
         },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        },
+        {
+            "term": "Omics",
+            "uri": "http://edamontology.org/topic_3391"
+        },
         {
             "term": "Pathology",
             "uri": "http://edamontology.org/topic_0634"
diff --git a/data/patpat/patpat.biotools.json b/data/patpat/patpat.biotools.json
new file mode 100644
index 0000000000000..2fe05e042d7de
--- /dev/null
+++ b/data/patpat/patpat.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T22:17:13.026046Z",
+    "biotoolsCURIE": "biotools:patpat",
+    "biotoolsID": "patpat",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xuelianzhang@fudan.edu.cn",
+            "name": "Xuelian Zhang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Weiheng Liao",
+            "orcidid": "https://orcid.org/0000-0002-1230-4402"
+        }
+    ],
+    "description": "Patpat stands for Proteomics Aiders Telescope, a public proteomics dataset search framework that simply passes in protein identifiers to search for relevant datasets and returns metadata to aid your research.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://github.com/henry-leo/Patpat/wiki"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Protein identification",
+                    "uri": "http://edamontology.org/operation_3767"
+                },
+                {
+                    "term": "Spectral library search",
+                    "uri": "http://edamontology.org/operation_3801"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/henry-leo/Patpat",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T22:17:13.031999Z",
+    "license": "Apache-2.0",
+    "name": "Patpat",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD076",
+            "metadata": {
+                "abstract": "SUMMARY: As the FAIR (Findable, Accessible, Interoperable, Reusable) principles have become widely accepted in the proteomics field, under the guidance of ProteomeXchange and The Human Proteome Organization Proteomics Standards Initiative, proteomics public databases have been providing Application Programming Interfaces for programmatic access. Based on generating logic from proteomics data, we present Patpat, an extensible framework for searching public datasets, merging results from multiple databases to help researchers find their proteins of interest in the vast mass spectrometry. Patpat's 2D strategy of combining results from multiple databases allows users to provide only protein identifiers to obtain metadata for relevant datasets, improving the 'Findable' of proteomics data. AVAILABILITY AND IMPLEMENTATION: The Patpat framework is released under the Apache 2.0 license open source, and the source code is stored on GitHub (https://github.com/henry-leo/Patpat) and is freely available. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Liao W."
+                    },
+                    {
+                        "name": "Zhang X."
+                    }
+                ],
+                "date": "2023-02-03T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Patpat: a public proteomics dataset search framework"
+            },
+            "pmcid": "PMC9933831",
+            "pmid": "36744907"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/pclassoreg/pclassoreg.biotools.json b/data/pclassoreg/pclassoreg.biotools.json
new file mode 100644
index 0000000000000..1a4f22a5345a5
--- /dev/null
+++ b/data/pclassoreg/pclassoreg.biotools.json
@@ -0,0 +1,129 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T13:20:41.419877Z",
+    "biotoolsCURIE": "biotools:pclassoreg",
+    "biotoolsID": "pclassoreg",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hanjunwei@ems.hrbmu.edu.cn",
+            "name": "Junwei Han",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "liuwei@hljit.edu.cn",
+            "name": "Wei Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Haiyan Yuan"
+        },
+        {
+            "name": "Wei Wang"
+        }
+    ],
+    "description": "A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://cran.r-project.org/web/packages/PCLassoReg/PCLassoReg.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Protein identification",
+                    "uri": "http://edamontology.org/operation_3767"
+                },
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cran.r-project.org/web/packages/PCLassoReg/index.html",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-15T13:20:41.422559Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/weiliu123/PCLassoReg"
+        }
+    ],
+    "name": "PCLassoReg",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.12.005",
+            "metadata": {
+                "abstract": "© 2022 The Author(s)Risk gene identification has attracted much attention in the past two decades. Since most genes need to be translated into proteins and cooperate with other proteins to form protein complexes to carry out cellular functions, which significantly extends the functional diversity of individual proteins, revealing the molecular mechanism of cancer from a comprehensive perspective needs to shift from identifying individual risk genes toward identifying risk protein complexes. Here, we embed protein complexes into the regularized learning framework and propose a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes. Experiments on deep proteomic data of two cancer types show that PCLassoLog yields superior predictive performance on independent datasets. More importantly, PCLassoLog identifies risk protein complexes that not only contain individual risk proteins but also incorporate close partners that synergize with them. Furthermore, selection probabilities are calculated and two other protein complex-based models are proposed to complement PCLassoLog in identifying reliable risk protein complexes. Based on PCLassoLog, a pan-cancer analysis is performed to identify risk protein complexes in 12 cancer types. Finally, PCLassoLog is used to discover risk protein complexes associated with gene mutation. We implement all protein complex-based models as an R package PCLassoReg, which may serve as an effective tool to discover risk protein complexes in various contexts.",
+                "authors": [
+                    {
+                        "name": "Han J."
+                    },
+                    {
+                        "name": "Liu W."
+                    },
+                    {
+                        "name": "Wang W."
+                    },
+                    {
+                        "name": "Yuan H."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery"
+            },
+            "pmcid": "PMC9791601",
+            "pmid": "36582441"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
+}
diff --git a/data/pcp-lod/pcp-lod.biotools.json b/data/pcp-lod/pcp-lod.biotools.json
new file mode 100644
index 0000000000000..911becd647f42
--- /dev/null
+++ b/data/pcp-lod/pcp-lod.biotools.json
@@ -0,0 +1,136 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-02T23:43:29.045403Z",
+    "biotoolsCURIE": "biotools:pcp-lod",
+    "biotoolsID": "pcp-lod",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mk3961@cumc.columbia.edu",
+            "name": "Marianthi-Anna Kioumourtzoglou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Junhui Zhang"
+        },
+        {
+            "name": "Elizabeth A. Gibson",
+            "orcidid": "https://orcid.org/0000-0001-5119-5133"
+        },
+        {
+            "name": "Jingkai Yan",
+            "orcidid": "https://orcid.org/0000-0002-2094-2092"
+        }
+    ],
+    "description": "Principal Component Pursuit for Pattern Identification in Environmental Mixtures.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                },
+                {
+                    "term": "Principal component analysis",
+                    "uri": "http://edamontology.org/operation_3960"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/lizzyagibson/PCP-LOD",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-02T23:43:29.047903Z",
+    "license": "BSD-2-Clause",
+    "name": "PCP-LOD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1289/EHP10479",
+            "metadata": {
+                "abstract": "© 2022, Public Health Services, US Dept of Health and Human Services. All rights reserved.BACKGROUND: Environmental health researchers often aim to identify sources or behaviors that give rise to potentially harmful environmental exposures. OBJECTIVE: We adapted principal component pursuit (PCP)—a robust and well-established technique for dimensionality reduction in computer vision and signal processing—to identify patterns in environmental mixtures. PCP decomposes the exposure mixture into a low-rank matrix containing consistent patterns of exposure across pollutants and a sparse matrix isolating unique or extreme exposure events. METHODS: We adapted PCP to accommodate nonnegative data, missing data, and values below a given limit of detection (LOD). We simulated data to represent environmental mixtures of two sizes with increasing proportions <LOD and three noise structures. We applied PCP-LOD to evaluate its performance in comparison with principal component analysis (PCA). We next applied principal component pursuit with limit of detection (PCP-LOD) to an exposure mixture of 21 persistent organic pollutants (POPs) measured in 1,000 U.S. adults from the 2001–2002 National Health and Nutrition Examination Survey (NHANES). We applied singular value decomposition to the estimated low-rank matrix to characterize the patterns. RESULTS: PCP-LOD recovered the true number of patterns through cross-validation for all simulations; based on an a priori specified criterion, PCA recovered the true number of patterns in 32% of simulations. PCP-LOD achieved lower relative predictive error than PCA for all simulated data sets with up to 50% of the data <LOD. When 75% of values were <LOD, PCP-LOD outperformed PCA only when noise was low. In the POP mixture, PCP-LOD identified a rank-three underlying structure and separated 6% of values as extreme events. One pattern represented comprehensive exposure to all POPs. The other patterns grouped chemicals based on known structure and toxicity. DISCUSSION: PCP-LOD serves as a useful tool to express multidimensional exposures as consistent patterns that, if found to be related to adverse health, are amenable to targeted public health messaging. https://doi.org/10.1289/EHP10479.",
+                "authors": [
+                    {
+                        "name": "Benavides J."
+                    },
+                    {
+                        "name": "Chillrud L."
+                    },
+                    {
+                        "name": "Gibson E.A."
+                    },
+                    {
+                        "name": "Goldsmith J."
+                    },
+                    {
+                        "name": "Herbstman J.B."
+                    },
+                    {
+                        "name": "Kioumourtzoglou M.-A."
+                    },
+                    {
+                        "name": "Nunez Y."
+                    },
+                    {
+                        "name": "Wright J."
+                    },
+                    {
+                        "name": "Yan J."
+                    },
+                    {
+                        "name": "Zhang J."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Environmental Health Perspectives",
+                "title": "Principal Component Pursuit for Pattern Identification in Environmental Mixtures"
+            },
+            "pmcid": "PMC9683097",
+            "pmid": "36416734"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Nutritional science",
+            "uri": "http://edamontology.org/topic_3390"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        },
+        {
+            "term": "Toxicology",
+            "uri": "http://edamontology.org/topic_2840"
+        }
+    ]
+}
diff --git a/data/pdacr/pdacr.biotools.json b/data/pdacr/pdacr.biotools.json
new file mode 100644
index 0000000000000..fabbf590705d5
--- /dev/null
+++ b/data/pdacr/pdacr.biotools.json
@@ -0,0 +1,129 @@
+{
+    "additionDate": "2023-03-18T22:09:47.466019Z",
+    "biotoolsCURIE": "biotools:pdacr",
+    "biotoolsID": "pdacr",
+    "confidence_flag": "high",
+    "credit": [
+        {
+            "email": "Richard.austin.moffitt@emory.edu",
+            "name": "Richard A. Moffitt",
+            "orcidid": "https://orcid.org/0000-0003-2723-5902",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jen Jen Yeh"
+        },
+        {
+            "name": "Ryan R. Kawalerski"
+        },
+        {
+            "name": "Luke A. Torre-Healy",
+            "orcidid": "http://orcid.org/0000-0002-9513-4620"
+        }
+    ],
+    "description": "Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://pdacR.bmi.stonybrook.edu",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-18T22:09:47.470427Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "http://github.com/rmoffitt/pdacR"
+        }
+    ],
+    "name": "pdacR",
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S42003-023-04461-6",
+            "metadata": {
+                "abstract": "Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR (http://pdacR.bmi.stonybrook.edu, github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.",
+                "authors": [
+                    {
+                        "name": "Aguirre A.J."
+                    },
+                    {
+                        "name": "Chrastecka L."
+                    },
+                    {
+                        "name": "Kawalerski R.R."
+                    },
+                    {
+                        "name": "Moffitt R.A."
+                    },
+                    {
+                        "name": "Oh K."
+                    },
+                    {
+                        "name": "Peng X.L."
+                    },
+                    {
+                        "name": "Rashid N.U."
+                    },
+                    {
+                        "name": "Torre-Healy L.A."
+                    },
+                    {
+                        "name": "Yeh J.J."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Communications Biology",
+                "title": "Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model"
+            },
+            "pmcid": "PMC9918476",
+            "pmid": "36765128"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/pdiffinder/pdiffinder.biotools.json b/data/pdiffinder/pdiffinder.biotools.json
new file mode 100644
index 0000000000000..d0c00c2efa830
--- /dev/null
+++ b/data/pdiffinder/pdiffinder.biotools.json
@@ -0,0 +1,154 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T13:11:43.348321Z",
+    "biotoolsCURIE": "biotools:pdiffinder",
+    "biotoolsID": "pdiffinder",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chenhuan7809@gmail.com",
+            "name": "Huan Chen",
+            "orcidid": "https://orcid.org/0000-0001-8005-5690",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xiaotinghua@zju.edu.cn",
+            "name": "Xiaoting Hua",
+            "orcidid": "https://orcid.org/0000-0001-8215-916X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Mengjie Shao",
+            "orcidid": "https://orcid.org/0000-0001-6624-978X"
+        },
+        {
+            "name": "Chengzhi Liu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Pdif-mediated antibiotic resistance genes transfer in bacteria identified by pdifFinder.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence",
+                        "uri": "http://edamontology.org/data_2044"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Antimicrobial resistance prediction",
+                    "uri": "http://edamontology.org/operation_3482"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Plasmid map drawing",
+                    "uri": "http://edamontology.org/operation_0578"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://pdif.dmicrobe.cn/pdif/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-15T13:11:43.351006Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/mjshao06/pdifFinder"
+        }
+    ],
+    "name": "pdifFinder",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC521",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Modules consisting of antibiotic resistance genes (ARGs) flanked by inverted repeat Xer-specific recombination sites were thought to be mobile genetic elements that promote horizontal transmission. Less frequently, the presence of mobile modules in plasmids, which facilitate a pdif-mediated ARGs transfer, has been reported. Here, numerous ARGs and toxin-antitoxin genes have been found in pdif site pairs. However, the mechanisms underlying this apparent genetic mobility is currently not understood, and the studies relating to pdif-mediated ARGs transfer onto most bacterial genera are lacking. We developed the web server pdifFinder based on an algorithm called PdifSM that allows the prediction of diverse pdif-ARGs modules in bacterial genomes. Using test set consisting of almost 32 thousand plasmids from 717 species, PdifSM identified 481 plasmids from various bacteria containing pdif sites with ARGs. We found 28-bp-long elements from different genera with clear base preferences. The data we obtained indicate that XerCD-dif site-specific recombination mechanism may have evolutionary adapted to facilitate the pdif-mediated ARGs transfer. Through multiple sequence alignment and evolutionary analyses of duplicated pdif-ARGs modules, we discovered that pdif sites allow an interspecies transfer of ARGs but also across different genera. Mutations in pdif sites generate diverse arrays of modules which mediate multidrug-resistance, as these contain variable numbers of diverse ARGs, insertion sequences and other functional genes. The identification of pdif-ARGs modules and studies focused on the mechanism of ARGs co-transfer will help us to understand and possibly allow controlling the spread of MDR bacteria in clinical settings. The pdifFinder code, standalone software package and description with tutorials are available at https://github.com/mjshao06/pdifFinder.",
+                "authors": [
+                    {
+                        "name": "Chen H."
+                    },
+                    {
+                        "name": "Hua X."
+                    },
+                    {
+                        "name": "Leptihn S."
+                    },
+                    {
+                        "name": "Liang Q."
+                    },
+                    {
+                        "name": "Liu C."
+                    },
+                    {
+                        "name": "Ma N."
+                    },
+                    {
+                        "name": "Shao M."
+                    },
+                    {
+                        "name": "Ying N."
+                    },
+                    {
+                        "name": "Yu Y."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "Pdif-mediated antibiotic resistance genes transfer in bacteria identified by pdifFinder"
+            },
+            "pmid": "36470841"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Mobile genetic elements",
+            "uri": "http://edamontology.org/topic_0798"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/pdrp/pdrp.biotools.json b/data/pdrp/pdrp.biotools.json
new file mode 100644
index 0000000000000..b69d8011fba26
--- /dev/null
+++ b/data/pdrp/pdrp.biotools.json
@@ -0,0 +1,125 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-02T23:37:51.066691Z",
+    "biotoolsCURIE": "biotools:pdrp",
+    "biotoolsID": "pdrp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "talam@hbku.edu.qa",
+            "name": "Tanvir Alam",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jawwad A. Shamsi"
+        },
+        {
+            "name": "Rizwan Qureshi"
+        },
+        {
+            "name": "Syed Abdullah Basit"
+        }
+    ],
+    "description": "Machine learning based personalized drug response prediction for lung cancer patients.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Hydrogen bond calculation",
+                    "uri": "http://edamontology.org/operation_0394"
+                },
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/rizwanqureshi123/PDRP/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-02T23:37:51.069893Z",
+    "license": "Not licensed",
+    "name": "PDRP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41598-022-23649-0",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Lung cancers with a mutated epidermal growth factor receptor (EGFR) are a major contributor to cancer fatalities globally. Targeted tyrosine kinase inhibitors (TKIs) have been developed against EGFR and show encouraging results for survival rate and quality of life. However, drug resistance may affect treatment plans and treatment efficacy may be lost after about a year. Predicting the response to EGFR-TKIs for EGFR-mutated lung cancer patients is a key research area. In this study, we propose a personalized drug response prediction model (PDRP), based on molecular dynamics simulations and machine learning, to predict the response of first generation FDA-approved small molecule EGFR-TKIs, Gefitinib/Erlotinib, in lung cancer patients. The patient’s mutation status is taken into consideration in molecular dynamics (MD) simulation. Each patient’s unique mutation status was modeled considering MD simulation to extract molecular-level geometric features. Moreover, additional clinical features were incorporated into machine learning model for drug response prediction. The complete feature set includes demographic and clinical information (DCI), geometrical properties of the drug-target binding site, and the binding free energy of the drug-target complex from the MD simulation. PDRP incorporates an XGBoost classifier, which achieves state-of-the-art performance with 97.5% accuracy, 93% recall, 96.5% precision, and 94% F1-score, for a 4-class drug response prediction task. We found that modeling the geometry of the binding pocket combined with binding free energy is a good predictor for drug response. However, we observed that clinical information had a little impact on the performance of the model. The proposed model could be tested on other types of cancers. We believe PDRP will support the planning of effective treatment regimes based on clinical-genomic information. The source code and related files are available on GitHub at: https://github.com/rizwanqureshi123/PDRP/.",
+                "authors": [
+                    {
+                        "name": "Alam T."
+                    },
+                    {
+                        "name": "Basit S.A."
+                    },
+                    {
+                        "name": "Fan X."
+                    },
+                    {
+                        "name": "Nawaz M."
+                    },
+                    {
+                        "name": "Qureshi R."
+                    },
+                    {
+                        "name": "Shamsi J.A."
+                    },
+                    {
+                        "name": "Yan H."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "Machine learning based personalized drug response prediction for lung cancer patients"
+            },
+            "pmcid": "PMC9640729",
+            "pmid": "36344580"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular dynamics",
+            "uri": "http://edamontology.org/topic_0176"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/pdxs/pdxs.biotools.json b/data/pdxs/pdxs.biotools.json
new file mode 100644
index 0000000000000..0d64c65de6793
--- /dev/null
+++ b/data/pdxs/pdxs.biotools.json
@@ -0,0 +1,208 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-17T22:03:15.931324Z",
+    "biotoolsCURIE": "biotools:pdxs",
+    "biotoolsID": "pdxs",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Carol.Bult@jax.org",
+            "name": "Carol J. Bult",
+            "orcidid": "http://orcid.org/0000-0001-9433-210X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Anuj Srivastava"
+        },
+        {
+            "name": "Xing Yi Woo"
+        },
+        {
+            "name": "Michael W. Lloyd",
+            "orcidid": "http://orcid.org/0000-0003-1021-8129"
+        }
+    ],
+    "description": "A Genomically and Clinically Annotated Patient Derived Xenograft (PDX) Resource for Preclinical Research in Non-Small Cell Lung Cancer.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Nucleic acid design",
+                    "uri": "http://edamontology.org/operation_3095"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://tumor.informatics.jax.org/mtbwi/pdxSearch.do",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-01-17T22:03:15.933841Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/TheJacksonLaboratory/PDX-SOC"
+        }
+    ],
+    "name": "PDXs",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1158/0008-5472.CAN-22-0948",
+            "metadata": {
+                "abstract": "© 2022 The Authors.Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research.",
+                "authors": [
+                    {
+                        "name": "Airhart S.D."
+                    },
+                    {
+                        "name": "Bult C.J."
+                    },
+                    {
+                        "name": "Bundy M."
+                    },
+                    {
+                        "name": "Chavaree M."
+                    },
+                    {
+                        "name": "Chen M."
+                    },
+                    {
+                        "name": "Cheng M."
+                    },
+                    {
+                        "name": "Domanskyi S."
+                    },
+                    {
+                        "name": "Gandara D.R."
+                    },
+                    {
+                        "name": "Gandour-Edwards R."
+                    },
+                    {
+                        "name": "George J."
+                    },
+                    {
+                        "name": "Goodwin N."
+                    },
+                    {
+                        "name": "Graber J.H."
+                    },
+                    {
+                        "name": "Grubb S.C."
+                    },
+                    {
+                        "name": "Holland W."
+                    },
+                    {
+                        "name": "Jocoy E.L."
+                    },
+                    {
+                        "name": "Karuturi R.K.M."
+                    },
+                    {
+                        "name": "Keck J."
+                    },
+                    {
+                        "name": "Lara Jr P.N."
+                    },
+                    {
+                        "name": "Liu E.T."
+                    },
+                    {
+                        "name": "Lloyd M.W."
+                    },
+                    {
+                        "name": "Mack P.C."
+                    },
+                    {
+                        "name": "Neuhauser S.B."
+                    },
+                    {
+                        "name": "Openshaw T.H."
+                    },
+                    {
+                        "name": "Paisie C."
+                    },
+                    {
+                        "name": "Peterson J.G."
+                    },
+                    {
+                        "name": "Riess J.W."
+                    },
+                    {
+                        "name": "Sanderson B.J."
+                    },
+                    {
+                        "name": "Simons A.K."
+                    },
+                    {
+                        "name": "Srivastava A."
+                    },
+                    {
+                        "name": "Stafford G.A."
+                    },
+                    {
+                        "name": "Tepper C.G."
+                    },
+                    {
+                        "name": "Tsai R.A."
+                    },
+                    {
+                        "name": "Woo X.Y."
+                    }
+                ],
+                "date": "2022-11-15T00:00:00Z",
+                "journal": "Cancer Research",
+                "title": "A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non-Small Cell Lung Cancer"
+            },
+            "pmcid": "PMC9664138",
+            "pmid": "36069866"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene structure",
+            "uri": "http://edamontology.org/topic_0114"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/pentaho/pentaho.biotools.json b/data/pentaho/pentaho.biotools.json
new file mode 100644
index 0000000000000..211a5bd0e88ce
--- /dev/null
+++ b/data/pentaho/pentaho.biotools.json
@@ -0,0 +1,51 @@
+{
+    "additionDate": "2023-01-26T10:15:11.979844Z",
+    "biotoolsCURIE": "biotools:pentaho",
+    "biotoolsID": "pentaho",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "Hitachi Vantara ; Pentaho Corporation",
+            "typeEntity": "Consortium",
+            "url": "https://www.hitachivantara.com/en-us/products/dataops-software/data-integration-analytics.html?source=pentaho-redirect"
+        }
+    ],
+    "description": "Pentaho is business intelligence (BI) software that provides data integration, OLAP services, reporting, information dashboards, data mining and extract, transform, load (ETL) capabilities.",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://github.com/pentaho"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/pentaho",
+    "lastUpdate": "2023-02-01T12:24:03.794479Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/pentaho/pentaho-kettle"
+        }
+    ],
+    "name": "Pentaho",
+    "owner": "iacs-biocomputacion",
+    "version": [
+        "Stable release 9.2.0.0-290  June 2, 2021;"
+    ]
+}
diff --git a/data/perceiver_cpi/perceiver_cpi.biotools.json b/data/perceiver_cpi/perceiver_cpi.biotools.json
new file mode 100644
index 0000000000000..626458d3f57eb
--- /dev/null
+++ b/data/perceiver_cpi/perceiver_cpi.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-01T23:10:57.432164Z",
+    "biotoolsCURIE": "biotools:perceiver_cpi",
+    "biotoolsID": "perceiver_cpi",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "kangj@korea.ac.kr",
+            "name": "Jaewoo Kang",
+            "orcidid": "https://orcid.org/0000-0001-6798-9106",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Gwanghoon Jang"
+        },
+        {
+            "name": "Hajung Kim"
+        },
+        {
+            "name": "Ngoc-Quang Nguyen",
+            "orcidid": "https://orcid.org/0000-0002-7448-535X"
+        }
+    ],
+    "description": "A nested cross-attention network for compound-protein interaction prediction.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Molecular docking",
+                    "uri": "http://edamontology.org/operation_0478"
+                },
+                {
+                    "term": "Protein interaction network prediction",
+                    "uri": "http://edamontology.org/operation_3094"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/dmis-lab/PerceiverCPI",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-01T23:10:57.434856Z",
+    "license": "MIT",
+    "name": "Perceiver CPI",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC731",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Compound-protein interaction (CPI) plays an essential role in drug discovery and is performed via expensive molecular docking simulations. Many artificial intelligence-based approaches have been proposed in this regard. Recently, two types of models have accomplished promising results in exploiting molecular information: graph convolutional neural networks that construct a learned molecular representation from a graph structure (atoms and bonds), and neural networks that can be applied to compute on descriptors or fingerprints of molecules. However, the superiority of one method over the other is yet to be determined. Modern studies have endeavored to aggregate information that is extracted from compounds and proteins to form the CPI task. Nonetheless, these approaches have used a simple concatenation to combine them, which cannot fully capture the interaction between such information. RESULTS: We propose the Perceiver CPI network, which adopts a cross-attention mechanism to improve the learning ability of the representation of drug and target interactions and exploits the rich information obtained from extended-connectivity fingerprints to improve the performance. We evaluated Perceiver CPI on three main datasets, Davis, KIBA and Metz, to compare the performance of our proposed model with that of state-of-the-art methods. The proposed method achieved satisfactory performance and exhibited significant improvements over previous approaches in all experiments. AVAILABILITY AND IMPLEMENTATION: Perceiver CPI is available at https://github.com/dmis-lab/PerceiverCPI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Jang G."
+                    },
+                    {
+                        "name": "Kang J."
+                    },
+                    {
+                        "name": "Kim H."
+                    },
+                    {
+                        "name": "Nguyen N.-Q."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Perceiver CPI: a nested cross-attention network for compound-protein interaction prediction"
+            },
+            "pmid": "36416124"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/petitefinder/petitefinder.biotools.json b/data/petitefinder/petitefinder.biotools.json
new file mode 100644
index 0000000000000..33a98c8e3dfb7
--- /dev/null
+++ b/data/petitefinder/petitefinder.biotools.json
@@ -0,0 +1,105 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T11:47:04.661679Z",
+    "biotoolsCURIE": "biotools:petitefinder",
+    "biotoolsID": "petitefinder",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chris.nunn@utoronto.ca",
+            "name": "Christopher J. Nunn",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Eugene Klyshko"
+        },
+        {
+            "name": "Sidhartha Goyal"
+        }
+    ],
+    "description": "An automated computer vision tool to compute Petite colony frequencies in baker's yeast.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Image annotation",
+                    "uri": "http://edamontology.org/operation_3553"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.github.com/javathejhut/petiteFinder",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T11:47:04.666434Z",
+    "license": "Not licensed",
+    "name": "petiteFinder",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-023-05168-5",
+            "metadata": {
+                "abstract": "Background: Mitochondrial respiration is central to cellular and organismal health in eukaryotes. In baker’s yeast, however, respiration is dispensable under fermentation conditions. Because yeast are tolerant of this mitochondrial dysfunction, yeast are widely used by biologists as a model organism to ask a variety of questions about the integrity of mitochondrial respiration. Fortunately, baker’s yeast also display a visually identifiable Petite colony phenotype that indicates when cells are incapable of respiration. Petite colonies are smaller than their Grande (wild-type) counterparts, and their frequency can be used to infer the integrity of mitochondrial respiration in populations of cells. Unfortunately, the computation of Petite colony frequencies currently relies on laborious manual colony counting methods which limit both experimental throughput and reproducibility. Results: To address these problems, we introduce a deep learning enabled tool, petiteFinder, that increases the throughput of the Petite frequency assay. This automated computer vision tool detects Grande and Petite colonies and computes Petite colony frequencies from scanned images of Petri dishes. It achieves accuracy comparable to human annotation but at up to 100 times the speed and outperforms semi-supervised Grande/Petite colony classification approaches. Combined with the detailed experimental protocols we provide, we believe this study can serve as a foundation to standardize this assay. Finally, we comment on how Petite colony detection as a computer vision problem highlights ongoing difficulties with small object detection in existing object detection architectures. Conclusion: Colony detection with petiteFinder results in high accuracy Petite and Grande detection in images in a completely automated fashion. It addresses issues in scalability and reproducibility of the Petite colony assay which currently relies on manual colony counting. By constructing this tool and providing details of experimental conditions, we hope this study will enable larger-scale experiments that rely on Petite colony frequencies to infer mitochondrial function in yeast.",
+                "authors": [
+                    {
+                        "name": "Goyal S."
+                    },
+                    {
+                        "name": "Klyshko E."
+                    },
+                    {
+                        "name": "Nunn C.J."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "petiteFinder: an automated computer vision tool to compute Petite colony frequencies in baker’s yeast"
+            },
+            "pmcid": "PMC9930278",
+            "pmid": "36793007"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Surgery",
+            "uri": "http://edamontology.org/topic_3421"
+        }
+    ]
+}
diff --git a/data/pfamscan/pfamscan.biotools.json b/data/pfamscan/pfamscan.biotools.json
new file mode 100644
index 0000000000000..a7fec7e916d2d
--- /dev/null
+++ b/data/pfamscan/pfamscan.biotools.json
@@ -0,0 +1,32 @@
+{
+    "additionDate": "2023-02-01T21:20:48.225503Z",
+    "biotoolsCURIE": "biotools:pfamscan",
+    "biotoolsID": "pfamscan",
+    "description": "This tool is used to search a FASTA sequence against a library of Pfam HMM.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein sequence analysis",
+                    "uri": "http://edamontology.org/operation_2479"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://ftp.ebi.ac.uk/pub/databases/Pfam/Tools/",
+    "lastUpdate": "2023-02-01T21:21:37.515832Z",
+    "name": "PfamScan",
+    "owner": "gallardoalba",
+    "topic": [
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ],
+    "version": [
+        "1.6"
+    ]
+}
diff --git a/data/pfresgo/pfresgo.biotools.json b/data/pfresgo/pfresgo.biotools.json
new file mode 100644
index 0000000000000..0f8080406a51d
--- /dev/null
+++ b/data/pfresgo/pfresgo.biotools.json
@@ -0,0 +1,123 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T11:43:39.445306Z",
+    "biotoolsCURIE": "biotools:pfresgo",
+    "biotoolsID": "pfresgo",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Geoff.Webb@monash.edu",
+            "name": "Seiya Imoto",
+            "orcidid": "https://orcid.org/0000-0002-2989-308X",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "imoto@ims.u-tokyo.ac.jp",
+            "name": "Jiangning Song",
+            "orcidid": "https://orcid.org/0000-0001-8031-9086",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "Jiangning.Song@monash.edu",
+            "name": "Geoffrey I Webb",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Chen Li",
+            "orcidid": "https://orcid.org/0000-0002-1847-754X"
+        }
+    ],
+    "description": "An attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene functional annotation",
+                    "uri": "http://edamontology.org/operation_3672"
+                },
+                {
+                    "term": "Protein function prediction",
+                    "uri": "http://edamontology.org/operation_1777"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/BioColLab/PFresGO",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T11:43:39.450566Z",
+    "license": "Not licensed",
+    "name": "PFresGO",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD094",
+            "metadata": {
+                "abstract": "MOTIVATION: The rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotation simply focus on the use of protein-level information but ignore inter-relationships among annotations. RESULTS: Here, we established PFresGO, an attention-based deep-learning approach that incorporates hierarchical structures in Gene Ontology (GO) graphs and advances in natural language processing algorithms for the functional annotation of proteins. PFresGO employs a self-attention operation to capture the inter-relationships of GO terms, updates its embedding accordingly and uses a cross-attention operation to project protein representations and GO embedding into a common latent space to identify global protein sequence patterns and local functional residues. We demonstrate that PFresGO consistently achieves superior performance across GO categories when compared with 'state-of-the-art' methods. Importantly, we show that PFresGO can identify functionally important residues in protein sequences by assessing the distribution of attention weightings. PFresGO should serve as an effective tool for the accurate functional annotation of proteins and functional domains within proteins. AVAILABILITY AND IMPLEMENTATION: PFresGO is available for academic purposes at https://github.com/BioColLab/PFresGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Akutsu T."
+                    },
+                    {
+                        "name": "Bi Y."
+                    },
+                    {
+                        "name": "Gasser R.B."
+                    },
+                    {
+                        "name": "Imoto S."
+                    },
+                    {
+                        "name": "Li C."
+                    },
+                    {
+                        "name": "Pan T."
+                    },
+                    {
+                        "name": "Purcell A.W."
+                    },
+                    {
+                        "name": "Song J."
+                    },
+                    {
+                        "name": "Wang Z."
+                    },
+                    {
+                        "name": "Webb G.I."
+                    }
+                ],
+                "date": "2023-03-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships"
+            },
+            "pmid": "36794913"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Proteins",
+            "uri": "http://edamontology.org/topic_0078"
+        }
+    ]
+}
diff --git a/data/pgg_mhc/pgg_mhc.biotools.json b/data/pgg_mhc/pgg_mhc.biotools.json
new file mode 100644
index 0000000000000..d4c4e049c47a4
--- /dev/null
+++ b/data/pgg_mhc/pgg_mhc.biotools.json
@@ -0,0 +1,129 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-01T23:04:16.200728Z",
+    "biotoolsCURIE": "biotools:pgg_mhc",
+    "biotoolsID": "pgg_mhc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xushua@fudan.edu.cn",
+            "name": "Shuhua Xu",
+            "orcidid": "https://orcid.org/0000-0002-1975-1002",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Baonan Wang"
+        },
+        {
+            "name": "Sen Ma"
+        },
+        {
+            "name": "Xiaohan Zhao"
+        }
+    ],
+    "description": "Toward understanding the diversity of major histocompatibility complexes in human populations",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Haplotype mapping",
+                    "uri": "http://edamontology.org/operation_0487"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.pggmhc.org/pggmhc/#/home",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-01T23:04:16.204053Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/Shuhua-Group/PGG.MHC"
+        }
+    ],
+    "name": "PGG MHC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC997",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.The human leukocyte antigen (HLA) system, or the human version of the major histocompatibility complex (MHC), is known for its extreme polymorphic nature and high heterogeneity. Taking advantage of whole-genome and whole-exome sequencing data, we developed PGG.MHC to provide a platform to explore the diversity of the MHC in Asia as well as in global populations. PGG.MHC currently archives high-resolution HLA alleles of 53 254 samples representing 190 populations spanning 66 countries. PGG.MHC provides: (i) high-quality allele frequencies for eight classical HLA loci (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1, -DPA1 and -DPB1); (ii) visualization of population prevalence of HLA alleles on global, regional, and country-wide levels; (iii) haplotype structure of 134 populations; (iv) two online analysis tools including 'HLA imputation' for inferring HLA alleles from SNP genotyping data and 'HLA association' to perform case/control studies for HLA-related phenotypes and (v) East Asian-specific reference panels for HLA imputation. Equipped with high-quality frequency data and user-friendly computer tools, we expect that the PGG.MHC database can advance the understanding and facilitate applications of MHC genomic diversity in both evolutionary and medical studies. The PGG.MHC database is freely accessible via https://pog.fudan.edu.cn/pggmhc or https://www.pggmhc.org/pggmhc.",
+                "authors": [
+                    {
+                        "name": "Jiang X."
+                    },
+                    {
+                        "name": "Ma S."
+                    },
+                    {
+                        "name": "Wang B."
+                    },
+                    {
+                        "name": "Xu S."
+                    },
+                    {
+                        "name": "Zhao X."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "PGG.MHC: toward understanding the diversity of major histocompatibility complexes in human populations"
+            },
+            "pmcid": "PMC9825418",
+            "pmid": "36321663"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/phagcn2/phagcn2.biotools.json b/data/phagcn2/phagcn2.biotools.json
new file mode 100644
index 0000000000000..2ba76edc20615
--- /dev/null
+++ b/data/phagcn2/phagcn2.biotools.json
@@ -0,0 +1,97 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T13:05:15.401003Z",
+    "biotoolsCURIE": "biotools:phagcn2",
+    "biotoolsID": "phagcn2",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jingzhejiang@gmail.com",
+            "name": "Jing-Zhe Jiang",
+            "orcidid": "https://orcid.org/0000-0001-5260-7822",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "yannisun@cityu.edu.hk",
+            "name": "Yanni Sun",
+            "orcidid": "https://orcid.org/0000-0003-1373-8023",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "ylh@gdpu.edu.cn",
+            "name": "Li-Hong Yuan",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Wen-Guang Yuan",
+            "orcidid": "https://orcid.org/0000-0002-0191-642X"
+        }
+    ],
+    "description": "Virus classification for viral genomic fragments using PhaGCN2.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network visualisation",
+                    "uri": "http://edamontology.org/operation_3925"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "Taxonomic classification",
+                    "uri": "http://edamontology.org/operation_3460"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/KennthShang/PhaGCN2.0",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-15T13:05:15.403488Z",
+    "license": "Not licensed",
+    "name": "PhaGCN2",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC505",
+            "pmid": "36464489"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene and protein families",
+            "uri": "http://edamontology.org/topic_0623"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Taxonomy",
+            "uri": "http://edamontology.org/topic_0637"
+        }
+    ]
+}
diff --git a/data/phagetailfinder/phagetailfinder.biotools.json b/data/phagetailfinder/phagetailfinder.biotools.json
new file mode 100644
index 0000000000000..20704dcc925c5
--- /dev/null
+++ b/data/phagetailfinder/phagetailfinder.biotools.json
@@ -0,0 +1,151 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T11:37:26.794049Z",
+    "biotoolsCURIE": "biotools:phagetailfinder",
+    "biotoolsID": "phagetailfinder",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "fanzhang@hit.edu.cn",
+            "name": "Fan Zhang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "fengxiazhou_hit@163.com",
+            "name": "Fengxia Zhou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Han Yang"
+        },
+        {
+            "name": "Jiqiu Wu"
+        }
+    ],
+    "description": "A tool for phage tail module detection and annotation.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence",
+                        "uri": "http://edamontology.org/data_2044"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.microbiome-bigdata.com/PHISDetector/index/tools/PhageTailFinder",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T11:38:52.870744Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/HIT-ImmunologyLab/PhageTailFinder"
+        }
+    ],
+    "name": "PhageTailFinder",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FGENE.2023.947466",
+            "metadata": {
+                "abstract": "Decades of overconsumption of antimicrobials in the treatment and prevention of bacterial infections have resulted in the increasing emergence of drug-resistant bacteria, which poses a significant challenge to public health, driving the urgent need to find alternatives to conventional antibiotics. Bacteriophages are viruses infecting specific bacterial hosts, often destroying the infected bacterial hosts. Phages attach to and enter their potential hosts using their tail proteins, with the composition of the tail determining the range of potentially infected bacteria. To aid the exploitation of bacteriophages for therapeutic purposes, we developed the PhageTailFinder algorithm to predict tail-related proteins and identify the putative tail module in previously uncharacterized phages. The PhageTailFinder relies on a two-state hidden Markov model (HMM) to predict the probability of a given protein being tail-related. The process takes into account the natural modularity of phage tail-related proteins, rather than simply considering amino acid properties or secondary structures for each protein in isolation. The PhageTailFinder exhibited robust predictive power for phage tail proteins in novel phages due to this sequence-independent operation. The performance of the prediction model was evaluated in 13 extensively studied phages and a sample of 992 complete phages from the NCBI database. The algorithm achieved a high true-positive prediction rate (>80%) in over half (571) of the studied phages, and the ROC value was 0.877 using general models and 0.968 using corresponding morphologic models. It is notable that the median ROC value of 992 complete phages is more than 0.75 even for novel phages, indicating the high accuracy and specificity of the PhageTailFinder. When applied to a dataset containing 189,680 viral genomes derived from 11,810 bulk metagenomic human stool samples, the ROC value was 0.895. In addition, tail protein clusters could be identified for further studies by density-based spatial clustering of applications with the noise algorithm (DBSCAN). The developed PhageTailFinder tool can be accessed either as a web server (http://www.microbiome-bigdata.com/PHISDetector/index/tools/PhageTailFinder) or as a stand-alone program on a standard desktop computer (https://github.com/HIT-ImmunologyLab/PhageTailFinder).",
+                "authors": [
+                    {
+                        "name": "Chen C."
+                    },
+                    {
+                        "name": "Gan R."
+                    },
+                    {
+                        "name": "Ren C."
+                    },
+                    {
+                        "name": "Si Y."
+                    },
+                    {
+                        "name": "Wu J."
+                    },
+                    {
+                        "name": "Yang H."
+                    },
+                    {
+                        "name": "Yu L."
+                    },
+                    {
+                        "name": "Zhang F."
+                    },
+                    {
+                        "name": "Zhou F."
+                    }
+                ],
+                "date": "2023-01-23T00:00:00Z",
+                "journal": "Frontiers in Genetics",
+                "title": "PhageTailFinder: A tool for phage tail module detection and annotation"
+            },
+            "pmcid": "PMC9901426",
+            "pmid": "36755570"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Protein interaction experiment",
+            "uri": "http://edamontology.org/topic_3957"
+        },
+        {
+            "term": "Protein secondary structure",
+            "uri": "http://edamontology.org/topic_3542"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ]
+}
diff --git a/data/pharmacorank/pharmacorank.biotools.json b/data/pharmacorank/pharmacorank.biotools.json
new file mode 100644
index 0000000000000..b982608077740
--- /dev/null
+++ b/data/pharmacorank/pharmacorank.biotools.json
@@ -0,0 +1,115 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-01T22:55:08.776855Z",
+    "biotoolsCURIE": "biotools:pharmacorank",
+    "biotoolsID": "pharmacorank",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "wmclaughlin@som.geisinger.edu",
+            "name": "William A. McLaughlin",
+            "orcidid": "https://orcid.org/0000-0001-9234-9418",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Paul J. DePietro"
+        },
+        {
+            "name": "Sergey Gnilopyat"
+        },
+        {
+            "name": "Thomas K. Parry",
+            "orcidid": "https://orcid.org/0000-0003-4328-8203"
+        }
+    ],
+    "description": "The Pharmacorank Search Tool for the Retrieval of Prioritized Protein Drug Targets and Drug Repositioning Candidates According to Selected Diseases.",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "http://protein.som.geisinger.edu/Pharmacorank/Downloads/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://protein.som.geisinger.edu/Pharmacorank/",
+    "lastUpdate": "2023-02-01T22:55:08.779603Z",
+    "name": "Pharmacorank",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/BIOM12111559",
+            "metadata": {
+                "abstract": "© 2022 by the authors.We present the Pharmacorank search tool as an objective means to obtain prioritized protein drug targets and their associated medications according to user-selected diseases. This tool could be used to obtain prioritized protein targets for the creation of novel medications or to predict novel indications for medications that already exist. To prioritize the proteins associated with each disease, a gene similarity profiling method based on protein functions is implemented. The priority scores of the proteins are found to correlate well with the likelihoods that the associated medications are clinically relevant in the disease’s treatment. When the protein priority scores are plotted against the percentage of protein targets that are known to bind medications currently indicated to treat the disease, which we termed the pertinency score, a strong correlation was observed. The correlation coefficient was found to be 0.9978 when using a weighted second-order polynomial fit. As the highly predictive fit was made using a broad range of diseases, we were able to identify a general threshold for the pertinency score as a starting point for considering drug repositioning candidates. Several repositioning candidates are described for proteins that have high predicated pertinency scores, and these provide illustrative examples of the applications of the tool. We also describe focused reviews of repositioning candidates for Alzheimer’s disease. Via the tool’s URL, https://protein.som.geisinger.edu/Pharmacorank/, an open online interface is provided for interactive use; and there is a site for programmatic access.",
+                "authors": [
+                    {
+                        "name": "DePietro P.J."
+                    },
+                    {
+                        "name": "Gnilopyat S."
+                    },
+                    {
+                        "name": "McLaughlin W.A."
+                    },
+                    {
+                        "name": "Parry T.K."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Biomolecules",
+                "title": "The Pharmacorank Search Tool for the Retrieval of Prioritized Protein Drug Targets and Drug Repositioning Candidates According to Selected Diseases"
+            },
+            "pmcid": "PMC9687941",
+            "pmid": "36358909"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Pharmacology",
+            "uri": "http://edamontology.org/topic_0202"
+        },
+        {
+            "term": "Proteins",
+            "uri": "http://edamontology.org/topic_0078"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/pharokka/pharokka.biotools.json b/data/pharokka/pharokka.biotools.json
new file mode 100644
index 0000000000000..f0268da0f7227
--- /dev/null
+++ b/data/pharokka/pharokka.biotools.json
@@ -0,0 +1,133 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T12:31:17.004078Z",
+    "biotoolsCURIE": "biotools:pharokka",
+    "biotoolsID": "pharokka",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "george.bouras@adelaide.edu.au",
+            "name": "George Bouras",
+            "orcidid": "https://orcid.org/0000-0002-5885-4186",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Peter-John Wormald"
+        },
+        {
+            "name": "Sarah Vreugde"
+        },
+        {
+            "name": "Roshan Nepal",
+            "orcidid": "https://orcid.org/0000-0002-5469-3397"
+        }
+    ],
+    "description": "Pharokka is a rapid standardised annotation tool for bacteriophage genomes and metagenomes.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://pharokka.readthedocs.io/en/latest/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Antimicrobial resistance prediction",
+                    "uri": "http://edamontology.org/operation_3482"
+                },
+                {
+                    "term": "Formatting",
+                    "uri": "http://edamontology.org/operation_0335"
+                },
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "tRNA gene prediction",
+                    "uri": "http://edamontology.org/operation_0464"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/gbouras13/pharokka",
+    "language": [
+        "C",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-15T12:31:56.523982Z",
+    "license": "MIT",
+    "name": "Pharokka",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC776",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: In recent years, there has been an increasing interest in bacteriophages, which has led to growing numbers of bacteriophage genomic sequences becoming available. Consequently, there is a need for a rapid and consistent genomic annotation tool dedicated for bacteriophages. Existing tools either are not designed specifically for bacteriophages or are web- and email-based and require significant manual curation, which makes their integration into bioinformatic pipelines challenging. Pharokka was created to provide a tool that annotates bacteriophage genomes easily, rapidly and consistently with standards compliant outputs. Moreover, Pharokka requires only two lines of code to install and use and takes under 5 min to run for an average 50-kb bacteriophage genome. AVAILABILITY AND IMPLEMENTATION: Pharokka is implemented in Python and is available as a bioconda package using 'conda install -c bioconda pharokka'. The source code is available on GitHub (https://github.com/gbouras13/pharokka). Pharokka has been tested on Linux-64 and MacOSX machines and on Windows using a Linux Virtual Machine.",
+                "authors": [
+                    {
+                        "name": "Bouras G."
+                    },
+                    {
+                        "name": "Houtak G."
+                    },
+                    {
+                        "name": "Nepal R."
+                    },
+                    {
+                        "name": "Psaltis A.J."
+                    },
+                    {
+                        "name": "Vreugde S."
+                    },
+                    {
+                        "name": "Wormald P.-J."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Pharokka: a fast scalable bacteriophage annotation tool"
+            },
+            "pmcid": "PMC9805569",
+            "pmid": "36453861"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/phenocomb/phenocomb.biotools.json b/data/phenocomb/phenocomb.biotools.json
new file mode 100644
index 0000000000000..abe1ecfa60de2
--- /dev/null
+++ b/data/phenocomb/phenocomb.biotools.json
@@ -0,0 +1,92 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-20T00:26:06.912410Z",
+    "biotoolsCURIE": "biotools:phenocomb",
+    "biotoolsID": "phenocomb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "david.m.woods@cuanschutz.edu",
+            "name": "David M. Woods",
+            "orcidid": "http://orcid.org/0000-0002-6328-8107",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ann Strange"
+        },
+        {
+            "name": "Brian Thompson",
+            "orcidid": "http://orcid.org/0000-0003-0983-2762"
+        },
+        {
+            "name": "Carol Amato",
+            "orcidid": "http://orcid.org/0000-0002-4945-8819"
+        },
+        {
+            "name": "Emily Monk",
+            "orcidid": "http://orcid.org/0000-0003-1037-4172"
+        },
+        {
+            "name": "Paulo E. P. Burke",
+            "orcidid": "http://orcid.org/0000-0002-9640-299X"
+        }
+    ],
+    "description": "A discovery tool to assess complex phenotypes in high-dimension, single-cell datasets.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Filtering",
+                    "uri": "http://edamontology.org/operation_3695"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/SciOmicsLab/PhenoComb",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-20T00:26:06.915047Z",
+    "license": "Not licensed",
+    "name": "PhenoComb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioadv/vbac052"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cytometry",
+            "uri": "http://edamontology.org/topic_3934"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/phenotrack3d/phenotrack3d.biotools.json b/data/phenotrack3d/phenotrack3d.biotools.json
new file mode 100644
index 0000000000000..43f9d66808cbe
--- /dev/null
+++ b/data/phenotrack3d/phenotrack3d.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T12:25:18.401388Z",
+    "biotoolsCURIE": "biotools:phenotrack3d",
+    "biotoolsID": "phenotrack3d",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "christian.fournier@inrae.fr",
+            "name": "Christian Fournier",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "christophe.pradal@cirad.fr",
+            "name": "Christophe Pradal",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Benoit Daviet"
+        },
+        {
+            "name": "Romain Fernandez"
+        }
+    ],
+    "description": "An automatic high-throughput phenotyping pipeline to track maize organs over time.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Sequence alignment",
+                    "uri": "http://edamontology.org/operation_0292"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/openalea/phenotrack3d",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-15T12:25:18.403958Z",
+    "license": "Other",
+    "name": "PhenoTrack3D",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13007-022-00961-4",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. Results: We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10–355 plants. Conclusions: We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.",
+                "authors": [
+                    {
+                        "name": "Cabrera-Bosquet L."
+                    },
+                    {
+                        "name": "Daviet B."
+                    },
+                    {
+                        "name": "Fernandez R."
+                    },
+                    {
+                        "name": "Fournier C."
+                    },
+                    {
+                        "name": "Pradal C."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Plant Methods",
+                "title": "PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time"
+            },
+            "pmcid": "PMC9730636",
+            "pmid": "36482291"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/phers/phers.biotools.json b/data/phers/phers.biotools.json
new file mode 100644
index 0000000000000..3fc6ac452d309
--- /dev/null
+++ b/data/phers/phers.biotools.json
@@ -0,0 +1,130 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T20:45:22.979474Z",
+    "biotoolsCURIE": "biotools:phers",
+    "biotoolsID": "phers",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jakejhughey@gmail.com",
+            "name": "Jacob J. Hughey",
+            "orcidid": "http://orcid.org/0000-0002-1558-6089",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Layla Aref",
+            "orcidid": "http://orcid.org/0000-0002-4785-8042"
+        },
+        {
+            "name": "Lisa Bastarache",
+            "orcidid": "http://orcid.org/0000-0003-3020-447X"
+        }
+    ],
+    "description": "A tool using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://cloud.r-project.org/web/packages/phers/phers.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genetic mapping",
+                    "uri": "http://edamontology.org/operation_0282"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://phers.hugheylab.org",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-27T20:45:22.982117Z",
+    "license": "GPL-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://cloud.r-project.org/web/packages/phers/index.html"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/hugheylab/phers/"
+        }
+    ],
+    "name": "PheRS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac619",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: Electronic health record (EHR) data linked to DNA biobanks are a valuable resource for understanding the phenotypic effects of human genetic variation. We previously developed the phenotype risk score (PheRS) as an approach to quantify the extent to which a patient's clinical features resemble a given Mendelian disease. Using PheRS, we have uncovered novel associations between Mendelian disease-like phenotypes and rare genetic variants, and identified patients who may have undiagnosed Mendelian disease. Although the PheRS approach is conceptually simple, it involves multiple mapping steps and was previously only available as custom scripts, limiting the approach's usability. Thus, we developed the phers R package, a complete and user-friendly set of functions and maps for performing a PheRS-based analysis on linked clinical and genetic data. The package includes up-to-date maps between EHR-based phenotypes (i.e. ICD codes and phecodes), human phenotype ontology terms and Mendelian diseases. Starting with occurrences of ICD codes, the package enables the user to calculate PheRSs, validate the scores using case-control analyses, and perform genetic association analyses. By increasing PheRS's transparency and usability, the phers R package will help improve our understanding of the relationships between rare genetic variants and clinically meaningful human phenotypes. AVAILABILITY AND IMPLEMENTATION: The phers R package is free and open-source and available on CRAN and at https://phers.hugheylab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Aref L."
+                    },
+                    {
+                        "name": "Bastarache L."
+                    },
+                    {
+                        "name": "Hughey J.J."
+                    }
+                ],
+                "date": "2022-10-31T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants"
+            },
+            "pmcid": "PMC9620826",
+            "pmid": "36083022"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Rare diseases",
+            "uri": "http://edamontology.org/topic_3325"
+        }
+    ]
+}
diff --git a/data/phevir/phevir.biotools.json b/data/phevir/phevir.biotools.json
new file mode 100644
index 0000000000000..fe4b8b60a631e
--- /dev/null
+++ b/data/phevir/phevir.biotools.json
@@ -0,0 +1,93 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T12:18:34.527332Z",
+    "biotoolsCURIE": "biotools:phevir",
+    "biotoolsID": "phevir",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "skolnick@gatech.edu",
+            "name": "Jeffrey Skolnick",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Courtney Astore"
+        },
+        {
+            "name": "Hongyi Zhou"
+        }
+    ],
+    "description": "An artificial intelligence algorithm that predicts the molecular role of pathogens in complex human diseases.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Virulence prediction",
+                    "uri": "http://edamontology.org/operation_3461"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://sites.gatech.edu/cssb/phevir/",
+    "lastUpdate": "2023-02-15T12:18:34.529816Z",
+    "name": "PHEVIR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41598-022-25412-X",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Infectious diseases are known to cause a wide variety of post-infection complications. However, it’s been challenging to identify which diseases are most associated with a given pathogen infection. Using the recently developed LeMeDISCO approach that predicts comorbid diseases associated with a given set of putative mode of action (MOA) proteins and pathogen-human protein interactomes, we developed PHEVIR, an algorithm which predicts the corresponding human disease comorbidities of 312 viruses and 57 bacteria. These predictions provide an understanding of the molecular bases of complications and means of identifying appropriate drug targets to treat them. As an illustration of its power, PHEVIR is applied to identify putative driver pathogens and corresponding human MOA proteins for Type 2 diabetes, atherosclerosis, Alzheimer’s disease, and inflammatory bowel disease. Additionally, we explore the origins of the oncogenicity/oncolyticity of certain pathogens and the relationship between heart disease and influenza. The full PHEVIR database is available at https://sites.gatech.edu/cssb/phevir/.",
+                "authors": [
+                    {
+                        "name": "Astore C."
+                    },
+                    {
+                        "name": "Skolnick J."
+                    },
+                    {
+                        "name": "Zhou H."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "PHEVIR: an artificial intelligence algorithm that predicts the molecular role of pathogens in complex human diseases"
+            },
+            "pmcid": "PMC9719543",
+            "pmid": "36463386"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cardiology",
+            "uri": "http://edamontology.org/topic_3335"
+        },
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Rare diseases",
+            "uri": "http://edamontology.org/topic_3325"
+        }
+    ]
+}
diff --git a/data/photizo/photizo.biotools.json b/data/photizo/photizo.biotools.json
new file mode 100644
index 0000000000000..923588ec935d7
--- /dev/null
+++ b/data/photizo/photizo.biotools.json
@@ -0,0 +1,131 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-18T21:29:00.795555Z",
+    "biotoolsCURIE": "biotools:photizo",
+    "biotoolsID": "photizo",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "cdendrou@well.ox.ac.uk",
+            "name": "Calliope A. Dendrou",
+            "orcidid": "http://orcid.org/0000-0003-1179-4021",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Melissa Grant-Peters"
+        },
+        {
+            "name": "Charlotte Rich-Griffin",
+            "orcidid": "http://orcid.org/0000-0001-8212-9542"
+        },
+        {
+            "name": "Gianfelice Cinque",
+            "orcidid": "http://orcid.org/0000-0001-6801-8010"
+        },
+        {
+            "name": "Jonathan E. Grant-Peters",
+            "orcidid": "http://orcid.org/0000-0002-6383-1253"
+        }
+    ],
+    "description": "An open-source library for cross-sample analysis of FTIR spectroscopy data.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://github.com/DendrouLab/Photizo/blob/master/Photizo_documentation.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Principal component analysis",
+                    "uri": "http://edamontology.org/operation_3960"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Spectral analysis",
+                    "uri": "http://edamontology.org/operation_3214"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/DendrouLab/Photizo",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-18T21:29:00.798138Z",
+    "license": "Not licensed",
+    "name": "Photizo",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac346",
+            "metadata": {
+                "abstract": "© 2022 The Author(s) 2022. Published by Oxford University Press.Motivation: With continually improved instrumentation, Fourier transform infrared (FTIR) microspectroscopy can now be used to capture thousands of high-resolution spectra for chemical characterization of a sample. The spatially resolved nature of this method lends itself well to histological profiling of complex biological specimens. However, current software can make joint analysis of multiple samples challenging and, for large datasets, computationally infeasible. Results: To overcome these limitations, we have developed Photizo-an open-source Python library enabling high-Throughput spectral data pre-processing, visualization and downstream analysis, including principal component analysis, clustering, macromolecular quantification and mapping. Photizo can be used for analysis of data without a spatial component, as well as spatially resolved data, obtained e.g. by scanning mode IR microspectroscopy and IR imaging by focal plane array detector.",
+                "authors": [
+                    {
+                        "name": "Cinque G."
+                    },
+                    {
+                        "name": "Dendrou C.A."
+                    },
+                    {
+                        "name": "Grant-Peters J.E."
+                    },
+                    {
+                        "name": "Grant-Peters M."
+                    },
+                    {
+                        "name": "Rich-Griffin C."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-07-01T00:00:00Z",
+                "journal": "Bioinformatics",
+                "title": "Photizo: An open-source library for cross-sample analysis of FTIR spectroscopy data"
+            },
+            "pmcid": "PMC9237726",
+            "pmid": "35608303"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sample collections",
+            "uri": "http://edamontology.org/topic_3277"
+        }
+    ]
+}
diff --git a/data/phyldiag/phyldiag.biotools.json b/data/phyldiag/phyldiag.biotools.json
new file mode 100644
index 0000000000000..6cd4eab96d4e1
--- /dev/null
+++ b/data/phyldiag/phyldiag.biotools.json
@@ -0,0 +1,142 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-06T10:30:37.888943Z",
+    "biotoolsCURIE": "biotools:phyldiag",
+    "biotoolsID": "phyldiag",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hrc@bio.ens.psl.eu",
+            "name": "Hugues Roest Crollius",
+            "orcidid": "http://orcid.org/0000-0002-8209-173X",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ],
+            "url": "https://www.ibens.ens.fr/spip.php?article221"
+        },
+        {
+            "email": "jlucas@biologie.ens.fr",
+            "name": "Joseph MEX Lucas",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Developer"
+            ]
+        },
+        {
+            "name": "IBENS - DYOGEN Team",
+            "typeEntity": "Institute",
+            "url": "https://www.ibens.ens.fr/spip.php?rubrique43"
+        }
+    ],
+    "description": "PhylDiag computes synteny blocs between two genomes. From the comparison of two extant genomes and corresponding gene families, PhylDiag detects conserved segments, i.e. segments of chromosomes unbroken during evolution.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome analysis",
+                    "uri": "http://edamontology.org/operation_3918"
+                }
+            ]
+        },
+        {
+            "operation": [
+                {
+                    "term": "Genome comparison",
+                    "uri": "http://edamontology.org/operation_3209"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/DyogenIBENS/PhylDiag",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-03-06T10:41:14.539429Z",
+    "license": "CECILL-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/DyogenIBENS/PhylDiag"
+        }
+    ],
+    "maturity": "Emerging",
+    "name": "PhylDiag",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "alouis",
+    "publication": [
+        {
+            "doi": "10.1186/1471-2105-15-268",
+            "metadata": {
+                "abstract": "Background: Extant genomes share regions where genes have the same order and orientation, which are thought to arise from the conservation of an ancestral order of genes during evolution. Such regions of so-called conserved synteny, or synteny blocks, must be precisely identified and quantified, as a prerequisite to better understand the evolutionary history of genomes.Results: Here we describe PhylDiag, a software that identifies statistically significant synteny blocks in pairwise comparisons of eukaryote genomes. Compared to previous methods, PhylDiag uses gene trees to define gene homologies, thus allowing gene deletions to be considered as events that may break the synteny. PhylDiag also accounts for gene orientations, blocks of tandem duplicates and lineage specific de novo gene births. Starting from two genomes and the corresponding gene trees, PhylDiag returns synteny blocks with gaps less than or equal to the maximum gap parameter gapmax. This parameter is theoretically estimated, and together with a utility to graphically display results, contributes to making PhylDiag a user friendly method. In addition, putative synteny blocks are subject to a statistical validation to verify that they are unlikely to be due to a random combination of genes.Conclusions: We benchmark several known metrics to measure 2D-distances in a matrix of homologies and we compare PhylDiag to i-ADHoRe 3.0 on real and simulated data. We show that PhylDiag correctly identifies small synteny blocks even with insertions, deletions, incorrect annotations or micro-inversions. Finally, PhylDiag allowed us to identify the most relevant distance metric for 2D-distance calculation between homologies.",
+                "authors": [
+                    {
+                        "name": "Crollius H.R."
+                    },
+                    {
+                        "name": "Lucas J.M.E.X."
+                    },
+                    {
+                        "name": "Muffato M."
+                    }
+                ],
+                "citationCount": 8,
+                "date": "2014-08-08T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "PhylDiag: Identifying complex synteny blocks that include tandem duplications using phylogenetic gene trees"
+            },
+            "pmcid": "PMC4155083",
+            "pmid": "25103980",
+            "type": [
+                "Primary"
+            ]
+        },
+        {
+            "doi": "10.1371/journal.pone.0180198",
+            "metadata": {
+                "abstract": "A conserved segment, i.e. a segment of chromosome unbroken during evolution, is an important operational concept in comparative genomics. Until now, algorithms that are designed to identify conserved segments often return synteny blocks that overlap, synteny blocks that include micro-rearrangements or synteny blocks erroneously short. Here we present definitions of conserved segments and synteny blocks independent of any heuristic method and we describe four new post-processing strategies to refine synteny blocks into accurate conserved segments. The first strategy identifies micro-rearrangements, the second strategy identifies mono-genic conserved segments, the third returns non-overlapping segments and the fourth repairs incorrect ruptures of synteny. All these refinements are implemented in a new version of PhylDiag that has been benchmarked against i-ADHoRe 3.0 and Cyntenator, based on a realistic simulated evolution and true simulated conserved segments.",
+                "authors": [
+                    {
+                        "name": "Crollius H.R."
+                    },
+                    {
+                        "name": "Lucas J.M.E.X."
+                    }
+                ],
+                "citationCount": 2,
+                "date": "2017-07-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "High precision detection of conserved segments from synteny blocks"
+            },
+            "pmcid": "PMC5495381",
+            "pmid": "28671949",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "genomicus",
+            "type": "usedBy"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Comparative genomics",
+            "uri": "http://edamontology.org/topic_0797"
+        }
+    ]
+}
diff --git a/data/phytest/phytest.biotools.json b/data/phytest/phytest.biotools.json
new file mode 100644
index 0000000000000..5f2204221c8dc
--- /dev/null
+++ b/data/phytest/phytest.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T15:04:28.613999Z",
+    "biotoolsCURIE": "biotools:phytest",
+    "biotoolsID": "phytest",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Robert Turnbull"
+        },
+        {
+            "name": "Simon Mutch"
+        },
+        {
+            "name": "Sebastian Duchene",
+            "orcidid": "https://orcid.org/0000-0002-2863-0907"
+        },
+        {
+            "name": "Wytamma Wirth",
+            "orcidid": "https://orcid.org/0000-0001-7070-0078"
+        }
+    ],
+    "description": "Quality Control for Phylogenetic Analyses.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://phytest-devs.github.io/phytest/"
+        },
+        {
+            "type": [
+                "Training material"
+            ],
+            "url": "https://github.com/phytest-devs/phytest-temporal-signal-example"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phylogenetic analysis",
+                    "uri": "http://edamontology.org/operation_0324"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Sequencing quality control",
+                    "uri": "http://edamontology.org/operation_3218"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/phytest-devs/phytest",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-27T15:04:37.821762Z",
+    "license": "MIT",
+    "name": "Phytest",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac664",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The ability to automatically conduct quality control checks on phylogenetic analyses is becoming more important with the increase in genetic sequencing and the use of real-time pipelines e.g. in the SARS-CoV-2 era. Implementations of real-time phylogenetic analyses require automated testing to make sure that problems in the data are caught automatically within analysis pipelines and in a timely manner. Here, we present Phytest (version 1.1) a tool for automating quality control checks on sequences, trees and metadata during phylogenetic analyses. RESULTS: Phytest is a phylogenetic analysis testing program that easily integrates into existing phylogenetic pipelines. We demonstrate the utility of Phytest with real-world examples. AVAILABILITY AND IMPLEMENTATION: Phytest source code is available on GitHub (https://github.com/phytest-devs/phytest) and can be installed via PyPI with the command 'pip install phytest'. Extensive documentation can be found at https://phytest-devs.github.io/phytest/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Duchene S."
+                    },
+                    {
+                        "name": "Mutch S."
+                    },
+                    {
+                        "name": "Turnbull R."
+                    },
+                    {
+                        "name": "Wirth W."
+                    }
+                ],
+                "date": "2022-11-15T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Phytest: quality control for phylogenetic analyses"
+            },
+            "pmcid": "PMC9676013",
+            "pmid": "36205601"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Data quality management",
+            "uri": "http://edamontology.org/topic_3572"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/piggtex/piggtex.biotools.json b/data/piggtex/piggtex.biotools.json
new file mode 100644
index 0000000000000..c46259ee84202
--- /dev/null
+++ b/data/piggtex/piggtex.biotools.json
@@ -0,0 +1,114 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-01T22:43:31.797087Z",
+    "biotoolsCURIE": "biotools:piggtex",
+    "biotoolsID": "piggtex",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Jinyan Teng"
+        },
+        {
+            "name": "Lingzhao Fang"
+        },
+        {
+            "name": "Yahui Gao"
+        }
+    ],
+    "description": "A compendium of genetic regulatory effects across pig tissues.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene name",
+                        "uri": "http://edamontology.org/data_2299"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Identifier",
+                        "uri": "http://edamontology.org/data_0842"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Genetic mapping",
+                    "uri": "http://edamontology.org/operation_0282"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://piggtex.farmgtex.org/",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-02-01T22:43:31.799591Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/FarmGTEx/PigGTEx-Pipeline-v0"
+        }
+    ],
+    "name": "PigGTEx",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.21203/RS.3.RS-2261680/V1"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/pitha/pitha.biotools.json b/data/pitha/pitha.biotools.json
new file mode 100644
index 0000000000000..ff016037a5694
--- /dev/null
+++ b/data/pitha/pitha.biotools.json
@@ -0,0 +1,114 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-01T22:34:02.106189Z",
+    "biotoolsCURIE": "biotools:pitha",
+    "biotoolsID": "pitha",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "czhang5@unl.edu",
+            "name": "Chi Zhang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Shide Liang"
+        }
+    ],
+    "description": "A Webtool to Predict Immunogenicity for Humanized and Fully Human Therapeutic Antibodies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "PDB ID",
+                        "uri": "http://edamontology.org/data_1127"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Protein structure",
+                        "uri": "http://edamontology.org/data_1460"
+                    },
+                    "format": [
+                        {
+                            "term": "PDB",
+                            "uri": "http://edamontology.org/format_1476"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                },
+                {
+                    "term": "Protein structure prediction",
+                    "uri": "http://edamontology.org/operation_0474"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://sysbio.unl.edu/PITHA",
+    "lastUpdate": "2023-02-01T22:34:02.108326Z",
+    "name": "PITHA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1007/978-1-0716-2609-2_7",
+            "metadata": {
+                "abstract": "© 2023, Springer Science+Business Media, LLC, part of Springer Nature.Immunogenicity is an important concern to therapeutic antibodies during antibody design and development. Based on the co-crystal structures of idiotypic antibodies and their antibodies, one can see that anti-idiotypic antibodies usually bind the complementarity-determining regions (CDR) of idiotypic antibodies. Sequence and structural features, such as cavity volume at the CDR region and hydrophobicity of CDR-H3 loop region, were identified for distinguishing immunogenic antibodies from non-immunogenic antibodies. These features were integrated together with a machine learning platform to predict immunogenicity for humanized and fully human therapeutic antibodies (PITHA). This method achieved an accuracy of 83% in a leave-one-out experiment for 29 therapeutic antibodies with available crystal structures. The web server of this method is accessible at http://mabmedicine.com/PITHA or http://sysbio.unl.edu/PITHA. This method, as a step of computer-aided antibody design, helps evaluate the safety of new therapeutic antibody, which can save time and money during the therapeutic antibody development.",
+                "authors": [
+                    {
+                        "name": "Liang S."
+                    },
+                    {
+                        "name": "Zhang C."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Methods in Molecular Biology",
+                "title": "PITHA: A Webtool to Predict Immunogenicity for Humanized and Fully Human Therapeutic Antibodies"
+            },
+            "pmid": "36346590"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Data mining",
+            "uri": "http://edamontology.org/topic_3473"
+        },
+        {
+            "term": "Human biology",
+            "uri": "http://edamontology.org/topic_2815"
+        },
+        {
+            "term": "Immunogenetics",
+            "uri": "http://edamontology.org/topic_3930"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/plantintron/plantintron.biotools.json b/data/plantintron/plantintron.biotools.json
new file mode 100644
index 0000000000000..037d5032de00c
--- /dev/null
+++ b/data/plantintron/plantintron.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T12:46:52.895357Z",
+    "biotoolsCURIE": "biotools:plantintron",
+    "biotoolsID": "plantintron",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhaijx@sustech.edu.cn",
+            "name": "Jixian Zhai",
+            "orcidid": "http://orcid.org/0000-0002-0217-0666",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Hong Zhang",
+            "orcidid": "http://orcid.org/0000-0001-9780-431X"
+        },
+        {
+            "name": "Jinbu Jia",
+            "orcidid": "http://orcid.org/0000-0002-7932-7860"
+        }
+    ],
+    "description": "Plant Intron Splicing Efficiency Database. \nSearch gene intron ratio levels from plants public RNA-Seq libraries",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene name",
+                        "uri": "http://edamontology.org/data_2299"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                },
+                {
+                    "term": "Splicing analysis",
+                    "uri": "http://edamontology.org/operation_2499"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://plantintron.com/",
+    "lastUpdate": "2023-02-26T12:46:52.897911Z",
+    "name": "plantintron",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1007/s11427-022-2193-3",
+            "metadata": {
+                "abstract": "© 2022, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature.Intron retention is the most common alternative splicing event in plants and plays a crucial role in the responses of plants to environmental signals. Despite a large number of RNA-seq libraries from different treatments and genetic mutants stored in public domains, a resource for querying the intron-splicing ratio of individual intron is still required. Here, we established the first-ever large-scale splicing efficiency database in any organism. Our database includes over 57,000 plant public RNA-seq libraries, comprising 25,283 from Arabidopsis, 17,789 from maize, 10,710 from rice, and 3,974 from soybean, and covers a total of 1.6 million introns in these four species. In addition, we manually curated and annotated all the mutant- and treatment-related libraries as well as their matched controls included in our library collection, and added graphics to display intron-splicing efficiency across various tissues, developmental stages, and stress-related conditions. The result is a large collection of 3,313 treatment conditions and 3,594 genetic mutants for discovering differentially regulated splicing efficiency. Our online database can be accessed at https://plantintron.com/.",
+                "authors": [
+                    {
+                        "name": "Jia J."
+                    },
+                    {
+                        "name": "Zhai J."
+                    },
+                    {
+                        "name": "Zhang H."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Science China Life Sciences",
+                "title": "Plant Intron-Splicing Efficiency Database (PISE): exploring splicing of ∼1,650,000 introns in Arabidopsis, maize, rice, and soybean from ∼57,000 public RNA-seq libraries"
+            },
+            "pmid": "36409390"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/pldbpred/pldbpred.biotools.json b/data/pldbpred/pldbpred.biotools.json
new file mode 100644
index 0000000000000..50519b1146682
--- /dev/null
+++ b/data/pldbpred/pldbpred.biotools.json
@@ -0,0 +1,115 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-01T22:28:10.453753Z",
+    "biotoolsCURIE": "biotools:pldbpred",
+    "biotoolsID": "pldbpred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "prabina.meher@icar.gov.in",
+            "name": "Prabina Kumar Meher",
+            "orcidid": "https://orcid.org/0000-0002-7098-8785",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ajit Gupta"
+        },
+        {
+            "name": "Rajender Parsad"
+        },
+        {
+            "name": "Sanchita Naha"
+        },
+        {
+            "name": "Soumen Pal"
+        },
+        {
+            "name": "Upendra Kumar Pradhan"
+        }
+    ],
+    "description": "A novel computational model for discovery of DNA binding proteins in plants.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "DNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3900"
+                },
+                {
+                    "term": "Ecological modelling",
+                    "uri": "http://edamontology.org/operation_3946"
+                },
+                {
+                    "term": "Subcellular localisation prediction",
+                    "uri": "http://edamontology.org/operation_2489"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://iasri-sg.icar.gov.in/pldbpred/",
+    "lastUpdate": "2023-02-01T22:28:10.456287Z",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://iasri-sg.icar.gov.in/pldbpred/source_code.php"
+        }
+    ],
+    "name": "PlDBPred",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC483",
+            "pmid": "36416116"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/pleistodist/pleistodist.biotools.json b/data/pleistodist/pleistodist.biotools.json
new file mode 100644
index 0000000000000..2622842a7f6b9
--- /dev/null
+++ b/data/pleistodist/pleistodist.biotools.json
@@ -0,0 +1,88 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-23T09:36:49.074948Z",
+    "biotoolsCURIE": "biotools:pleistodist",
+    "biotoolsID": "pleistodist",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "David J.X. Tan",
+            "orcidid": "http://orcid.org/0000-0001-7019-7871"
+        },
+        {
+            "name": "Michael J. Andersen",
+            "orcidid": "http://orcid.org/0000-0002-7220-5588"
+        }
+    ],
+    "description": "A toolbox for visualising and quantifying the effects of Pleistocene sea-level change on island archipelagos.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "Sequence assembly visualisation",
+                    "uri": "http://edamontology.org/operation_3184"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/g33k5p34k/PleistoDistR",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-23T09:36:49.077566Z",
+    "license": "MIT",
+    "name": "PleistoDist",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1111/2041-210X.14024",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.Pleistocene sea-level change played a significant role in the evolution and assembly of island biotas. The formation of land bridges between islands during Quaternary glacial maxima, when sea levels were more than 120 metres below present-day sea levels, often facilitated historical dispersal and gene flow between islands that are today geographically disconnected. Despite the importance of Pleistocene sea-level change, few software packages exist that model the effects of this phenomenon in a standardised and generalised manner. Here, we present PleistoDist, an R package that allows users to visualise and quantify the effects of Pleistocene sea-level change on islands over time, and test multiple temporally explicit hypotheses of inter-island dispersal and community assembly. Re-analysing published datasets, we demonstrate how using PleistoDist to account for historical sea-level change can provide greater explanatory power when analysing extant island communities, and show how population genetic simulations can be used to generate spatiotemporally explicit neutral expectations of population genetic structure across island archipelagos.",
+                "authors": [
+                    {
+                        "name": "Andersen M.J."
+                    },
+                    {
+                        "name": "Gyllenhaal E.F."
+                    },
+                    {
+                        "name": "Tan D.J.X."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Methods in Ecology and Evolution",
+                "title": "PleistoDist: A toolbox for visualising and quantifying the effects of Pleistocene sea-level change on island archipelagos"
+            }
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Population genetics",
+            "uri": "http://edamontology.org/topic_3056"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/plexusnet/plexusnet.biotools.json b/data/plexusnet/plexusnet.biotools.json
new file mode 100644
index 0000000000000..cabea121a2760
--- /dev/null
+++ b/data/plexusnet/plexusnet.biotools.json
@@ -0,0 +1,110 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T11:33:17.874398Z",
+    "biotoolsCURIE": "biotools:plexusnet",
+    "biotoolsID": "plexusnet",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Daniel L. Rubin"
+        },
+        {
+            "name": "Joseph C. Liao"
+        },
+        {
+            "name": "Mahmoud Abbas"
+        },
+        {
+            "name": "Okyaz Eminaga",
+            "orcidid": "https://orcid.org/0000-0003-0861-3138"
+        }
+    ],
+    "description": "A neural network architectural concept for medical image classification.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/oeminaga/PlexusNet.git",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T11:33:17.879877Z",
+    "license": "GPL-3.0",
+    "name": "PlexusNet",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2023.106594",
+            "metadata": {
+                "abstract": "State-of-the-art (SOTA) convolutional neural network models have been widely adapted in medical imaging and applied to address different clinical problems. However, the complexity and scale of such models may not be justified in medical imaging and subject to the available resource budget. Further increasing the number of representative feature maps for the classification task decreases the model explainability. The current data normalization practice is fixed prior to model development and discounting the specification of the data domain. Acknowledging these issues, the current work proposed a new scalable model family called PlexusNet; the block architecture and model scaling by the network's depth, width, and branch regulate PlexusNet's architecture. The efficient computation costs outlined the dimensions of PlexusNet scaling and design. PlexusNet includes a new learnable data normalization algorithm for better data generalization. We applied a simple yet effective neural architecture search to design PlexusNet tailored to five clinical classification problems that achieve a performance noninferior to the SOTA models ResNet-18 and EfficientNet B0/1. It also does so with lower parameter capacity and representative feature maps in ten-fold ranges than the smallest SOTA models with comparable performance. The visualization of representative features revealed distinguishable clusters associated with categories based on latent features generated by PlexusNet. The package and source code are at https://github.com/oeminaga/PlexusNet.git.",
+                "authors": [
+                    {
+                        "name": "Abbas M."
+                    },
+                    {
+                        "name": "Brooks J.D."
+                    },
+                    {
+                        "name": "Eminaga O."
+                    },
+                    {
+                        "name": "Laurie M."
+                    },
+                    {
+                        "name": "Liao J.C."
+                    },
+                    {
+                        "name": "Rubin D.L."
+                    },
+                    {
+                        "name": "Shen J."
+                    }
+                ],
+                "date": "2023-03-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "PlexusNet: A neural network architectural concept for medical image classification"
+            },
+            "pmid": "36753979"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Echography",
+            "uri": "http://edamontology.org/topic_3954"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        }
+    ]
+}
diff --git a/data/plinkr/plinkr.biotools.json b/data/plinkr/plinkr.biotools.json
new file mode 100644
index 0000000000000..1cd81a7875834
--- /dev/null
+++ b/data/plinkr/plinkr.biotools.json
@@ -0,0 +1,31 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-07T08:44:41.107315Z",
+    "biotoolsCURIE": "biotools:plinkr",
+    "biotoolsID": "plinkr",
+    "cost": "Free of charge",
+    "description": "R package to work with PLINK",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/AJResearchGroup/plinkr",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-07T08:48:33.821538Z",
+    "license": "GPL-3.0",
+    "maturity": "Mature",
+    "name": "plinkr",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "richelbilderbeek",
+    "toolType": [
+        "Library"
+    ],
+    "version": [
+        "0.20.2"
+    ]
+}
diff --git a/data/plmsnosite/plmsnosite.biotools.json b/data/plmsnosite/plmsnosite.biotools.json
new file mode 100644
index 0000000000000..ce4299c98d5c0
--- /dev/null
+++ b/data/plmsnosite/plmsnosite.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T11:26:52.384879Z",
+    "biotoolsCURIE": "biotools:plmsnosite",
+    "biotoolsID": "plmsnosite",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dbkc@mtu.edu",
+            "name": "Dukka B. KC",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Hiroto Saigo"
+        },
+        {
+            "name": "Pawel Pratyush"
+        },
+        {
+            "name": "Suresh Pokharel"
+        }
+    ],
+    "description": "An ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/KCLabMTU/pLMSNOSite",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T11:26:52.389474Z",
+    "license": "Apache-2.0",
+    "name": "pLMSNOSite",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-023-05164-9",
+            "metadata": {
+                "abstract": "Background: Protein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biological processes including immune response, protein stability, transcription regulation, post translational regulation, DNA damage repair, redox regulation, and is an emerging paradigm of redox signaling for protection against oxidative stress. The development of robust computational tools to predict protein SNO sites would contribute to further interpretation of the pathological and physiological mechanisms of SNO. Results: Using an intermediate fusion-based stacked generalization approach, we integrated embeddings from supervised embedding layer and contextualized protein language model (ProtT5) and developed a tool called pLMSNOSite (protein language model-based SNO site predictor). On an independent test set of experimentally identified SNO sites, pLMSNOSite achieved values of 0.340, 0.735 and 0.773 for MCC, sensitivity and specificity respectively. These results show that pLMSNOSite performs better than the compared approaches for the prediction of S-nitrosylation sites. Conclusion: Together, the experimental results suggest that pLMSNOSite achieves significant improvement in the prediction performance of S-nitrosylation sites and represents a robust computational approach for predicting protein S-nitrosylation sites. pLMSNOSite could be a useful resource for further elucidation of SNO and is publicly available at https://github.com/KCLabMTU/pLMSNOSite.",
+                "authors": [
+                    {
+                        "name": "Kc D.B."
+                    },
+                    {
+                        "name": "Pokharel S."
+                    },
+                    {
+                        "name": "Pratyush P."
+                    },
+                    {
+                        "name": "Saigo H."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model"
+            },
+            "pmcid": "PMC9909867",
+            "pmid": "36755242"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Protein modifications",
+            "uri": "http://edamontology.org/topic_0601"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/pneumokity/pneumokity.biotools.json b/data/pneumokity/pneumokity.biotools.json
new file mode 100644
index 0000000000000..462e652b62216
--- /dev/null
+++ b/data/pneumokity/pneumokity.biotools.json
@@ -0,0 +1,134 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T11:22:07.301086Z",
+    "biotoolsCURIE": "biotools:pneumokity",
+    "biotoolsID": "pneumokity",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "norman.fry@ukhsa.gov.uk",
+            "name": "Norman K. Fry",
+            "orcidid": "https://orcid.org/0000-0003-4862-6507",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Carmen L. Sheppard",
+            "orcidid": "https://orcid.org/0000-0002-2699-9057"
+        },
+        {
+            "name": "Catherine Satzke",
+            "orcidid": "https://orcid.org/0000-0003-3164-8849"
+        },
+        {
+            "name": "Sam Manna",
+            "orcidid": "https://orcid.org/0000-0002-7694-9605"
+        }
+    ],
+    "description": "A fast, flexible, specific, and sensitive tool for Streptococcus pneumoniae serotype screening and mixed serotype detection from genome sequence data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/CarmenSheppard/PneumoKITy",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T11:22:07.305689Z",
+    "license": "Not licensed",
+    "name": "PneumoKITy",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1099/MGEN.0.000904",
+            "metadata": {
+                "abstract": "Determination of serotypes of Streptococcus pneumoniae is essential for monitoring current vaccine programmes. Since October 2017, pneumococcal serotypes in England have been derived from whole genome sequencing (WGS) data using our bioinformatic tool PneumoCaT. That tool was designed for serotype determination from pure cultures in a reference laboratory. To help determine multiple serotypes in pneumococcal carriage samples, we developed a new software tool named PneumoK-ITy (Pneumococcal K-mer Integrated Typing) that uses the powerful Mash k-mer screening method for pneumococcal serotyp-ing. Mash k-mer screening is more sequence specific and much faster than the mapping method used in PneumoCaT and can determine 54 (58.1 %) of the 93 serotypes in the SSI Diagnostica phenotypical serotyping scheme to type level with the remain-der called to serogroup or subgroup level (e.g., 11A/D). PneumoKITy can be run on both FastQ and assembly input, requiring up to 11× less memory and running up to 29× faster than the current version of PneumoCaT (1.2.1) on FastQ files. PneumoK-ITy can be used as a rapid, flexible serotype screening method which adds sensitive detection of mixed serotypes, e.g., for nasopharyngeal carriage studies where the presence of multiple serotypes is common. PneumoKITy’s ability to function from assembly file, for pure culture serotype detection, increases its speed. This speed potentially enables the software to be run using low infrastructure overhead via web-based platforms. PneumoKITy could be used as a fast initial screening method with other tools used for those serotypes that could not be fully determined to type level if necessary. PneumoKITy was found to be highly accurate and sensitive when run on a panel of FastQ files derived from mixed cultures with all serotypes in 47/51 (92.2 %) of samples being accurately detected. PneumoKITy was also able to accurately estimate the relative abundance of serotypes in the same sample. Estimates being within a mean relative abundance of 1.5 % of the expected abundance in mixtures with known concentrations. PneumoKITy was able to detect minor serotypes with expected abundance of 1 % in the known mixture serotypes. PneumoKITy is a rapid, flexible tool with wide-ranging applications outside of the pure-culture, reference laboratory serotyping remit of PneumoCaT.",
+                "authors": [
+                    {
+                        "name": "Amin-Chowdhury Z."
+                    },
+                    {
+                        "name": "Bertran M."
+                    },
+                    {
+                        "name": "Fry N.K."
+                    },
+                    {
+                        "name": "Groves N."
+                    },
+                    {
+                        "name": "Ladhani S."
+                    },
+                    {
+                        "name": "Litt D.J."
+                    },
+                    {
+                        "name": "Manna S."
+                    },
+                    {
+                        "name": "Satzke C."
+                    },
+                    {
+                        "name": "Sheppard C.L."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Microbial Genomics",
+                "title": "PneumoKITy: A fast, flexible, specific, and sensitive tool for Streptococcus pneumoniae serotype screening and mixed serotype detection from genome sequence data"
+            },
+            "pmcid": "PMC9837567",
+            "pmid": "36748701"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Gene structure",
+            "uri": "http://edamontology.org/topic_0114"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/poagnet/poagnet.biotools.json b/data/poagnet/poagnet.biotools.json
new file mode 100644
index 0000000000000..60110b6d799aa
--- /dev/null
+++ b/data/poagnet/poagnet.biotools.json
@@ -0,0 +1,88 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T12:11:47.004249Z",
+    "biotoolsCURIE": "biotools:poagnet",
+    "biotoolsID": "poagnet",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yip4002@med.cornell.edu",
+            "name": "Yifan Peng",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Lei Liu PhD"
+        },
+        {
+            "name": "Sarah H. Van Tassel MD"
+        },
+        {
+            "name": "Mingquan Lin PhD",
+            "orcidid": "https://orcid.org/0000-0003-0862-6588"
+        }
+    ],
+    "description": "Primary Open-Angle Glaucoma Diagnosis from Optic Disc Photographs Using a Siamese Network.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/bionlplab/poagnet",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-15T12:11:47.006960Z",
+    "license": "BSD-3-Clause",
+    "name": "POAGNet",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.XOPS.2022.100209",
+            "pmcid": "PMC9754976",
+            "pmid": "36531584"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        }
+    ]
+}
diff --git a/data/pocketoptimizer/pocketoptimizer.biotools.json b/data/pocketoptimizer/pocketoptimizer.biotools.json
new file mode 100644
index 0000000000000..4de87b6248c31
--- /dev/null
+++ b/data/pocketoptimizer/pocketoptimizer.biotools.json
@@ -0,0 +1,130 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-31T13:58:18.710049Z",
+    "biotoolsCURIE": "biotools:pocketoptimizer",
+    "biotoolsID": "pocketoptimizer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "birte.hoecker@uni-bayreuth.de",
+            "name": "Birte Höcker",
+            "orcidid": "https://orcid.org/0000-0002-8250-9462",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Dominik Lemm",
+            "orcidid": "https://orcid.org/0000-0002-8075-1765"
+        },
+        {
+            "name": "Jakob Noske",
+            "orcidid": "https://orcid.org/0000-0001-7763-2645"
+        },
+        {
+            "name": "Josef Paul Kynast",
+            "orcidid": "https://orcid.org/0000-0003-1412-1797"
+        },
+        {
+            "name": "Steffen Schmidt",
+            "orcidid": "https://orcid.org/0000-0001-9077-6010"
+        }
+    ],
+    "description": "A modular framework for computer-aided ligand-binding design.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Forcefield parameterisation",
+                    "uri": "http://edamontology.org/operation_3893"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                },
+                {
+                    "term": "Side chain modelling",
+                    "uri": "http://edamontology.org/operation_0480"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Hoecker-Lab/pocketoptimizer",
+    "language": [
+        "Perl",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-31T13:58:18.712523Z",
+    "license": "GPL-3.0",
+    "name": "PocketOptimizer",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/PRO.4516",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.The ability to design customized proteins to perform specific tasks is of great interest. We are particularly interested in the design of sensitive and specific small molecule ligand-binding proteins for biotechnological or biomedical applications. Computational methods can narrow down the immense combinatorial space to find the best solution and thus provide starting points for experimental procedures. However, success rates strongly depend on accurate modeling and energetic evaluation. Not only intra- but also intermolecular interactions have to be considered. To address this problem, we developed PocketOptimizer, a modular computational protein design pipeline, that predicts mutations in the binding pockets of proteins to increase affinity for a specific ligand. Its modularity enables users to compare different combinations of force fields, rotamer libraries, and scoring functions. Here, we present a much-improved version––PocketOptimizer 2.0. We implemented a cleaner user interface, an extended architecture with more supported tools, such as force fields and scoring functions, a backbone-dependent rotamer library, as well as different improvements in the underlying algorithms. Version 2.0 was tested against a benchmark of design cases and assessed in comparison to the first version. Our results show how newly implemented features such as the new rotamer library can lead to improved prediction accuracy. Therefore, we believe that PocketOptimizer 2.0, with its many new and improved functionalities, provides a robust and versatile environment for the design of small molecule-binding pockets in proteins. It is widely applicable and extendible due to its modular framework. PocketOptimizer 2.0 can be downloaded at https://github.com/Hoecker-Lab/pocketoptimizer.",
+                "authors": [
+                    {
+                        "name": "Hocker B."
+                    },
+                    {
+                        "name": "Kynast J.P."
+                    },
+                    {
+                        "name": "Lemm D."
+                    },
+                    {
+                        "name": "Noske J."
+                    },
+                    {
+                        "name": "Schmidt S."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Protein Science",
+                "title": "PocketOptimizer 2.0: A modular framework for computer-aided ligand-binding design"
+            },
+            "pmcid": "PMC9793973",
+            "pmid": "36403089"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Protein folding, stability and design",
+            "uri": "http://edamontology.org/topic_0130"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ],
+    "version": [
+        "2.0"
+    ]
+}
diff --git a/data/podcall/podcall.biotools.json b/data/podcall/podcall.biotools.json
new file mode 100644
index 0000000000000..c35712630b2b5
--- /dev/null
+++ b/data/podcall/podcall.biotools.json
@@ -0,0 +1,126 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-31T13:52:38.907517Z",
+    "biotoolsCURIE": "biotools:podcall",
+    "biotoolsID": "podcall",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "marine.jeanmougin@rr-research.no",
+            "name": "Marine Jeanmougin",
+            "orcidid": "https://orcid.org/0000-0001-6239-4357",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Guro Elisabeth Lind"
+        },
+        {
+            "name": "Hans Petter Brodal"
+        },
+        {
+            "name": "Hege Marie Vedeld"
+        },
+        {
+            "name": "Heidi Dietrichson Pharo"
+        }
+    ],
+    "description": "Positive droplet calling and normalization of droplet digital PCR DNA methylation data.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://bioconductor.org/packages/release/bioc/manuals/PoDCall/man/PoDCall.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Methylation calling",
+                    "uri": "http://edamontology.org/operation_3919"
+                },
+                {
+                    "term": "PCR primer design",
+                    "uri": "http://edamontology.org/operation_0308"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://bioconductor.org/packages/PoDCall/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-31T13:52:38.925988Z",
+    "license": "GPL-3.0",
+    "name": "PoDCall",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC766",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Droplet digital PCR (ddPCR) holds great promises for investigating DNA methylation with high sensitivity. Yet, the lack of methods for analyzing ddPCR DNA methylation data has resulted in users processing the data manually at the expense of standardization. RESULTS: PoDCall is an R package performing automated calling of positive droplets, quantification and normalization of methylation levels in ddPCR experiments. A Shiny application provides users with an intuitive and interactive interface to access PoDCall functionalities. AVAILABILITY AND IMPLEMENTATION: The PoDCall R package is freely available on Bioconductor at https://bioconductor.org/packages/PoDCall/. The Shiny application can be executed from the R console using the wrapper function PoDCall::podcallShiny(). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Brodal H.P."
+                    },
+                    {
+                        "name": "Dietrichson Pharo H."
+                    },
+                    {
+                        "name": "Jeanmougin M."
+                    },
+                    {
+                        "name": "Lind G.E."
+                    },
+                    {
+                        "name": "Vedeld H.M."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "PoDCall: positive droplet calling and normalization of droplet digital PCR DNA methylation data"
+            },
+            "pmcid": "PMC9825742",
+            "pmid": "36448696"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "PCR experiment",
+            "uri": "http://edamontology.org/topic_3519"
+        },
+        {
+            "term": "Probes and primers",
+            "uri": "http://edamontology.org/topic_0632"
+        }
+    ]
+}
diff --git a/data/polishclr/polishclr.biotools.json b/data/polishclr/polishclr.biotools.json
new file mode 100644
index 0000000000000..c2febe1440415
--- /dev/null
+++ b/data/polishclr/polishclr.biotools.json
@@ -0,0 +1,95 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T11:16:13.050344Z",
+    "biotoolsCURIE": "biotools:polishclr",
+    "biotoolsID": "polishclr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "severin@iastate.edu",
+            "name": "Andrew Severin",
+            "orcidid": "https://orcid.org/0000-0002-8015-9665",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Amanda R Stahlke",
+            "orcidid": "https://orcid.org/0000-0001-5724-598X"
+        },
+        {
+            "name": "Anna K Childers",
+            "orcidid": "https://orcid.org/0000-0002-0747-8539"
+        },
+        {
+            "name": "Jennifer Chang",
+            "orcidid": "https://orcid.org/0000-0002-8381-3765"
+        }
+    ],
+    "description": "A Nextflow workflow for polishing PacBio CLR genome assemblies",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://isugifnf.github.io/polishCLR/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "Sequencing error detection",
+                    "uri": "http://edamontology.org/operation_3195"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/isugifNF/polishCLR",
+    "language": [
+        "Shell"
+    ],
+    "lastUpdate": "2023-03-18T11:16:13.054712Z",
+    "license": "Not licensed",
+    "name": "polishCLR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/GBE/EVAD020",
+            "pmid": "36792366"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/pollendetect/pollendetect.biotools.json b/data/pollendetect/pollendetect.biotools.json
new file mode 100644
index 0000000000000..5d13378eed4ea
--- /dev/null
+++ b/data/pollendetect/pollendetect.biotools.json
@@ -0,0 +1,135 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-31T13:46:42.463997Z",
+    "biotoolsCURIE": "biotools:pollendetect",
+    "biotoolsID": "pollendetect",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "lingmin@mail.hzau.edu.cn",
+            "name": "Ling Min",
+            "orcidid": "https://orcid.org/0000-0003-4278-0626",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "liqingyuan@webmail.hzau.edu.cn",
+            "name": "Qingyuan Li",
+            "orcidid": "https://orcid.org/0000-0002-8579-3390",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Zhihao Tan"
+        },
+        {
+            "name": "Jie Kong",
+            "orcidid": "https://orcid.org/0000-0003-2456-9595"
+        }
+    ],
+    "description": "An Open-Source Pollen Viability Status Recognition System Based on Deep Learning Neural Networks.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Tanzhihao1998/Identification-of-pollen-activity/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-31T13:46:42.466580Z",
+    "license": "Not licensed",
+    "name": "PollenDetect",
+    "operatingSystem": [
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/IJMS232113469",
+            "metadata": {
+                "abstract": "© 2022 by the authors.Pollen grains, the male gametophytes for reproduction in higher plants, are vulnerable to various stresses that lead to loss of viability and eventually crop yield. A conventional method for assessing pollen viability is manual counting after staining, which is laborious and hinders high-throughput screening. We developed an automatic detection tool (PollenDetect) to distinguish viable and nonviable pollen based on the YOLOv5 neural network, which is adjusted to adapt to the small target detection task. Compared with manual work, PollenDetect significantly reduced detection time (from approximately 3 min to 1 s for each image). Meanwhile, PollenDetect can maintain high detection accuracy. When PollenDetect was tested on cotton pollen viability, 99% accuracy was achieved. Furthermore, the results obtained using PollenDetect show that high temperature weakened cotton pollen viability, which is highly similar to the pollen viability results obtained using 2,3,5-triphenyltetrazolium formazan quantification. PollenDetect is an open-source software that can be further trained to count different types of pollen for research purposes. Thus, PollenDetect is a rapid and accurate system for recognizing pollen viability status, and is important for screening stress-resistant crop varieties for the identification of pollen viability and stress resistance genes during genetic breeding research.",
+                "authors": [
+                    {
+                        "name": "Aierxi A."
+                    },
+                    {
+                        "name": "Kong J."
+                    },
+                    {
+                        "name": "Li Q."
+                    },
+                    {
+                        "name": "Min L."
+                    },
+                    {
+                        "name": "Su F."
+                    },
+                    {
+                        "name": "Tan Z."
+                    },
+                    {
+                        "name": "Wang W."
+                    },
+                    {
+                        "name": "Yang J."
+                    },
+                    {
+                        "name": "Yang T."
+                    },
+                    {
+                        "name": "Yang W."
+                    },
+                    {
+                        "name": "Zhang X."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "International Journal of Molecular Sciences",
+                "title": "PollenDetect: An Open-Source Pollen Viability Status Recognition System Based on Deep Learning Neural Networks"
+            },
+            "pmcid": "PMC9653958",
+            "pmid": "36362251"
+        }
+    ],
+    "toolType": [
+        "Desktop application"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Cytometry",
+            "uri": "http://edamontology.org/topic_3934"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        }
+    ]
+}
diff --git a/data/polynote/polynote.biotools.json b/data/polynote/polynote.biotools.json
new file mode 100644
index 0000000000000..974f9a2c2c812
--- /dev/null
+++ b/data/polynote/polynote.biotools.json
@@ -0,0 +1,50 @@
+{
+    "additionDate": "2023-01-27T12:17:23.020048Z",
+    "biotoolsCURIE": "biotools:polynote",
+    "biotoolsID": "polynote",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "jeremyrsmith,  jonathanindig,omidmogasemi",
+            "url": "https://polynote.org/0.5.0/about/"
+        }
+    ],
+    "description": "Features\n\nPolynote is a different kind of notebook. It supports mixing multiple languages in one notebook, and sharing data between them seamlessly. It encourages reproducible notebooks with its immutable data model. (Learn more)\n\nHere are some of its unique features:",
+    "download": [
+        {
+            "type": "Software package",
+            "url": "https://github.com/polynote/polynote"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://polynote.org/latest/",
+    "lastUpdate": "2023-02-01T12:25:09.143210Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/polynote/polynote"
+        }
+    ],
+    "name": "Polynote",
+    "owner": "iacs-biocomputacion",
+    "version": [
+        "0.5.0"
+    ]
+}
diff --git a/data/popar/popar.biotools.json b/data/popar/popar.biotools.json
new file mode 100644
index 0000000000000..3a44936373ae8
--- /dev/null
+++ b/data/popar/popar.biotools.json
@@ -0,0 +1,92 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T12:06:31.193822Z",
+    "biotoolsCURIE": "biotools:popar",
+    "biotoolsID": "popar",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Gotway.Michael@mayo.edu",
+            "name": "Michael B. Gotway",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "dongaoma@asu.edu",
+            "name": "DongAo Ma",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "fhaghigh@asu.edu",
+            "name": "Fatemeh Haghighi",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "jianming.liang@asu.edu",
+            "name": "Jianming Liang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "jpang12@asu.edu",
+            "name": "Jiaxuan Pang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "mhossei2@asu.edu",
+            "name": "Mohammad Reza Hosseinzadeh Taher",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "nuislam@asu.edu",
+            "name": "Nahid Ul Islam",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Patch Order Prediction and Appearance Recovery for Self-supervised Medical Image Analysis.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Backbone modelling",
+                    "uri": "http://edamontology.org/operation_0479"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://GitHub.com/JLiangLab/POPAR",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-15T12:06:31.196354Z",
+    "license": "Other",
+    "name": "POPAR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1007/978-3-031-16852-9_8",
+            "pmcid": "PMC9728135",
+            "pmid": "36507898"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        }
+    ]
+}
diff --git a/data/porechop_abi/porechop_abi.biotools.json b/data/porechop_abi/porechop_abi.biotools.json
index 5d8c60cbe0b32..f0049aa481148 100644
--- a/data/porechop_abi/porechop_abi.biotools.json
+++ b/data/porechop_abi/porechop_abi.biotools.json
@@ -3,17 +3,57 @@
     "additionDate": "2022-12-07T14:31:52.494999Z",
     "biotoolsCURIE": "biotools:porechop_abi",
     "biotoolsID": "porechop_abi",
+    "confidence_flag": "tool",
     "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Quentin Bonenfant"
+        },
+        {
+            "name": "Hélène Touzet",
+            "orcidid": "http://orcid.org/0000-0001-5305-9987"
+        },
+        {
+            "name": "Laurent Noé",
+            "orcidid": "http://orcid.org/0000-0002-1170-8376"
+        }
+    ],
     "description": "Porechop_ABI (ab initio) is an extension of Porechop whose purpose is to process adapter sequences in ONT reads.",
     "editPermission": {
         "type": "private"
     },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Base-calling",
+                    "uri": "http://edamontology.org/operation_3185"
+                },
+                {
+                    "term": "Demultiplexing",
+                    "uri": "http://edamontology.org/operation_3933"
+                },
+                {
+                    "term": "Primer removal",
+                    "uri": "http://edamontology.org/operation_3237"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
     "homepage": "https://github.com/bonsai-team/Porechop_ABI",
     "language": [
         "C++",
         "Python"
     ],
-    "lastUpdate": "2022-12-07T14:31:52.497611Z",
+    "lastUpdate": "2023-02-27T14:44:21.978463Z",
     "license": "GPL-3.0",
     "link": [
         {
@@ -30,6 +70,13 @@
         "Mac"
     ],
     "owner": "pauffret",
+    "publication": [
+        {
+            "doi": "10.1093/bioadv/vbac085",
+            "pmcid": "PMC9869717",
+            "pmid": "36698762"
+        }
+    ],
     "toolType": [
         "Command-line tool"
     ],
diff --git a/data/ppi-miner/ppi-miner.biotools.json b/data/ppi-miner/ppi-miner.biotools.json
new file mode 100644
index 0000000000000..29d209ae9c4f4
--- /dev/null
+++ b/data/ppi-miner/ppi-miner.biotools.json
@@ -0,0 +1,133 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-31T13:41:32.515173Z",
+    "biotoolsCURIE": "biotools:ppi-miner",
+    "biotoolsID": "ppi-miner",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Feng-lei Li"
+        },
+        {
+            "name": "Lin Wang"
+        },
+        {
+            "name": "Xin-yue Ma"
+        },
+        {
+            "name": "Yong Cang"
+        },
+        {
+            "name": "Fang Bai",
+            "orcidid": "https://orcid.org/0000-0003-1468-5568"
+        }
+    ],
+    "description": "A Structure and Sequence Motif Co-Driven Protein-Protein Interaction Mining and Modeling Computational Method.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Backbone modelling",
+                    "uri": "http://edamontology.org/operation_0479"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                },
+                {
+                    "term": "Sequence motif recognition",
+                    "uri": "http://edamontology.org/operation_0239"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://bailab.siais.shanghaitech.edu.cn/services/ppi-miner",
+    "language": [
+        "Julia",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-31T13:41:32.517756Z",
+    "license": "GPL-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/Wang-Lin-boop/PPI-Miner"
+        }
+    ],
+    "name": "PPI-Miner",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCIM.2C01033",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society. All rights reserved.Protein-protein interactions (PPIs) play important roles in biological processes of life, and predicting PPIs becomes a critical scientific issue of concern. Most PPIs occur through small domains or motifs (fragments), which are challenging and laborious to map by standard biochemical approaches because they generally require the cloning of several truncation mutants. Here, we present a computational method, named as PPI-Miner, to fish potential protein interacting partners utilizing protein motifs as queries. In brief, this work first developed a motif-matching algorithm designed to identify the proteins that contain sequential or structural similar motifs with the given query motif. Being aligned to the query motif, the binding mode of the discovered motif and its receptor protein will be initially determined to be used to build PPI complexes accordingly. Eventually, a PPI complex structure could be built and optimized with a designed automatic protocol. Besides discovering PPIs, PPI-Miner can also be applied to other areas, i.e., the rational design of molecular glues and protein vaccines. In this work, PPI-Miner was employed to mine the potential cereblon (CRBN) substrates from human proteome. As a result, 1,739 candidates were predicted, and 16 of them have been experimentally validated in previous studies. The source code of PPI-Miner can be obtained from the GitHub repository (https://github.com/Wang-Lin-boop/PPI-Miner), the webserver is freely available for users (https://bailab.siais.shanghaitech.edu.cn/services/ppi-miner), and the database of predicted CRBN substrates is accessible at https://bailab.siais.shanghaitech.edu.cn/services/crbn-subslib.",
+                "authors": [
+                    {
+                        "name": "Bai F."
+                    },
+                    {
+                        "name": "Cang Y."
+                    },
+                    {
+                        "name": "Li F.-L."
+                    },
+                    {
+                        "name": "Ma X.-Y."
+                    },
+                    {
+                        "name": "Wang L."
+                    }
+                ],
+                "date": "2022-12-12T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "PPI-Miner: A Structure and Sequence Motif Co-Driven Protein-Protein Interaction Mining and Modeling Computational Method"
+            },
+            "pmid": "36448715"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein structural motifs and surfaces",
+            "uri": "http://edamontology.org/topic_0166"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ]
+}
diff --git a/data/pprint2/pprint2.biotools.json b/data/pprint2/pprint2.biotools.json
index 477133ffc37d8..dfce8e31935d5 100644
--- a/data/pprint2/pprint2.biotools.json
+++ b/data/pprint2/pprint2.biotools.json
@@ -1,22 +1,33 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2022-10-10T09:28:30.124663Z",
     "biotoolsCURIE": "biotools:pprint2",
     "biotoolsID": "pprint2",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "credit": [
         {
-            "name": "Dr Gajendra P.S. Raghava",
-            "url": "https://webs.iiitd.edu.in/raghava/pprint2/"
-        }
-    ],
-    "description": "A web server for predicting RNA interacting residues.",
-    "documentation": [
+            "email": "raghava@iiitd.ac.in",
+            "name": "Gajendra P.S. Raghava",
+            "orcidid": "http://orcid.org/0000-0002-8902-2876",
+            "typeEntity": "Person"
+        },
         {
-            "type": [
-                "General"
-            ],
-            "url": "https://webs.iiitd.edu.in/raghava/pprint2/"
+            "name": "Harshita Sahu"
+        },
+        {
+            "name": "Khushboo Bajaj"
+        },
+        {
+            "name": "Anjali Dhall",
+            "orcidid": "http://orcid.org/0000-0002-0400-2084"
+        },
+        {
+            "name": "Sumeet Patiyal",
+            "orcidid": "http://orcid.org/0000-0003-1358-292X"
         }
     ],
+    "description": "Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile.",
     "editPermission": {
         "type": "private"
     },
@@ -24,36 +35,91 @@
         {
             "operation": [
                 {
-                    "term": "Analysis",
-                    "uri": "http://edamontology.org/operation_2945"
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Protein structure validation",
+                    "uri": "http://edamontology.org/operation_0321"
+                },
+                {
+                    "term": "RNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3901"
                 }
             ]
         }
     ],
     "homepage": "https://webs.iiitd.edu.in/raghava/pprint2/",
-    "lastUpdate": "2022-10-10T09:28:33.538494Z",
+    "language": [
+        "Perl",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T11:10:51.301536Z",
+    "license": "GPL-3.0",
     "link": [
         {
             "type": [
-                "Software catalogue"
+                "Repository"
             ],
-            "url": "https://webs.iiitd.edu.in/raghava/pprint2/"
+            "url": "https://github.com/raghavagps/pprint2"
         }
     ],
-    "name": "pprint2",
+    "name": "Pprint2",
     "operatingSystem": [
         "Linux",
         "Mac",
         "Windows"
     ],
     "owner": "raghavagps",
+    "publication": [
+        {
+            "doi": "10.1093/bib/bbac538",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.This paper describes a method Pprint2, which is an improved version of Pprint developed for predicting RNA-interacting residues in a protein. Training and independent/validation datasets used in this study comprises of 545 and 161 non-redundant RNA-binding proteins, respectively. All models were trained on training dataset and evaluated on the validation dataset. The preliminary analysis reveals that positively charged amino acids such as H, R and K, are more prominent in the RNA-interacting residues. Initially, machine learning based models have been developed using binary profile and obtain maximum area under curve (AUC) 0.68 on validation dataset. The performance of this model improved significantly from AUC 0.68 to 0.76, when evolutionary profile is used instead of binary profile. The performance of our evolutionary profile-based model improved further from AUC 0.76 to 0.82, when convolutional neural network has been used for developing model. Our final model based on convolutional neural network using evolutionary information achieved AUC 0.82 with Matthews correlation coefficient of 0.49 on the validation dataset. Our best model outperforms existing methods when evaluated on the independent/validation dataset. A user-friendly standalone software and web-based server named 'Pprint2' has been developed for predicting RNA-interacting residues (https://webs.iiitd.edu.in/raghava/pprint2 and https://github.com/raghavagps/pprint2).",
+                "authors": [
+                    {
+                        "name": "Bajaj K."
+                    },
+                    {
+                        "name": "Dhall A."
+                    },
+                    {
+                        "name": "Patiyal S."
+                    },
+                    {
+                        "name": "Raghava G.P.S."
+                    },
+                    {
+                        "name": "Sahu H."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile"
+            },
+            "pmid": "36516298"
+        }
+    ],
     "toolType": [
+        "Command-line tool",
         "Web application"
     ],
     "topic": [
         {
-            "term": "Computational biology",
-            "uri": "http://edamontology.org/topic_3307"
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Proteins",
+            "uri": "http://edamontology.org/topic_0078"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
         }
     ]
 }
diff --git a/data/praline_database/praline_database.biotools.json b/data/praline_database/praline_database.biotools.json
new file mode 100644
index 0000000000000..37b13e0c3aea7
--- /dev/null
+++ b/data/praline_database/praline_database.biotools.json
@@ -0,0 +1,136 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T12:00:57.939597Z",
+    "biotoolsCURIE": "biotools:praline_database",
+    "biotoolsID": "praline_database",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "alexandros.armaos@iit.it",
+            "name": "Alexandros Armaos",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "gian.tartaglia@iit.it",
+            "name": "Gian Gaetano Tartaglia",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Andrea Vandelli"
+        },
+        {
+            "name": "Marc Torrent Burgas",
+            "orcidid": "http://orcid.org/0000-0001-6567-3474"
+        }
+    ],
+    "description": "PRALINE - Protein and Rna humAn singLe-nucleotIde-variaNts in condEnsates - a database to interrogate proteins and RNAs contained in human condensates such as stress-granules (SGs), processing bodies (PBs), droplets, amyloids and many more.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                },
+                {
+                    "term": "RNA secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0278"
+                },
+                {
+                    "term": "RNA structure prediction",
+                    "uri": "http://edamontology.org/operation_2441"
+                },
+                {
+                    "term": "RNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3901"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://alvinlee.bio.uniroma1.it/praline",
+    "lastUpdate": "2023-02-15T12:00:57.942353Z",
+    "name": "PRALINE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac847",
+            "metadata": {
+                "abstract": "© The Author(s) 2023. Published by Oxford University Press.SUMMARY: Biological condensates are membraneless organelles with different material properties. Proteins and RNAs are the main components, but most of their interactions are still unknown. Here, we introduce PRALINE, a database for the interrogation of proteins and RNAs contained in stress granules, processing bodies and other assemblies including droplets and amyloids. PRALINE provides information about the predicted and experimentally validated protein-protein, protein-RNA and RNA-RNA interactions. For proteins, it reports the liquid-liquid phase separation and liquid-solid phase separation propensities. For RNAs, it provides information on predicted secondary structure content. PRALINE shows detailed information on human single-nucleotide variants, their clinical significance and presence in protein and RNA binding sites, and how they can affect condensates' physical properties. AVAILABILITY AND IMPLEMENTATION: PRALINE is freely accessible on the web at http://praline.tartaglialab.com.",
+                "authors": [
+                    {
+                        "name": "Armaos A."
+                    },
+                    {
+                        "name": "Arnal Segura M."
+                    },
+                    {
+                        "name": "Broglia L."
+                    },
+                    {
+                        "name": "Colantoni A."
+                    },
+                    {
+                        "name": "Fiorentino J."
+                    },
+                    {
+                        "name": "Monti M."
+                    },
+                    {
+                        "name": "Sanchez de Groot N."
+                    },
+                    {
+                        "name": "Tartaglia G.G."
+                    },
+                    {
+                        "name": "Torrent Burgas M."
+                    },
+                    {
+                        "name": "Vandelli A."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "The PRALINE database: protein and Rna humAn singLe nucleotIde variaNts in condEnsates"
+            },
+            "pmcid": "PMC9825767",
+            "pmid": "36592044"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein variants",
+            "uri": "http://edamontology.org/topic_3120"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/prawns/prawns.biotools.json b/data/prawns/prawns.biotools.json
new file mode 100644
index 0000000000000..58476449c2b11
--- /dev/null
+++ b/data/prawns/prawns.biotools.json
@@ -0,0 +1,122 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T11:55:45.805522Z",
+    "biotoolsCURIE": "biotools:prawns",
+    "biotoolsID": "prawns",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mpop@umd.edu",
+            "name": "Mihai Pop",
+            "orcidid": "https://orcid.org/0000-0001-9617-5304",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Errol Strain"
+        },
+        {
+            "name": "Hugh Rand"
+        },
+        {
+            "name": "Kiran Javkar",
+            "orcidid": "https://orcid.org/0000-0003-1638-6625"
+        }
+    ],
+    "description": "Compact pan-genomic features for whole-genome population genomics.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "De-novo assembly",
+                    "uri": "http://edamontology.org/operation_0524"
+                },
+                {
+                    "term": "Genome comparison",
+                    "uri": "http://edamontology.org/operation_3209"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/KiranJavkar/PRAWNS.git).<h4",
+    "language": [
+        "C++",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-15T11:55:45.808146Z",
+    "license": "GPL-3.0",
+    "name": "PRAWNS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC844",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Scientists seeking to understand the genomic basis of bacterial phenotypes, such as antibiotic resistance, today have access to an unprecedented number of complete and nearly complete genomes. Making sense of these data requires computational tools able to perform multiple-genome comparisons efficiently, yet currently available tools cannot scale beyond several tens of genomes. RESULTS: We describe PRAWNS, an efficient and scalable tool for multiple-genome analysis. PRAWNS defines a concise set of genomic features (metablocks), as well as pairwise relationships between them, which can be used as a basis for large-scale genotype-phenotype association studies. We demonstrate the effectiveness of PRAWNS by identifying genomic regions associated with antibiotic resistance in Acinetobacter baumannii. AVAILABILITY AND IMPLEMENTATION: PRAWNS is implemented in C++ and Python3, licensed under the GPLv3 license, and freely downloadable from GitHub (https://github.com/KiranJavkar/PRAWNS.git). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Javkar K."
+                    },
+                    {
+                        "name": "Pop M."
+                    },
+                    {
+                        "name": "Rand H."
+                    },
+                    {
+                        "name": "Strain E."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "PRAWNS: compact pan-genomic features for whole-genome population genomics"
+            },
+            "pmcid": "PMC9825322",
+            "pmid": "36579850"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Population genomics",
+            "uri": "http://edamontology.org/topic_3796"
+        },
+        {
+            "term": "Structural variation",
+            "uri": "http://edamontology.org/topic_3175"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/predaot/predaot.biotools.json b/data/predaot/predaot.biotools.json
new file mode 100644
index 0000000000000..818c5de91419f
--- /dev/null
+++ b/data/predaot/predaot.biotools.json
@@ -0,0 +1,130 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T11:07:38.736061Z",
+    "biotoolsCURIE": "biotools:predaot",
+    "biotoolsID": "predaot",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jyryu@duksung.ac.kr",
+            "name": "Jae Yong Ryu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "ksoh@krict.re.kr",
+            "name": "Kwang-Seok Oh",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jidon Jang"
+        },
+        {
+            "name": "Woo Dae Jang"
+        }
+    ],
+    "description": "A computational framework for prediction of acute oral toxicity based on multiple random forest models.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "SMILES string",
+                        "uri": "http://edamontology.org/data_2301"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Indel detection",
+                    "uri": "http://edamontology.org/operation_0452"
+                },
+                {
+                    "term": "Recombination detection",
+                    "uri": "http://edamontology.org/operation_0451"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://predaot.netlify.app/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T11:07:38.740041Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/CSB-L/PredAOT"
+        }
+    ],
+    "name": "PredAOT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-023-05176-5",
+            "metadata": {
+                "abstract": "Background: Acute oral toxicity of drug candidates can lead to drug development failure; thus, predicting the acute oral toxicity of small compounds is important for successful drug development. However, evaluation of the acute oral toxicity of small compounds considered in the early stages of drug discovery is limited because of cost and time. Here, we developed a computational framework, PredAOT, that predicts the acute oral toxicity of small compounds in mice and rats. Methods: PredAOT is based on multiple random forest models for the accurate prediction of acute oral toxicity. A total of 6226 and 6238 compounds evaluated in mice and rats, respectively, were used to train the models. Results: PredAOT has the advantage of predicting acute oral toxicity in mice and rats simultaneously, and its prediction performance is similar to or better than that of existing tools. Conclusion: PredAOT will be a useful tool for the quick and accurate prediction of the acute oral toxicity of small compounds in mice and rats during drug development.",
+                "authors": [
+                    {
+                        "name": "Jang J."
+                    },
+                    {
+                        "name": "Jang W.D."
+                    },
+                    {
+                        "name": "Oh K.-S."
+                    },
+                    {
+                        "name": "Ryu J.Y."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "PredAOT: a computational framework for prediction of acute oral toxicity based on multiple random forest models"
+            },
+            "pmcid": "PMC9951537",
+            "pmid": "36829107"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Drug development",
+            "uri": "http://edamontology.org/topic_3373"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        }
+    ]
+}
diff --git a/data/predator/predator.biotools.json b/data/predator/predator.biotools.json
new file mode 100644
index 0000000000000..532bf2c740d29
--- /dev/null
+++ b/data/predator/predator.biotools.json
@@ -0,0 +1,84 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T11:50:58.691032Z",
+    "biotoolsCURIE": "biotools:predator",
+    "biotoolsID": "predator",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "berk_gurdamar@hotmail.com",
+            "name": "Berk Gurdamar",
+            "orcidid": "http://orcid.org/0000-0003-3811-2211",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Osman Ugur Sezerman"
+        }
+    ],
+    "description": "An R package for network-based mutation impact prediction.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Variant classification",
+                    "uri": "http://edamontology.org/operation_3225"
+                },
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                },
+                {
+                    "term": "Variant prioritisation",
+                    "uri": "http://edamontology.org/operation_3226"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://berkgurdamar.github.io/predatoR/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-15T11:50:58.693657Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/berkgurdamar/predatoR"
+        }
+    ],
+    "name": "predatoR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1101/2022.11.29.518310"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Protein folding, stability and design",
+            "uri": "http://edamontology.org/topic_0130"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        }
+    ]
+}
diff --git a/data/prediction/prediction.biotools.json b/data/prediction/prediction.biotools.json
new file mode 100644
index 0000000000000..27eedf2cfc029
--- /dev/null
+++ b/data/prediction/prediction.biotools.json
@@ -0,0 +1,97 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T02:51:17.563105Z",
+    "biotoolsCURIE": "biotools:prediction",
+    "biotoolsID": "prediction",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "beatriz.parra@correounivalle.edu.co",
+            "name": "Beatriz Parra",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "dilopezal@unal.edu.co",
+            "name": "Diana Lopez-Alvarez",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Carlos A. Pardo"
+        },
+        {
+            "name": "David E. Valencia-Valencia"
+        }
+    ],
+    "description": "A predictive model to establish the performance of Oxford sequencing reads of SARS-CoV-2.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Read binning",
+                    "uri": "http://edamontology.org/operation_3798"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://genomicdashboard.herokuapp.com",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-15T02:51:17.565829Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/TAOLabUV/PredictION"
+        }
+    ],
+    "name": "PredictION",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.7717/PEERJ.14425",
+            "pmcid": "PMC9744141",
+            "pmid": "36518292"
+        }
+    ],
+    "toolType": [
+        "Script",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/presto-measure/presto-measure.biotools.json b/data/presto-measure/presto-measure.biotools.json
new file mode 100644
index 0000000000000..a6e279681b0db
--- /dev/null
+++ b/data/presto-measure/presto-measure.biotools.json
@@ -0,0 +1,122 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-31T13:34:57.370030Z",
+    "biotoolsCURIE": "biotools:presto-measure",
+    "biotoolsID": "presto-measure",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Mats O. Tholén",
+            "orcidid": "https://orcid.org/0000-0003-4233-3279"
+        },
+        {
+            "name": "Riccardo Borgani",
+            "orcidid": "https://orcid.org/0000-0001-8199-5510"
+        },
+        {
+            "name": "Simone Gasparinetti",
+            "orcidid": "https://orcid.org/0000-0002-7238-693X"
+        },
+        {
+            "name": "David B. Haviland",
+            "orcidid": "https://orcid.org/0000-0001-8534-6577",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Measurement and control of a superconducting quantum processor with a fully integrated radio-frequency system on a chip.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/intermod-pro/presto-measure",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-31T13:34:57.372452Z",
+    "license": "MIT",
+    "name": "Presto",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1063/5.0101398",
+            "metadata": {
+                "abstract": "© 2022 Author(s).We describe a digital microwave platform called Presto, designed for measurement and control of multiple quantum bits (qubits) and based on the third-generation radio-frequency system on a chip. Presto uses direct digital synthesis to create signals up to 9 GHz on 16 synchronous output ports, while synchronously analyzing responses on 16 input ports. Presto has 16 DC-bias outputs, four inputs and four outputs for digital triggers or markers, and two continuous-wave outputs for synthesizing frequencies up to 15 GHz. Scaling to a large number of qubits is enabled through deterministic synchronization of multiple Presto units. A Python application programming interface configures a firmware for synthesis and analysis of pulses, coordinated by an event sequencer. The analysis integrates template matching (matched filtering) and low-latency (184-254 ns) feedback to enable a wide range of multi-qubit experiments. We demonstrate Presto's capabilities with experiments on a sample consisting of two superconducting qubits connected via a flux-tunable coupler. We show single-shot readout and active reset of a single qubit; randomized benchmarking of single-qubit gates showing 99.972% fidelity, limited by the coherence time of the qubit; and calibration of a two-qubit iSWAP gate.",
+                "authors": [
+                    {
+                        "name": "Bengtsson A."
+                    },
+                    {
+                        "name": "Borgani R."
+                    },
+                    {
+                        "name": "Bylander J."
+                    },
+                    {
+                        "name": "Delsing P."
+                    },
+                    {
+                        "name": "Di Carlo G.R."
+                    },
+                    {
+                        "name": "Gasparinetti S."
+                    },
+                    {
+                        "name": "Haviland D.B."
+                    },
+                    {
+                        "name": "Krizan C."
+                    },
+                    {
+                        "name": "Kudra M."
+                    },
+                    {
+                        "name": "Tancredi G."
+                    },
+                    {
+                        "name": "Tholen M.O."
+                    }
+                ],
+                "date": "2022-10-01T00:00:00Z",
+                "journal": "Review of Scientific Instruments",
+                "title": "Measurement and control of a superconducting quantum processor with a fully integrated radio-frequency system on a chip"
+            },
+            "pmid": "36319392"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-on-chip",
+            "uri": "http://edamontology.org/topic_3179"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        }
+    ]
+}
diff --git a/data/pro-map/pro-map.biotools.json b/data/pro-map/pro-map.biotools.json
new file mode 100644
index 0000000000000..a92998a967c3a
--- /dev/null
+++ b/data/pro-map/pro-map.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T02:46:54.020170Z",
+    "biotoolsCURIE": "biotools:pro-map",
+    "biotoolsID": "pro-map",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jonathan.blackburn@uct.ac.za",
+            "name": "Jonathan Michael Blackburn",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "m_mowoe@yahoo.co.uk",
+            "name": "Metoboroghene Oluwaseyi Mowoe",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Eduard Jonas"
+        },
+        {
+            "name": "Shaun Garnett"
+        }
+    ],
+    "description": "A robust pipeline for the pre-processing of single channel protein microarray data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Expression analysis",
+                    "uri": "http://edamontology.org/operation_2495"
+                },
+                {
+                    "term": "Protein sequence analysis",
+                    "uri": "http://edamontology.org/operation_2479"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://metaomics.uct.ac.za/shinyapps/Pro-MAP/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-15T02:46:54.022878Z",
+    "name": "Pro-MAP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05095-X",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: The central role of proteins in diseases has made them increasingly attractive as therapeutic targets and indicators of cellular processes. Protein microarrays are emerging as an important means of characterising protein activity. Their accurate downstream analysis to produce biologically significant conclusions is largely dependent on proper pre-processing of extracted signal intensities. However, existing computational tools are not specifically tailored to the nature of these data and lack unanimity. Results: Here, we present the single-channel Protein Microarray Analysis Pipeline, a tailored computational tool for analysis of single-channel protein microarrays enabling biomarker identification, implemented in R, and as an interactive web application. We compared four existing background correction and normalization methods as well as three array filtering techniques, applied to four real datasets with two microarray designs, extracted using two software programs. The normexp, cyclic loess, and array weighting methods were most effective for background correction, normalization, and filtering respectively. Conclusions: Thus, here we provided a versatile and effective pre-processing and differential analysis workflow for single-channel protein microarray data in form of an R script and web application (https://metaomics.uct.ac.za/shinyapps/Pro-MAP/.) for those not well versed in the R programming language.",
+                "authors": [
+                    {
+                        "name": "Blackburn J.M."
+                    },
+                    {
+                        "name": "Garnett S."
+                    },
+                    {
+                        "name": "Jonas E."
+                    },
+                    {
+                        "name": "Lennard K."
+                    },
+                    {
+                        "name": "Mowoe M.O."
+                    },
+                    {
+                        "name": "Talbot J."
+                    },
+                    {
+                        "name": "Townsend P."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "Pro-MAP: a robust pipeline for the pre-processing of single channel protein microarray data"
+            },
+            "pmcid": "PMC9733281",
+            "pmid": "36494629"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/profab/profab.biotools.json b/data/profab/profab.biotools.json
new file mode 100644
index 0000000000000..4bc6ff40ee7b6
--- /dev/null
+++ b/data/profab/profab.biotools.json
@@ -0,0 +1,100 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T11:03:28.076737Z",
+    "biotoolsCURIE": "biotools:profab",
+    "biotoolsID": "profab",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ahmet.rifaioglu@iste.edu.tr",
+            "name": "Ahmet S Rifaioğlu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ahmet Atakan"
+        },
+        {
+            "name": "A Samet Özdilek",
+            "orcidid": "https://orcid.org/0000-0002-2096-9295"
+        },
+        {
+            "name": "Tunca Doğan",
+            "orcidid": "https://orcid.org/0000-0002-1298-9763"
+        }
+    ],
+    "description": "ProFAB-open protein functional annotation benchmark.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein feature detection",
+                    "uri": "http://edamontology.org/operation_3092"
+                },
+                {
+                    "term": "Protein function prediction",
+                    "uri": "http://edamontology.org/operation_1777"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://profab.kansil.org",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T11:03:28.080479Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/kansil/ProFAB"
+        }
+    ],
+    "name": "ProFAB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC627",
+            "pmid": "36736370"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Enzymes",
+            "uri": "http://edamontology.org/topic_0821"
+        },
+        {
+            "term": "Function analysis",
+            "uri": "http://edamontology.org/topic_1775"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        }
+    ]
+}
diff --git a/data/prolonged_los/prolonged_los.biotools.json b/data/prolonged_los/prolonged_los.biotools.json
new file mode 100644
index 0000000000000..a3edcbc845cf6
--- /dev/null
+++ b/data/prolonged_los/prolonged_los.biotools.json
@@ -0,0 +1,141 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T10:57:56.549340Z",
+    "biotoolsCURIE": "biotools:prolonged_los",
+    "biotoolsID": "prolonged_los",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xiaoxinwu@zju.edu.cn",
+            "name": "Xiaoxin Wu",
+            "orcidid": "https://orcid.org/0000-0001-9785-8916",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "cqzhaowl@163.com",
+            "name": "Wenlong Zhao",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "joyjchan@gmail.com",
+            "name": "Jingjing Chen",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "tangsj@zju.edu.cn",
+            "name": "Songjia Tang",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Incident curve plotting",
+                    "uri": "http://edamontology.org/operation_3503"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cytjt007.shinyapps.io/prolonged_los/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-18T10:57:56.553794Z",
+    "name": "prolonged LOS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12967-023-03959-1",
+            "metadata": {
+                "abstract": "Background: Length of stay (LOS) is an important metric for evaluating the management of inpatients. This study aimed to explore the factors impacting the LOS of inpatients with type-2 diabetes mellitus (T2DM) and develop a predictive model for the early identification of inpatients with prolonged LOS. Methods: A 13-year multicenter retrospective study was conducted on 83,776 patients with T2DM to develop and validate a clinical predictive tool for prolonged LOS. Least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis were adopted to build the risk model for prolonged LOS, and a nomogram was taken to visualize the model. Furthermore, receiver operating characteristic curves, calibration curves, and decision curve analysis and clinical impact curves were used to respectively validate the discrimination, calibration, and clinical applicability of the model. Results: The result showed that age, cerebral infarction, antihypertensive drug use, antiplatelet and anticoagulant use, past surgical history, past medical history, smoking, drinking, and neutrophil percentage-to-albumin ratio were closely related to the prolonged LOS. Area under the curve values of the nomogram in the training, internal validation, external validation set 1, and external validation set 2 were 0.803 (95% CI [confidence interval] 0.799–0.808), 0.794 (95% CI 0.788–0.800), 0.754 (95% CI 0.739–0.770), and 0.743 (95% CI 0.722–0.763), respectively. The calibration curves indicated that the nomogram had a strong calibration. Besides, decision curve analysis, and clinical impact curves exhibited that the nomogram had favorable clinical practical value. Besides, an online interface (https://cytjt007.shinyapps.io/prolonged_los/) was developed to provide convenient access for users. Conclusion: In sum, the proposed model could predict the possible prolonged LOS of inpatients with T2DM and help the clinicians to improve efficiency in bed management.",
+                "authors": [
+                    {
+                        "name": "Chen J."
+                    },
+                    {
+                        "name": "Gong J."
+                    },
+                    {
+                        "name": "He Y."
+                    },
+                    {
+                        "name": "Li J."
+                    },
+                    {
+                        "name": "Lin X."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Tan J."
+                    },
+                    {
+                        "name": "Tang S."
+                    },
+                    {
+                        "name": "Wu X."
+                    },
+                    {
+                        "name": "Wu X."
+                    },
+                    {
+                        "name": "Yu Y."
+                    },
+                    {
+                        "name": "Zhang S."
+                    },
+                    {
+                        "name": "Zhang Z."
+                    },
+                    {
+                        "name": "Zhao W."
+                    },
+                    {
+                        "name": "Zhao Y."
+                    },
+                    {
+                        "name": "Zheng J."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Journal of Translational Medicine",
+                "title": "A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010–2022) multicenter retrospective case–control study"
+            },
+            "pmcid": "PMC9903472",
+            "pmid": "36750951"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        },
+        {
+            "term": "Quality affairs",
+            "uri": "http://edamontology.org/topic_3393"
+        }
+    ]
+}
diff --git a/data/prophaser/prophaser.biotools.json b/data/prophaser/prophaser.biotools.json
new file mode 100644
index 0000000000000..e8a54aa98b1f5
--- /dev/null
+++ b/data/prophaser/prophaser.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-31T00:02:16.665847Z",
+    "biotoolsCURIE": "biotools:prophaser",
+    "biotoolsID": "prophaser",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "carl.nettelblad@it.uu.se",
+            "name": "Carl Nettelblad",
+            "orcidid": "https://orcid.org/0000-0003-0458-6902",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "kristiina.ausmees@it.uu.se",
+            "name": "Kristiina Ausmees",
+            "orcidid": "https://orcid.org/0000-0002-6212-539X",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Achieving improved accuracy for imputation of ancient DNA.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Haplotype mapping",
+                    "uri": "http://edamontology.org/operation_0487"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "SNP detection",
+                    "uri": "http://edamontology.org/operation_0484"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/scicompuu/prophaser",
+    "language": [
+        "C++"
+    ],
+    "lastUpdate": "2023-01-31T00:02:16.668703Z",
+    "license": "BSD-3-Clause",
+    "name": "prophaser",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC738",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Genotype imputation has the potential to increase the amount of information that can be gained from the often limited biological material available in ancient samples. As many widely used tools have been developed with modern data in mind, their design is not necessarily reflective of the requirements in studies of ancient DNA. Here, we investigate if an imputation method based on the full probabilistic Li and Stephens model of haplotype frequencies might be beneficial for the particular challenges posed by ancient data. RESULTS: We present an implementation called prophaser and compare imputation performance to two alternative pipelines that have been used in the ancient DNA community based on the Beagle software. Considering empirical ancient data downsampled to lower coverages as well as present-day samples with artificially thinned genotypes, we show that the proposed method is advantageous at lower coverages, where it yields improved accuracy and ability to capture rare variation. The software prophaser is optimized for running in a massively parallel manner and achieved reasonable runtimes on the experiments performed when executed on a GPU. AVAILABILITY AND IMPLEMENTATION: The C++ code for prophaser is available in the GitHub repository https://github.com/scicompuu/prophaser. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Ausmees K."
+                    },
+                    {
+                        "name": "Nettelblad C."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Achieving improved accuracy for imputation of ancient DNA"
+            },
+            "pmcid": "PMC9805568",
+            "pmid": "36377787"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        }
+    ]
+}
diff --git a/data/proseqaprodb/proseqaprodb.biotools.json b/data/proseqaprodb/proseqaprodb.biotools.json
new file mode 100644
index 0000000000000..ffa3bfacf0801
--- /dev/null
+++ b/data/proseqaprodb/proseqaprodb.biotools.json
@@ -0,0 +1,87 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T10:55:28.899982Z",
+    "biotoolsCURIE": "biotools:proseqaprodb",
+    "biotoolsID": "proseqaprodb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Aakriti Jain"
+        },
+        {
+            "name": "Manish Kumar"
+        },
+        {
+            "name": "Manisha Goel"
+        },
+        {
+            "name": "Nikita Ray"
+        },
+        {
+            "name": "Rahul Kumar Vishwakarma"
+        }
+    ],
+    "description": "ProSequence Assisted Protein - DataBase, ProSeqAProDB is a comprehensive, non-redundant and manually curated dataset of proteins which are translated in a pre-pro-form.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "Protein folding analysis",
+                    "uri": "http://edamontology.org/operation_2415"
+                },
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://proseqaprodb.mkulab.in",
+    "lastUpdate": "2023-03-18T10:55:28.903778Z",
+    "name": "ProSeqAProDb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.JMB.2023.168022",
+            "pmid": "36828269"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Gene and protein families",
+            "uri": "http://edamontology.org/topic_0623"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        },
+        {
+            "term": "Protein folding, stability and design",
+            "uri": "http://edamontology.org/topic_0130"
+        },
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        }
+    ]
+}
diff --git a/data/prot-on/prot-on.biotools.json b/data/prot-on/prot-on.biotools.json
new file mode 100644
index 0000000000000..49d1aef0723f6
--- /dev/null
+++ b/data/prot-on/prot-on.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T20:01:56.562029Z",
+    "biotoolsCURIE": "biotools:prot-on",
+    "biotoolsID": "prot-on",
+    "cost": "Free of charge",
+    "description": "The identification of critical amino acids for redesigning the structurally characterized protein-protein interfaces paves the way for developing protein-based therapeutics to dealing with diverse range of diseases. To find such amino acids positions, the residues across the protein interaction surfaces are either randomly or strategically mutated. Scanning mutations in this manner is experimentally costly. Therefore, computational methods have been developed to estimate the impact of an interfacial mutation on protein-protein interactions. In this work, we present the PROT-ON tool that scans all possible interfacial mutations by using EvoEF1 (active in both on the web server and stand-alone versions) or FoldX (active only in the stand-alone version) with the aim of finding the most mutable positions.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein complex",
+                        "uri": "http://edamontology.org/data_2877"
+                    },
+                    "format": [
+                        {
+                            "term": "PDB",
+                            "uri": "http://edamontology.org/format_1476"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Protein-protein interaction analysis",
+                    "uri": "http://edamontology.org/operation_2949"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Protein interaction data",
+                        "uri": "http://edamontology.org/data_0906"
+                    },
+                    "format": [
+                        {
+                            "term": "Format (by type of data)",
+                            "uri": "http://edamontology.org/format_2350"
+                        }
+                    ]
+                }
+            ]
+        }
+    ],
+    "homepage": "http://proton.tools.ibg.edu.tr:8001",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-27T05:27:32.953630Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/CSB-KaracaLab/prot-on"
+        },
+        {
+            "type": [
+                "Service"
+            ],
+            "url": "http://proton.tools.ibg.edu.tr:8001"
+        }
+    ],
+    "name": "PROT-ON",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "mehdikosaca",
+    "toolType": [
+        "Bioinformatics portal",
+        "Command-line tool",
+        "Web application",
+        "Web service"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Biophysics",
+            "uri": "http://edamontology.org/topic_3306"
+        },
+        {
+            "term": "Drug development",
+            "uri": "http://edamontology.org/topic_3373"
+        },
+        {
+            "term": "Protein binding sites",
+            "uri": "http://edamontology.org/topic_3534"
+        },
+        {
+            "term": "Protein folding, stability and design",
+            "uri": "http://edamontology.org/topic_0130"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        }
+    ]
+}
diff --git a/data/proteomexchange/proteomexchange.biotools.json b/data/proteomexchange/proteomexchange.biotools.json
index 417bc7cb44eed..2fd7435d513cc 100644
--- a/data/proteomexchange/proteomexchange.biotools.json
+++ b/data/proteomexchange/proteomexchange.biotools.json
@@ -57,7 +57,7 @@
         }
     ],
     "homepage": "http://www.proteomexchange.org/",
-    "lastUpdate": "2021-04-18T12:49:51Z",
+    "lastUpdate": "2023-01-30T23:58:25.161224Z",
     "name": "ProteomeXchange",
     "operatingSystem": [
         "Linux",
@@ -152,7 +152,7 @@
                         "name": "Xenarios I."
                     }
                 ],
-                "citationCount": 1834,
+                "citationCount": 2056,
                 "date": "2014-01-01T00:00:00Z",
                 "journal": "Nature Biotechnology",
                 "title": "ProteomeXchange provides globally coordinated proteomics data submission and dissemination"
@@ -169,11 +169,90 @@
                         "name": "Hermjakob H."
                     }
                 ],
-                "citationCount": 54,
+                "citationCount": 68,
                 "date": "2006-07-07T00:00:00Z",
                 "journal": "Expert Review of Proteomics",
                 "title": "The proteomics identifications database (PRIDE) and the proteomExchange consortium: Making proteomics data accessible"
             }
+        },
+        {
+            "doi": "10.1093/nar/gkac1040",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.",
+                "authors": [
+                    {
+                        "name": "Bandeira N."
+                    },
+                    {
+                        "name": "Bandla C."
+                    },
+                    {
+                        "name": "Carver J.J."
+                    },
+                    {
+                        "name": "Deutsch E.W."
+                    },
+                    {
+                        "name": "Hewapathirana S."
+                    },
+                    {
+                        "name": "Ishihama Y."
+                    },
+                    {
+                        "name": "Kamatchinathan S."
+                    },
+                    {
+                        "name": "Kawano S."
+                    },
+                    {
+                        "name": "Kundu D.J."
+                    },
+                    {
+                        "name": "MacCoss M.J."
+                    },
+                    {
+                        "name": "MacLean B."
+                    },
+                    {
+                        "name": "Mendoza L."
+                    },
+                    {
+                        "name": "Okuda S."
+                    },
+                    {
+                        "name": "Perez-Riverol Y."
+                    },
+                    {
+                        "name": "Pullman B.S."
+                    },
+                    {
+                        "name": "Sharma V."
+                    },
+                    {
+                        "name": "Sun Z."
+                    },
+                    {
+                        "name": "Vizcaino J.A."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Watanabe Y."
+                    },
+                    {
+                        "name": "Wertz J."
+                    },
+                    {
+                        "name": "Zhu Y."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "The ProteomeXchange consortium at 10 years: 2023 update"
+            },
+            "pmcid": "PMC9825490",
+            "pmid": "36370099"
         }
     ],
     "toolType": [
diff --git a/data/proxybind/proxybind.biotools.json b/data/proxybind/proxybind.biotools.json
new file mode 100644
index 0000000000000..a8504a44d1225
--- /dev/null
+++ b/data/proxybind/proxybind.biotools.json
@@ -0,0 +1,101 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T23:44:17.878304Z",
+    "biotoolsCURIE": "biotools:proxybind",
+    "biotoolsID": "proxybind",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "matthieu.schapira@utoronto.ca",
+            "name": "Matthieu Schapira",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Evianne Rovers"
+        },
+        {
+            "name": "Lihua Liu"
+        }
+    ],
+    "description": "A compendium of binding sites for proximity-induced pharmacology.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "PTM localisation",
+                    "uri": "http://edamontology.org/operation_3755"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                },
+                {
+                    "term": "Protein-protein binding site prediction",
+                    "uri": "http://edamontology.org/operation_2464"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://polymorph.sgc.utoronto.ca/proxybind/index.php",
+    "lastUpdate": "2023-01-30T23:44:17.880723Z",
+    "name": "ProxyBind",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.11.010",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsProximity-induced pharmacology (ProxPharm) is a novel paradigm in drug discovery where a small molecule brings two proteins in close proximity to elicit a signal, generally from one protein onto another. The potential of ProxPharm compounds as a new therapeutic modality is firmly established by proteolysis targeting chimeras (PROTACs) that bring an E3 ubiquitin ligase in proximity to a target protein to induce ubiquitination and subsequent degradation of the target. The concept can be expanded to induce other post-translational modifications via the recruitment of different types of protein-modifying enzymes. To survey the human proteome for opportunities in proximity pharmacology, we systematically mapped non-catalytic drug binding pockets on the structure of protein-modifying enzymes available from the Protein Databank. In addition to binding sites exploited by previously reported ProxPharm compounds, we identified putative ligandable non-catalytic pockets in 236 kinases, 45 phosphatases, 37 deubiquitinases, 14 methyltransferases, 11 acetyltransferases, 13 glycosyltransferases, 4 deacetylases, 7 demethylases and 2 glycosidases, including cavities occupied by chemical matter that may serve as starting points for future ProxPharm compounds. This systematic survey confirms that proximity pharmacology is a versatile modality with largely unexplored and promising potential and reveals novel opportunities to pharmacologically rewire molecular circuitries. All data is available from the ProxyBind database at https://polymorph.sgc.utoronto.ca/proxybind/index.php.",
+                "authors": [
+                    {
+                        "name": "Liu L."
+                    },
+                    {
+                        "name": "Rovers E."
+                    },
+                    {
+                        "name": "Schapira M."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "ProxyBind: A compendium of binding sites for proximity-induced pharmacology"
+            },
+            "pmcid": "PMC9674861",
+            "pmid": "36420167"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Pharmacology",
+            "uri": "http://edamontology.org/topic_0202"
+        },
+        {
+            "term": "Protein modifications",
+            "uri": "http://edamontology.org/topic_0601"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/prr-hypred/prr-hypred.biotools.json b/data/prr-hypred/prr-hypred.biotools.json
new file mode 100644
index 0000000000000..c5476170b2eb6
--- /dev/null
+++ b/data/prr-hypred/prr-hypred.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T10:50:51.333828Z",
+    "biotoolsCURIE": "biotools:prr-hypred",
+    "biotoolsID": "prr-hypred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Adeel Malik"
+        },
+        {
+            "name": "Ahmad Firoz"
+        },
+        {
+            "name": "Balachandran Manavalan"
+        },
+        {
+            "name": "Chang-Bae Kim"
+        }
+    ],
+    "description": "A two-layer hybrid framework to predict pattern recognition receptors and their families by employing sequence encoded optimal features.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence",
+                        "uri": "http://edamontology.org/data_2044"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Feature selection",
+                    "uri": "http://edamontology.org/operation_3936"
+                },
+                {
+                    "term": "Sequence feature detection",
+                    "uri": "http://edamontology.org/operation_0253"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://procarb.org/PRRHyPred/",
+    "lastUpdate": "2023-03-18T10:50:51.338182Z",
+    "name": "PRR-HyPred",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.IJBIOMAC.2023.123622",
+            "metadata": {
+                "abstract": "Pattern recognition receptors (PRRs) recognize distinct features on the surface of pathogens or damaged cells and play key roles in the innate immune system. PRRs are divided into various families, including Toll-like receptors, retinoic acid-inducible gene-I-like receptors, nucleotide oligomerization domain-like receptors, and C-type lectin receptors. As these are implicated in host health and several diseases, their accurate identification is indispensable for their functional characterization and targeted therapeutic approaches. Here, we construct PRR-HyPred, a novel two-layer hybrid framework in which the first layer predicts whether a given sequence is PRR or non-PRR using a support vector machine, and in the second, the predicted PRR sequence is assigned to a specific family using a random forest-based classifier. Based on a 10-fold cross-validation test, PRR-HyPred achieved 83.4 % accuracy in the first layer and 95 % in the second, with Matthew's correlation coefficient values of 0.639 and 0.816, respectively. This is the first study that can simultaneously predict and classify PRRs into specific families. PRR-HyPred is available as a web portal at https://procarb.org/PRRHyPred/. We hope that it could be a valuable tool for the large-scale prediction and classification of PRRs and subsequently facilitate future studies.",
+                "authors": [
+                    {
+                        "name": "Akhter Y."
+                    },
+                    {
+                        "name": "Ali H.M."
+                    },
+                    {
+                        "name": "Firoz A."
+                    },
+                    {
+                        "name": "Kim C.-B."
+                    },
+                    {
+                        "name": "Malik A."
+                    },
+                    {
+                        "name": "Manavalan B."
+                    }
+                ],
+                "date": "2023-04-15T00:00:00Z",
+                "journal": "International Journal of Biological Macromolecules",
+                "title": "PRR-HyPred: A two-layer hybrid framework to predict pattern recognition receptors and their families by employing sequence encoded optimal features"
+            },
+            "pmid": "36773859"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Data mining",
+            "uri": "http://edamontology.org/topic_3473"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/pscl-2lsaesm/pscl-2lsaesm.biotools.json b/data/pscl-2lsaesm/pscl-2lsaesm.biotools.json
new file mode 100644
index 0000000000000..6f1950c29219a
--- /dev/null
+++ b/data/pscl-2lsaesm/pscl-2lsaesm.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T23:36:01.227169Z",
+    "biotoolsCURIE": "biotools:pscl-2lsaesm",
+    "biotoolsID": "pscl-2lsaesm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jiangning.song@monash.edu",
+            "name": "Jiangning Song",
+            "orcidid": "https://orcid.org/0000-0001-8031-9086",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "njyudj@njust.edu.cn",
+            "name": "Dong-Jun Yu",
+            "orcidid": "https://orcid.org/0000-0002-6786-8053",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Fazal Hadi"
+        },
+        {
+            "name": "Matee Ullah"
+        }
+    ],
+    "description": "Bioimage-based prediction of protein subcellular localization by integrating heterogeneous features with the two-level SAE-SM and mean ensemble method.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Protein signal peptide detection",
+                    "uri": "http://edamontology.org/operation_0418"
+                },
+                {
+                    "term": "Subcellular localisation prediction",
+                    "uri": "http://edamontology.org/operation_2489"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/csbio-njust-edu/PScL-2LSAESM",
+    "language": [
+        "C",
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-01-30T23:36:01.229802Z",
+    "license": "Not licensed",
+    "name": "PScL-2LSAESM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC727",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Over the past decades, a variety of in silico methods have been developed to predict protein subcellular localization within cells. However, a common and major challenge in the design and development of such methods is how to effectively utilize the heterogeneous feature sets extracted from bioimages. In this regards, limited efforts have been undertaken. RESULTS: We propose a new two-level stacked autoencoder network (termed 2L-SAE-SM) to improve its performance by integrating the heterogeneous feature sets. In particular, in the first level of 2L-SAE-SM, each optimal heterogeneous feature set is fed to train our designed stacked autoencoder network (SAE-SM). All the trained SAE-SMs in the first level can output the decision sets based on their respective optimal heterogeneous feature sets, known as 'intermediate decision' sets. Such intermediate decision sets are then ensembled using the mean ensemble method to generate the 'intermediate feature' set for the second-level SAE-SM. Using the proposed framework, we further develop a novel predictor, referred to as PScL-2LSAESM, to characterize image-based protein subcellular localization. Extensive benchmarking experiments on the latest benchmark training and independent test datasets collected from the human protein atlas databank demonstrate the effectiveness of the proposed 2L-SAE-SM framework for the integration of heterogeneous feature sets. Moreover, performance comparison of the proposed PScL-2LSAESM with current state-of-the-art methods further illustrates that PScL-2LSAESM clearly outperforms the existing state-of-the-art methods for the task of protein subcellular localization. AVAILABILITY AND IMPLEMENTATION: https://github.com/csbio-njust-edu/PScL-2LSAESM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Hadi F."
+                    },
+                    {
+                        "name": "Song J."
+                    },
+                    {
+                        "name": "Ullah M."
+                    },
+                    {
+                        "name": "Yu D.-J."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "PScL-2LSAESM: bioimage-based prediction of protein subcellular localization by integrating heterogeneous features with the two-level SAE-SM and mean ensemble method"
+            },
+            "pmid": "36413068"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Bioimaging",
+            "uri": "http://edamontology.org/topic_3383"
+        },
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Protein targeting and localisation",
+            "uri": "http://edamontology.org/topic_0140"
+        }
+    ]
+}
diff --git a/data/pseu-st/pseu-st.biotools.json b/data/pseu-st/pseu-st.biotools.json
new file mode 100644
index 0000000000000..b556a6e4d7732
--- /dev/null
+++ b/data/pseu-st/pseu-st.biotools.json
@@ -0,0 +1,103 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T10:43:12.559914Z",
+    "biotoolsCURIE": "biotools:pseu-st",
+    "biotoolsID": "pseu-st",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhuyuh@jlu.edu.cn",
+            "name": "Yuhui Zhu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Lina Xie"
+        },
+        {
+            "name": "Shutao Wang"
+        },
+        {
+            "name": "Xinru Zhang"
+        }
+    ],
+    "description": "A new stacked ensemble-learning method for identifying RNA pseudouridine sites.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Nucleic acid feature detection",
+                    "uri": "http://edamontology.org/operation_0415"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/jluzhangxinrubio/PseU-ST",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T10:43:12.564951Z",
+    "license": "Not licensed",
+    "name": "PseU-ST",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FGENE.2023.1121694",
+            "metadata": {
+                "abstract": "Background: Pseudouridine (Ψ) is one of the most abundant RNA modifications found in a variety of RNA types, and it plays a significant role in many biological processes. The key to studying the various biochemical functions and mechanisms of Ψ is to identify the Ψ sites. However, identifying Ψ sites using experimental methods is time-consuming and expensive. Therefore, it is necessary to develop computational methods that can accurately predict Ψ sites based on RNA sequence information. Methods: In this study, we proposed a new model called PseU-ST to identify Ψ sites in Homo sapiens (H. sapiens), Saccharomyces cerevisiae (S. cerevisiae), and Mus musculus (M. musculus). We selected the best six encoding schemes and four machine learning algorithms based on a comprehensive test of almost all of the RNA sequence encoding schemes available in the iLearnPlus software package, and selected the optimal features for each encoding scheme using chi-square and incremental feature selection algorithms. Then, we selected the optimal feature combination and the best base-classifier combination for each species through an extensive performance comparison and employed a stacking strategy to build the predictive model. Results: The results demonstrated that PseU-ST achieved better prediction performance compared with other existing models. The PseU-ST accuracy scores were 93.64%, 87.74%, and 89.64% on H_990, S_628, and M_944, respectively, representing increments of 13.94%, 6.05%, and 0.26%, respectively, higher than the best existing methods on the same benchmark training datasets. Conclusion: The data indicate that PseU-ST is a very competitive prediction model for identifying RNA Ψ sites in H. sapiens, M. musculus, and S. cerevisiae. In addition, we found that the Position-specific trinucleotide propensity based on single strand (PSTNPss) and Position-specific of three nucleotides (PS3) features play an important role in Ψ site identification. The source code for PseU-ST and the data are obtainable in our GitHub repository (https://github.com/jluzhangxinrubio/PseU-ST).",
+                "authors": [
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Xie L."
+                    },
+                    {
+                        "name": "Zhang X."
+                    },
+                    {
+                        "name": "Zhu Y."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Frontiers in Genetics",
+                "title": "PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites"
+            },
+            "pmcid": "PMC9892456",
+            "pmid": "36741328"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/psg-bar/psg-bar.biotools.json b/data/psg-bar/psg-bar.biotools.json
new file mode 100644
index 0000000000000..f3fcef6b169e6
--- /dev/null
+++ b/data/psg-bar/psg-bar.biotools.json
@@ -0,0 +1,125 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-23T09:49:37.778987Z",
+    "biotoolsCURIE": "biotools:psg-bar",
+    "biotoolsID": "psg-bar",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "acherkasov@prostatecentre.com",
+            "name": "Artem Cherkasov",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Mariia Radaeva"
+        },
+        {
+            "name": "Martin Ester"
+        },
+        {
+            "name": "Mohit Pandey",
+            "orcidid": "http://orcid.org/0000-0002-2562-7155"
+        }
+    ],
+    "description": "Ligand Binding Prediction using Protein Structure Graphs and Residual Graph Attention Networks.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein structure prediction",
+                    "uri": "http://edamontology.org/operation_0474"
+                },
+                {
+                    "term": "Protein-protein docking",
+                    "uri": "http://edamontology.org/operation_3899"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/diamondspark/PSG-BAR",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-23T09:49:37.781552Z",
+    "license": "Not licensed",
+    "name": "PSG-BAR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/molecules27165114",
+            "metadata": {
+                "abstract": "© 2022 by the authors.Computational prediction of ligand–target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein–ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)—the hallmark target of SARS-CoV-2 coronavirus.",
+                "authors": [
+                    {
+                        "name": "Cherkasov A."
+                    },
+                    {
+                        "name": "Ester M."
+                    },
+                    {
+                        "name": "Fernandez M."
+                    },
+                    {
+                        "name": "Garland O."
+                    },
+                    {
+                        "name": "Mslati H."
+                    },
+                    {
+                        "name": "Pandey M."
+                    },
+                    {
+                        "name": "Radaeva M."
+                    }
+                ],
+                "date": "2022-08-01T00:00:00Z",
+                "journal": "Molecules",
+                "title": "Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks"
+            },
+            "pmcid": "PMC9416537",
+            "pmid": "36014351"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein structural motifs and surfaces",
+            "uri": "http://edamontology.org/topic_0166"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/psm_utils/psm_utils.biotools.json b/data/psm_utils/psm_utils.biotools.json
new file mode 100644
index 0000000000000..9e95f869ab161
--- /dev/null
+++ b/data/psm_utils/psm_utils.biotools.json
@@ -0,0 +1,127 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T02:41:03.488373Z",
+    "biotoolsCURIE": "biotools:psm_utils",
+    "biotoolsID": "psm_utils",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Arthur Declercq"
+        },
+        {
+            "name": "Lennart Martens",
+            "orcidid": "https://orcid.org/0000-0003-4277-658X"
+        },
+        {
+            "name": "Ralf Gabriels",
+            "orcidid": "https://orcid.org/0000-0002-1679-1711"
+        },
+        {
+            "name": "Robbin Bouwmeester",
+            "orcidid": "https://orcid.org/0000-0001-6807-7029"
+        },
+        {
+            "name": "Sven Degroeve",
+            "orcidid": "https://orcid.org/0000-0001-8349-3370"
+        }
+    ],
+    "description": "A High-Level Python API for Parsing and Handling Peptide-Spectrum Matches and Proteomics Search Results.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://psm-utils.readthedocs.io/en/v0.2.1/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/compomics/psm_utils",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-15T02:41:03.491021Z",
+    "license": "Apache-2.0",
+    "name": "psm_utils",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JPROTEOME.2C00609",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society.A plethora of proteomics search engine output file formats are in circulation. This lack of standardized output files greatly complicates generic downstream processing of peptide-spectrum matches (PSMs) and PSM files. While standards exist to solve this problem, these are far from universally supported by search engines. Moreover, software libraries are available to read a selection of PSM file formats, but a package to parse PSM files into a unified data structure has been missing. Here, we present psm_utils, a Python package to read and write various PSM file formats and to handle peptidoforms, PSMs, and PSM lists in a unified and user-friendly Python-, command line-, and web-interface. psm_utils was developed with pragmatism and maintainability in mind, adhering to community standards and relying on existing packages where possible. The Python API and command line interface greatly facilitate handling various PSM file formats. Moreover, a user-friendly web application was built using psm_utils that allows anyone to interconvert PSM files and retrieve basic PSM statistics. psm_utils is freely available under the permissive Apache2 license at https://github.com/compomics/psm_utils.",
+                "authors": [
+                    {
+                        "name": "Bouwmeester R."
+                    },
+                    {
+                        "name": "Declercq A."
+                    },
+                    {
+                        "name": "Degroeve S."
+                    },
+                    {
+                        "name": "Gabriels R."
+                    },
+                    {
+                        "name": "Martens L."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Journal of Proteome Research",
+                "title": "Psm_utils: A High-Level Python API for Parsing and Handling Peptide-Spectrum Matches and Proteomics Search Results"
+            },
+            "pmid": "36508242"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/pspp/pspp.biotools.json b/data/pspp/pspp.biotools.json
new file mode 100644
index 0000000000000..0920300010b6e
--- /dev/null
+++ b/data/pspp/pspp.biotools.json
@@ -0,0 +1,57 @@
+{
+    "additionDate": "2023-01-31T07:02:12.726573Z",
+    "biotoolsCURIE": "biotools:pspp",
+    "biotoolsID": "pspp",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "description": "GNU PSPP is a program for statistical analysis of sampled data. It is a free as in freedom replacement for the proprietary program SPSS, and appears very similar to it with a few exceptions.\n\nThe most important of these exceptions are, that there are no “time bombs”; your copy of PSPP will not “expire” or deliberately stop working in the future. Neither are there any artificial limits on the number of cases or variables which you can use. There are no additional packages to purchase in order to get “advanced” functions; all functionality that PSPP currently supports is in the core package.",
+    "documentation": [
+        {
+            "type": [
+                "FAQ"
+            ],
+            "url": "https://www.gnu.org/software/pspp/faq.html"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://www.gnu.org/software/pspp/get.html"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                },
+                {
+                    "term": "Statistical calculation",
+                    "uri": "http://edamontology.org/operation_2238"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.gnu.org/software/pspp/",
+    "lastUpdate": "2023-02-01T12:30:57.520837Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "note": "Quick tour",
+            "type": [
+                "Other"
+            ],
+            "url": "https://www.gnu.org/software/pspp/tour.html"
+        }
+    ],
+    "name": "PSPP",
+    "owner": "iacs-biocomputacion",
+    "version": [
+        "1.6.2"
+    ]
+}
diff --git a/data/psrttca/psrttca.biotools.json b/data/psrttca/psrttca.biotools.json
new file mode 100644
index 0000000000000..37393957498ef
--- /dev/null
+++ b/data/psrttca/psrttca.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T02:35:50.654160Z",
+    "biotoolsCURIE": "biotools:psrttca",
+    "biotoolsID": "psrttca",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Chonlatip Pipattanaboon"
+        },
+        {
+            "name": "Phasit Charoenkwan"
+        },
+        {
+            "name": "Pietro Lio’",
+            "orcidid": "https://orcid.org/0000-0002-0540-5053"
+        },
+        {
+            "name": "Watshara Shoombuatong",
+            "orcidid": "https://orcid.org/0000-0002-3394-8709"
+        }
+    ],
+    "description": "A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://pmlabstack.pythonanywhere.com/PSRTTCA",
+    "lastUpdate": "2023-02-15T02:35:50.656871Z",
+    "name": "PSRTTCA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106368",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdDespite the arsenal of existing cancer therapies, the ongoing recurrence and new cases of cancer pose a serious health concern that necessitates the development of new and effective treatments. Cancer immunotherapy, which uses the body's immune system to combat cancer, is a promising treatment option. As a result, in silico methods for identifying and characterizing tumor T cell antigens (TTCAs) would be useful for better understanding their functional mechanisms. Although few computational methods for TTCA identification have been developed, their lack of model interpretability is a major drawback. Thus, developing computational methods for the effective identification and characterization of TTCAs is a critical endeavor. PSRTTCA, a new machine learning (ML)-based approach for improving the identification and characterization of TTCAs based on their primary sequences, is proposed in this study. Specifically, we introduce a new propensity score representation learning algorithm that allows one to generate various sets of propensity scores of amino acids, dipeptides, and g-gap dipeptides to be TTCAs. To enhance the predictive performance, optimal sets of variant propensity scores were determined and fed into the final meta-predictor (PSRTTCA). Benchmarking results revealed that PSRTTCA was a more precise and promising tool for the identification and characterization of TTCAs than conventional ML classifiers and existing methods. Furthermore, PSR-derived propensities of amino acids in becoming TTCAs are used to reveal the relationship between TTCAs and their informative physicochemical properties in order to provide insights into TTCA characteristics. Finally, a user-friendly online computational platform of PSRTTCA is publicly available at http://pmlabstack.pythonanywhere.com/PSRTTCA. The PSRTTCA predictor is anticipated to facilitate community-wide efforts in accelerating the discovery of novel TTCAs for cancer immunotherapy and other clinical applications.",
+                "authors": [
+                    {
+                        "name": "Charoenkwan P."
+                    },
+                    {
+                        "name": "Hasan M.M."
+                    },
+                    {
+                        "name": "Lio P."
+                    },
+                    {
+                        "name": "Moni M.A."
+                    },
+                    {
+                        "name": "Nantasenamat C."
+                    },
+                    {
+                        "name": "Pipattanaboon C."
+                    },
+                    {
+                        "name": "Shoombuatong W."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning"
+            },
+            "pmid": "36481763"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/psygenet2r/psygenet2r.biotools.json b/data/psygenet2r/psygenet2r.biotools.json
index 5510a0914194a..9f774353fbf43 100644
--- a/data/psygenet2r/psygenet2r.biotools.json
+++ b/data/psygenet2r/psygenet2r.biotools.json
@@ -6,6 +6,16 @@
         "BioConductor"
     ],
     "credit": [
+        {
+            "email": "carles.hernandez@isglobal.org",
+            "name": "Carles Hernandez-Ferrer",
+            "orcidid": "https://orcid.org/0000-0002-8029-7160",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Developer"
+            ],
+            "url": "http://www.carleshf.com"
+        },
         {
             "email": "alba.gutierrez@upf.edu",
             "name": "Alba Gutierrez-Sacristan",
@@ -13,6 +23,22 @@
             "typeRole": [
                 "Primary contact"
             ]
+        },
+        {
+            "email": "juanr.gonzalez@isglobal.org",
+            "name": "Juan R Gonzalez",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "email": "laura.furlong@upf.edu",
+            "name": "Laura I Furlong",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ]
         }
     ],
     "description": "Package to retrieve data from PsyGeNET database and to perform comorbidity studies with PsyGeNET's and user's data.",
@@ -27,11 +53,14 @@
     "download": [
         {
             "type": "Source code",
-            "url": "http://bioconductor/packages/release/bioc/src/contrib/psygenet2r_1.6.2.tar.gz"
+            "url": "http://www.bioconductor.org/packages/release/bioc/src/contrib/psygenet2r_1.30.0.tar.gz"
         }
     ],
     "editPermission": {
-        "type": "private"
+        "authors": [
+            "chernan3"
+        ],
+        "type": "group"
     },
     "function": [
         {
@@ -39,15 +68,19 @@
                 {
                     "term": "Database search",
                     "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Enrichment analysis",
+                    "uri": "http://edamontology.org/operation_3501"
                 }
             ]
         }
     ],
-    "homepage": "http://bioconductor.org/packages/release/bioc/html/psygenet2r.html",
+    "homepage": "http://www.bioconductor.org/packages/release/bioc/html/psygenet2r.html",
     "language": [
         "R"
     ],
-    "lastUpdate": "2018-12-10T12:58:54Z",
+    "lastUpdate": "2023-02-06T10:52:24.743022Z",
     "license": "MIT",
     "name": "psygenet2r",
     "operatingSystem": [
@@ -58,42 +91,31 @@
     "owner": "bioconductor_import",
     "publication": [
         {
-            "doi": "10.1093/bioinformatics/btv301",
+            "doi": "10.1093/bioinformatics/btx506",
             "metadata": {
-                "abstract": "© 2015 The Author.PsyGeNET (Psychiatric disorders and Genes association NETwork) is a knowledge platform for the exploratory analysis of psychiatric diseases and their associated genes. PsyGeNET is composed of a database and a web interface supporting data search, visualization, filtering and sharing. PsyGeNET integrates information from DisGeNET and data extracted from the literature by text mining, which has been curated by domain experts. It currently contains 2642 associations between 1271 genes and 37 psychiatric disease concepts. In its first release, PsyGeNET is focused on three psychiatric disorders: major depression, alcohol and cocaine use disorders. PsyGeNET represents a comprehensive, open access resource for the analysis of the molecular mechanisms underpinning psychiatric disorders and their comorbidities.",
+                "abstract": "© The Author 2017. Published by Oxford University Press. All rights reserved.Motivation: Psychiatric disorders have a great impact on morbidity and mortality. Genotype- phenotype resources for psychiatric diseases are key to enable the translation of research findings to a better care of patients. PsyGeNET is a knowledge resource on psychiatric diseases and their genes, developed by text mining and curated by domain experts. Results: We present psygenet2r, an R package that contains a variety of functions for leveraging PsyGeNET database and facilitating its analysis and interpretation. The package offers different types of queries to the database along with variety of analysis and visualization tools, including the study of the anatomical structures in which the genes are expressed and gaining insight of gene's molecular function. Psygenet2r is especially suited for network medicine analysis of psychiatric disorders.",
                 "authors": [
-                    {
-                        "name": "Bravo A."
-                    },
                     {
                         "name": "Furlong L.I."
                     },
                     {
-                        "name": "Grosdidier S."
+                        "name": "Gonzalez J.R."
                     },
                     {
                         "name": "Gutierrez-Sacristan A."
                     },
                     {
-                        "name": "Pinero J."
-                    },
-                    {
-                        "name": "Sanz F."
-                    },
-                    {
-                        "name": "Torrens M."
-                    },
-                    {
-                        "name": "Valverde O."
+                        "name": "Hernandez-Ferrer C."
                     }
                 ],
-                "citationCount": 38,
-                "date": "2015-05-11T00:00:00Z",
+                "citationCount": 2,
+                "date": "2017-12-15T00:00:00Z",
                 "journal": "Bioinformatics",
-                "title": "PsyGeNET: A knowledge platform on psychiatric disorders and their genes"
+                "title": "Psygenet2r: A R/Bioconductor package for the analysis of psychiatric disease genes"
             },
+            "pmid": "28961763",
             "type": [
-                "Other"
+                "Review"
             ]
         }
     ],
@@ -113,6 +135,6 @@
     ],
     "validated": 1,
     "version": [
-        "1.6.2"
+        "1.30.0"
     ]
 }
diff --git a/data/ptmint/ptmint.biotools.json b/data/ptmint/ptmint.biotools.json
new file mode 100644
index 0000000000000..f50913df9de25
--- /dev/null
+++ b/data/ptmint/ptmint.biotools.json
@@ -0,0 +1,128 @@
+{
+    "additionDate": "2023-02-25T14:13:11.980028Z",
+    "biotoolsCURIE": "biotools:ptmint",
+    "biotoolsID": "ptmint",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "haifengchen@sjtu.edu.cn",
+            "name": "Hai-Feng Chen",
+            "orcidid": "https://orcid.org/0000-0002-7496-4182",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "jian.zhang@sjtu.edu.cn",
+            "name": "Jian Zhang",
+            "orcidid": "https://orcid.org/0000-0002-6558-791X",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "jing.li@sjtu.edu.cn",
+            "name": "Jing Li",
+            "orcidid": "https://orcid.org/0000-0003-4602-3227",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "PTMint Database of Experimentally Verified PTM Regulation on Protein-Protein Interaction.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular docking",
+                    "uri": "http://edamontology.org/operation_0478"
+                },
+                {
+                    "term": "PTM localisation",
+                    "uri": "http://edamontology.org/operation_3755"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                },
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ptmint.sjtu.edu.cn/",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-02-25T14:13:11.982666Z",
+    "license": "Other",
+    "name": "PTMint",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC823",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Post-translational modification (PTM) is an important biochemical process. which includes six most well-studied types: phosphorylation, acetylation, methylation, sumoylation, ubiquitylation and glycosylation. PTM is involved in various cell signaling pathways and biological processes. Abnormal PTM status is closely associated with severe diseases (such as cancer and neurologic diseases) by regulating protein functions, such as protein-protein interactions (PPIs). A set of databases was constructed separately for PTM sites and PPI; however, the resource of regulation for PTM on PPI is still unsolved. RESULTS: Here, we firstly constructed a public accessible database of PTMint (PTMs that are associated with PPIs) (https://ptmint.sjtu.edu.cn/) that contains manually curated complete experimental evidence of the PTM regulation on PPIs in multiple organisms, including Homo sapiens, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae and Schizosaccharomyces pombe. Currently, the first version of PTMint encompassed 2477 non-redundant PTM sites in 1169 proteins affecting 2371 protein-protein pairs involving 357 diseases. Various annotations were systematically integrated, such as protein sequence, structure properties and protein complex analysis. PTMint database can help to insight into disease mechanism, disease diagnosis and drug discovery associated with PTM and PPI. AVAILABILITY AND IMPLEMENTATION: PTMint is freely available at: https://ptmint.sjtu.edu.cn/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Chen H.-F."
+                    },
+                    {
+                        "name": "Hong X."
+                    },
+                    {
+                        "name": "Li J."
+                    },
+                    {
+                        "name": "Li N."
+                    },
+                    {
+                        "name": "Lv J."
+                    },
+                    {
+                        "name": "Zhang J."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "PTMint database of experimentally verified PTM regulation on protein-protein interaction"
+            },
+            "pmcid": "PMC9848059",
+            "pmid": "36548389"
+        }
+    ],
+    "toolType": [
+        "Database portal",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Neurology",
+            "uri": "http://edamontology.org/topic_3334"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein modifications",
+            "uri": "http://edamontology.org/topic_0601"
+        }
+    ]
+}
diff --git a/data/pulps/pulps.biotools.json b/data/pulps/pulps.biotools.json
new file mode 100644
index 0000000000000..bf23571e7d22f
--- /dev/null
+++ b/data/pulps/pulps.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T10:27:18.034365Z",
+    "biotoolsCURIE": "biotools:pulps",
+    "biotoolsID": "pulps",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "guoyaping@zzu.edu.cn",
+            "name": "Yaping Guo",
+            "orcidid": "https://orcid.org/0000-0001-9937-363X",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "jlugomar@andrew.cmu.edu",
+            "name": "Jose Lugo-Martinez",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ruoxi Cai"
+        },
+        {
+            "name": "Peiran Jiang",
+            "orcidid": "https://orcid.org/0000-0003-1586-7806"
+        }
+    ],
+    "description": "A hybrid positive unlabeled learning framework for uncovering scaffolds across human proteome by measuring the propensity to drive phase separation.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence",
+                        "uri": "http://edamontology.org/data_2044"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Backbone modelling",
+                    "uri": "http://edamontology.org/operation_0479"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://pulps.zbiolab.cn",
+    "lastUpdate": "2023-03-18T10:27:18.038738Z",
+    "name": "PULPS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAD009",
+            "pmid": "36754843"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biophysics",
+            "uri": "http://edamontology.org/topic_3306"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
+}
diff --git a/data/pyascore/pyascore.biotools.json b/data/pyascore/pyascore.biotools.json
new file mode 100644
index 0000000000000..676b6d064f7f8
--- /dev/null
+++ b/data/pyascore/pyascore.biotools.json
@@ -0,0 +1,96 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-20T00:33:07.219299Z",
+    "biotoolsCURIE": "biotools:pyascore",
+    "biotoolsID": "pyascore",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jvillen@uw.edu",
+            "name": "Judit Villén",
+            "orcidid": "http://orcid.org/0000-0002-1005-1739",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Anthony S. Barente",
+            "orcidid": "http://orcid.org/0000-0002-8641-3775"
+        }
+    ],
+    "description": "A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "PTM localisation",
+                    "uri": "http://edamontology.org/operation_3755"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/AnthonyOfSeattle/pyAscoreValidation",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-20T00:33:07.222595Z",
+    "license": "MIT",
+    "name": "pyAscore",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/acs.jproteome.2c00194",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society.Determining the correct localization of post-translational modifications (PTMs) on peptides aids in interpreting their effect on protein function. While most algorithms for this task are available as standalone applications or incorporated into software suites, improving their versatility through access from popular scripting languages facilitates experimentation and incorporation into novel workflows. Here we describe pyAscore, an efficient and versatile implementation of the Ascore algorithm in Python for scoring the localization of user defined PTMs in data dependent mass spectrometry. pyAscore can be used from the command line or imported into Python scripts and accepts standard file formats from popular software tools used in bottom-up proteomics. Access to internal objects for scoring and working with modified peptides adds to the toolbox for working with PTMs in Python. pyAscore is available as an open source package for Python 3.6+ on all major operating systems and can be found at pyascore.readthedocs.io.",
+                "authors": [
+                    {
+                        "name": "Barente A.S."
+                    },
+                    {
+                        "name": "Villen J."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Journal of Proteome Research",
+                "title": "A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data"
+            },
+            "pmid": "36315500"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Protein modifications",
+            "uri": "http://edamontology.org/topic_0601"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/pygeneplexus/pygeneplexus.biotools.json b/data/pygeneplexus/pygeneplexus.biotools.json
new file mode 100644
index 0000000000000..49bb538e869aa
--- /dev/null
+++ b/data/pygeneplexus/pygeneplexus.biotools.json
@@ -0,0 +1,102 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T14:30:44.654626Z",
+    "biotoolsCURIE": "biotools:pygeneplexus",
+    "biotoolsID": "pygeneplexus",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Renming Liu"
+        },
+        {
+            "name": "Arjun Krishnan",
+            "orcidid": "http://orcid.org/0000-0002-7980-4110"
+        },
+        {
+            "name": "Christopher A Mancuso",
+            "orcidid": "http://orcid.org/0000-0003-3081-2758"
+        }
+    ],
+    "description": "A Python package for gene discovery using network-based machine learning.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://pygeneplexus.readthedocs.io/en/main/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene prediction",
+                    "uri": "http://edamontology.org/operation_2454"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/krishnanlab/PyGenePlexus",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-27T14:30:44.657202Z",
+    "license": "BSD-3-Clause",
+    "name": "PyGenePlexus",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btad064",
+            "metadata": {
+                "abstract": "© The Author(s) 2023. Published by Oxford University Press.SUMMARY: PyGenePlexus is a Python package that enables a user to gain insight into any gene set of interest through a molecular interaction network informed supervised machine learning model. PyGenePlexus provides predictions of how associated every gene in the network is to the input gene set, offers interpretability by comparing the model trained on the input gene set to models trained on thousands of known gene sets, and returns the network connectivity of the top predicted genes. AVAILABILITY AND IMPLEMENTATION: https://pypi.org/project/geneplexus/ and https://github.com/krishnanlab/PyGenePlexus. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Krishnan A."
+                    },
+                    {
+                        "name": "Liu R."
+                    },
+                    {
+                        "name": "Mancuso C.A."
+                    }
+                ],
+                "date": "2023-02-03T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "PyGenePlexus: a Python package for gene discovery using network-based machine learning"
+            },
+            "pmcid": "PMC9900208",
+            "pmid": "36721325"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genetics",
+            "uri": "http://edamontology.org/topic_3053"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        }
+    ]
+}
diff --git a/data/pymic/pymic.biotools.json b/data/pymic/pymic.biotools.json
new file mode 100644
index 0000000000000..d712a5c3c14d5
--- /dev/null
+++ b/data/pymic/pymic.biotools.json
@@ -0,0 +1,130 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T10:22:41.504265Z",
+    "biotoolsCURIE": "biotools:pymic",
+    "biotoolsID": "pymic",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Shaoting Zhang"
+        },
+        {
+            "name": "Xiangde Luo"
+        },
+        {
+            "name": "Guotai Wang",
+            "orcidid": "https://orcid.org/0000-0002-8632-158X"
+        },
+        {
+            "name": "Kang Li",
+            "orcidid": "https://orcid.org/0000-0002-8136-9816"
+        }
+    ],
+    "description": "A deep learning toolkit for annotation-efficient medical image segmentation.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://pymic.readthedocs.io/en/latest/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Image annotation",
+                    "uri": "http://edamontology.org/operation_3553"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/HiLab-git/PyMIC",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T10:22:41.508278Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/HiLab-git/PyMIC_examples"
+        }
+    ],
+    "name": "PyMIC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CMPB.2023.107398",
+            "metadata": {
+                "abstract": "Background and Objective: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models that are essential for computer-assisted diagnosis and treatment procedures. Existing toolkits mainly focus on fully supervised segmentation that assumes full and accurate pixel-level annotations are available. Such annotations are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost. We aim to develop a new deep learning toolkit to support annotation-efficient learning for medical image segmentation, which can accelerate and simplify the development of deep learning models with limited annotation budget, e.g., learning from partial, sparse or noisy annotations. Methods: Our proposed toolkit named PyMIC is a modular deep learning library for medical image segmentation tasks. In addition to basic components that support development of high-performance models for fully supervised segmentation, it contains several advanced components that are tailored for learning from imperfect annotations, such as loading annotated and unannounced images, loss functions for unannotated, partially or inaccurately annotated images, and training procedures for co-learning between multiple networks, etc. PyMIC is built on the PyTorch framework and supports development of semi-supervised, weakly supervised and noise-robust learning methods for medical image segmentation. Results: We present several illustrative medical image segmentation tasks based on PyMIC: (1) Achieving competitive performance on fully supervised learning; (2) Semi-supervised cardiac structure segmentation with only 10% training images annotated; (3) Weakly supervised segmentation using scribble annotations; and (4) Learning from noisy labels for chest radiograph segmentation. Conclusions: The PyMIC toolkit is easy to use and facilitates efficient development of medical image segmentation models with imperfect annotations. It is modular and flexible, which enables researchers to develop high-performance models with low annotation cost. The source code is available at:https://github.com/HiLab-git/PyMIC.",
+                "authors": [
+                    {
+                        "name": "Gu R."
+                    },
+                    {
+                        "name": "Li K."
+                    },
+                    {
+                        "name": "Luo X."
+                    },
+                    {
+                        "name": "Qu Y."
+                    },
+                    {
+                        "name": "Wang G."
+                    },
+                    {
+                        "name": "Yang S."
+                    },
+                    {
+                        "name": "Zhai S."
+                    },
+                    {
+                        "name": "Zhang S."
+                    },
+                    {
+                        "name": "Zhao Q."
+                    }
+                ],
+                "date": "2023-04-01T00:00:00Z",
+                "journal": "Computer Methods and Programs in Biomedicine",
+                "title": "PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation"
+            },
+            "pmid": "36773591"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Tomography",
+            "uri": "http://edamontology.org/topic_3452"
+        }
+    ]
+}
diff --git a/data/pymm/pymm.biotools.json b/data/pymm/pymm.biotools.json
new file mode 100644
index 0000000000000..a0882cfd968c3
--- /dev/null
+++ b/data/pymm/pymm.biotools.json
@@ -0,0 +1,94 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T23:16:21.633640Z",
+    "biotoolsCURIE": "biotools:pymm",
+    "biotoolsID": "pymm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Alessandro Nicola Nardi"
+        },
+        {
+            "name": "Andrea Amadei"
+        },
+        {
+            "name": "Cheng Giuseppe Chen",
+            "orcidid": "https://orcid.org/0000-0003-3553-4718"
+        },
+        {
+            "name": "Marco D’Abramo",
+            "orcidid": "https://orcid.org/0000-0001-6020-8581"
+        }
+    ],
+    "description": "An Open-Source Python Program for QM/MM Simulations Based on the Perturbed Matrix Method.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ChenGiuseppe/PyMM",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-30T23:16:21.636515Z",
+    "license": "GPL-2.0",
+    "name": "PyMM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCTC.2C00767",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Published by American Chemical Society.Quantum mechanical/molecular mechanics (QM/MM) methods are important tools in molecular modeling as they are able to couple an extended phase space sampling with an accurate description of the electronic properties of the system. Here, we describe a Python software package, called PyMM, which has been developed to apply a QM/MM approach, the perturbed matrix method, in a simple and efficient way. PyMM requires a classical atomic trajectory of the whole system and a set of unperturbed electronic properties of the ground and electronic excited states. The software output includes a set of the most common perturbed properties, such as the electronic excitation energies and the transitions dipole moments, as well as the eigenvectors describing the perturbed electronic states, which can be then used to estimate whatever electronic property. The software is composed of a simple and complete command-line interface, a set of internal input validation, and three main analyses focusing on (i) the perturbed eigenvector behavior, (ii) the calculation of the electronic absorption spectrum, and (iii) the estimation of the free energy differences along a reaction coordinate.",
+                "authors": [
+                    {
+                        "name": "Amadei A."
+                    },
+                    {
+                        "name": "Chen C.G."
+                    },
+                    {
+                        "name": "D'Abramo M."
+                    },
+                    {
+                        "name": "Nardi A.N."
+                    }
+                ],
+                "date": "2023-01-10T00:00:00Z",
+                "journal": "Journal of Chemical Theory and Computation",
+                "title": "PyMM: An Open-Source Python Program for QM/MM Simulations Based on the Perturbed Matrix Method"
+            },
+            "pmid": "36378163"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biomolecular simulation",
+            "uri": "http://edamontology.org/topic_3892"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        }
+    ]
+}
diff --git a/data/pypints/pypints.biotools.json b/data/pypints/pypints.biotools.json
new file mode 100644
index 0000000000000..6310b2260f00d
--- /dev/null
+++ b/data/pypints/pypints.biotools.json
@@ -0,0 +1,135 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-31T19:02:45.977662Z",
+    "biotoolsCURIE": "biotools:pyPINTS",
+    "biotoolsID": "pyPINTS",
+    "cost": "Free of charge",
+    "description": "Peak Identifier for Nascent Transcript Starts",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://github.com/hyulab/PINTS/blob/main/README.md"
+        },
+        {
+            "type": [
+                "Quick start guide"
+            ],
+            "url": "https://pints.yulab.org/tre_calling"
+        }
+    ],
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://github.com/hyulab/PINTS/archive/refs/heads/main.zip",
+            "version": "latest"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "RNA sequence",
+                        "uri": "http://edamontology.org/data_3495"
+                    },
+                    "format": [
+                        {
+                            "term": "BAM",
+                            "uri": "http://edamontology.org/format_2572"
+                        },
+                        {
+                            "term": "bigWig",
+                            "uri": "http://edamontology.org/format_3006"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Peak calling",
+                    "uri": "http://edamontology.org/operation_3222"
+                },
+                {
+                    "term": "Transcriptional regulatory element prediction",
+                    "uri": "http://edamontology.org/operation_0438"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Sequence coordinates",
+                        "uri": "http://edamontology.org/data_2012"
+                    },
+                    "format": [
+                        {
+                            "term": "BED",
+                            "uri": "http://edamontology.org/format_3003"
+                        }
+                    ]
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/hyulab/PINTS",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-31T22:06:47.303335Z",
+    "license": "GPL-3.0",
+    "maturity": "Mature",
+    "name": "PINTS",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "li_yao",
+    "publication": [
+        {
+            "doi": "10.1038/s41587-022-01211-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.Mounting evidence supports the idea that transcriptional patterns serve as more specific identifiers of active enhancers than histone marks; however, the optimal strategy to identify active enhancers both experimentally and computationally has not been determined. Here, we compared 13 genome-wide RNA sequencing (RNA-seq) assays in K562 cells and show that nuclear run-on followed by cap-selection assay (GRO/PRO-cap) has advantages in enhancer RNA detection and active enhancer identification. We also introduce a tool, peak identifier for nascent transcript starts (PINTS), to identify active promoters and enhancers genome wide and pinpoint the precise location of 5′ transcription start sites. Finally, we compiled a comprehensive enhancer candidate compendium based on the detected enhancer RNA (eRNA) transcription start sites (TSSs) available in 120 cell and tissue types, which can be accessed at https://pints.yulab.org. With knowledge of the best available assays and pipelines, this large-scale annotation of candidate enhancers will pave the way for selection and characterization of their functions in a time- and labor-efficient manner.",
+                "authors": [
+                    {
+                        "name": "Leung A.K.-Y."
+                    },
+                    {
+                        "name": "Liang J."
+                    },
+                    {
+                        "name": "Lis J.T."
+                    },
+                    {
+                        "name": "Ozer A."
+                    },
+                    {
+                        "name": "Yao L."
+                    },
+                    {
+                        "name": "Yu H."
+                    }
+                ],
+                "citationCount": 2,
+                "date": "2022-07-01T00:00:00Z",
+                "journal": "Nature Biotechnology",
+                "title": "A comparison of experimental assays and analytical methods for genome-wide identification of active enhancers"
+            },
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/pyradise/pyradise.biotools.json b/data/pyradise/pyradise.biotools.json
new file mode 100644
index 0000000000000..10f15d3ac3003
--- /dev/null
+++ b/data/pyradise/pyradise.biotools.json
@@ -0,0 +1,104 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T10:16:57.887590Z",
+    "biotoolsCURIE": "biotools:pyradise",
+    "biotoolsID": "pyradise",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Amith Kamath",
+            "orcidid": "https://orcid.org/0000-0002-7243-8883"
+        },
+        {
+            "name": "Close Stefan Scheib",
+            "orcidid": "https://orcid.org/0000-0001-9566-1132"
+        },
+        {
+            "name": "Elias Rüfenacht",
+            "orcidid": "https://orcid.org/0000-0001-6983-1695"
+        },
+        {
+            "name": "Mauricio Reyes",
+            "orcidid": "https://orcid.org/0000-0002-2434-9990"
+        }
+    ],
+    "description": "A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://pyradise.readthedocs.io/en/latest/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/ubern-mia/pyradise",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T10:16:57.892340Z",
+    "license": "Apache-2.0",
+    "name": "PyRaDiSe",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CMPB.2023.107374",
+            "metadata": {
+                "abstract": "Background and objective: Despite fast evolution cycles in deep learning methodologies for medical imaging in radiotherapy, auto-segmentation solutions rarely run in clinics due to the lack of open-source frameworks feasible for processing DICOM RT Structure Sets. Besides this shortage, available open-source DICOM RT Structure Set converters rely exclusively on 2D reconstruction approaches leading to pixelated contours with potentially low acceptance by healthcare professionals. PyRaDiSe, an open-source, deep learning framework independent Python package, addresses these issues by providing a framework for building auto-segmentation solutions feasible to operate directly on DICOM data. In addition, PyRaDiSe provides profound DICOM RT Structure Set conversion and processing capabilities; thus, it applies also to auto-segmentation-related tasks, such as dataset construction for deep learning model training. Methods: The PyRaDiSe package follows a holistic approach and provides DICOM data handling, deep learning model inference, pre-processing, and post-processing functionalities. The DICOM data handling allows for highly automated and flexible handling of DICOM image series, DICOM RT Structure Sets, and DICOM registrations, including 2D-based and 3D-based conversion from and to DICOM RT Structure Sets. For deep learning model inference, extending given skeleton classes is straightforwardly achieved, allowing for employing any deep learning framework. Furthermore, a profound set of pre-processing and post-processing routines is included that incorporate partial invertibility for restoring spatial properties, such as image origin or orientation. Results: The PyRaDiSe package, characterized by its flexibility and automated routines, allows for fast deployment and prototyping, reducing efforts for auto-segmentation pipeline implementation. Furthermore, while deep learning model inference is independent of the deep learning framework, it can easily be integrated into famous deep learning frameworks such as PyTorch or Tensorflow. The developed package has successfully demonstrated its capabilities in a research project at our institution for organs-at-risk segmentation in brain tumor patients. Furthermore, PyRaDiSe has shown its conversion performance for dataset construction. Conclusions: The PyRaDiSe package closes the gap between data science and clinical radiotherapy by enabling deep learning segmentation models to be easily transferred into clinical research practice. PyRaDiSe is available on https://github.com/ubern-mia/pyradise and can be installed directly from the Python Package Index using pip install pyradise.",
+                "authors": [
+                    {
+                        "name": "Ermis E."
+                    },
+                    {
+                        "name": "Kamath A."
+                    },
+                    {
+                        "name": "Poel R."
+                    },
+                    {
+                        "name": "Reyes M."
+                    },
+                    {
+                        "name": "Rufenacht E."
+                    },
+                    {
+                        "name": "Scheib S."
+                    },
+                    {
+                        "name": "Suter Y."
+                    }
+                ],
+                "date": "2023-04-01T00:00:00Z",
+                "journal": "Computer Methods and Programs in Biomedicine",
+                "title": "PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion"
+            },
+            "pmid": "36738608"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Medical imaging",
+            "uri": "http://edamontology.org/topic_3384"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/pythinfilm/pythinfilm.biotools.json b/data/pythinfilm/pythinfilm.biotools.json
new file mode 100644
index 0000000000000..f3081bf457d5a
--- /dev/null
+++ b/data/pythinfilm/pythinfilm.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T02:28:29.164834Z",
+    "biotoolsCURIE": "biotools:pythinfilm",
+    "biotoolsID": "pythinfilm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Alan E. Mark",
+            "orcidid": "https://orcid.org/0000-0001-5880-4798"
+        },
+        {
+            "name": "Martin Stroet",
+            "orcidid": "https://orcid.org/0000-0002-9570-2376"
+        },
+        {
+            "name": "Paul L. Burn",
+            "orcidid": "https://orcid.org/0000-0003-3405-3517"
+        },
+        {
+            "name": "Stephen Sanderson",
+            "orcidid": "https://orcid.org/0000-0002-4097-0496"
+        }
+    ],
+    "description": "Automated Molecular Dynamics Simulation Protocols for the Generation of Thin Film Morphologies.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://atb-uq.github.io/PyThinFilm/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/ATB-UQ/PyThinFilm",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-15T02:28:29.167467Z",
+    "license": "GPL-3.0",
+    "name": "PyThinFilm",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCIM.2C01334",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society.The performance of organic optoelectronic devices, such as organic light-emitting diodes (OLEDs) and organic solar cells (OSCs), is intrinsically related to the molecular-scale morphology of the thin films from which they are composed. However, the experimental characterization of morphology at the molecular level is challenging due to the often amorphous or at best semicrystalline nature of these films. Classical molecular modeling techniques, such as molecular dynamics (MD) simulation, are increasingly used to understand the relationship between morphology and the properties of thin-film devices. PyThinFilm (github.com/ATB-UQ/PyThinFilm) is an open-source Python package which allows fully automated MD simulations of thin film growth to be performed using vacuum and/or solution deposition processes. PyThinFilm utilizes the GROMACS simulation package in combination with interaction parameters from the Automated Topology Builder (atb.uq.edu.au). Here, PyThinFilm is described along with an overview of applications in which PyThinFilm has been used to study the thin films of organic semiconductor materials typically used in OLEDs and OSCs.",
+                "authors": [
+                    {
+                        "name": "Burn P.L."
+                    },
+                    {
+                        "name": "Caron B."
+                    },
+                    {
+                        "name": "Lee T."
+                    },
+                    {
+                        "name": "Mark A.E."
+                    },
+                    {
+                        "name": "Nada S."
+                    },
+                    {
+                        "name": "Sanderson S."
+                    },
+                    {
+                        "name": "Sanzogni A.V."
+                    },
+                    {
+                        "name": "Stroet M."
+                    }
+                ],
+                "date": "2023-01-09T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "PyThinFilm: Automated Molecular Dynamics Simulation Protocols for the Generation of Thin Film Morphologies"
+            },
+            "pmid": "36539938"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Molecular dynamics",
+            "uri": "http://edamontology.org/topic_0176"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        }
+    ]
+}
diff --git a/data/pyvisualfields/pyvisualfields.biotools.json b/data/pyvisualfields/pyvisualfields.biotools.json
new file mode 100644
index 0000000000000..31959ae14bda3
--- /dev/null
+++ b/data/pyvisualfields/pyvisualfields.biotools.json
@@ -0,0 +1,132 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T10:09:22.668842Z",
+    "biotoolsCURIE": "biotools:pyvisualfields",
+    "biotoolsID": "pyvisualfields",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mohammad_eslami@meei.harvard.edu",
+            "name": "Mohammad Eslami",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Nazlee Zebardast"
+        },
+        {
+            "name": "Saber Kazeminasab"
+        },
+        {
+            "name": "Tobias Elze"
+        }
+    ],
+    "description": "A Python Package for Visual Field Analysis.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/mohaEs/PyVisualField",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-03-18T10:09:22.673162Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://pypi.org/project/PyVisualFields/"
+        }
+    ],
+    "name": "PyVisualFields",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1167/TVST.12.2.6",
+            "metadata": {
+                "abstract": "Purpose: Artificial intelligence (AI) methods are changing all areas of research and have a variety of capabilities of analysis in ophthalmology, specifically in visual fields (VFs) to detect or predict vision loss progression. Whereas most of the AI algorithms are implemented in Python language, which offers numerous open-source functions and algorithms, the majority of algorithms in VF analysis are offered in the R language. This paper introduces PyVisualFields, a developed package to address this gap and make available VF analysis in the Python language. Methods: For the first version, the R libraries for VF analysis provided by vfprogression and visualFields packages are analyzed to define the overlaps and distinct functions. Then, we defined and translated this functionality into Python with the help of the wrapper library rpy2. Besides maintaining, the subsequent versions’ milestones are established, and the third version will be R-independent. Results: The developed Python package is available as open-source software via the GitHub repository and is ready to be installed from PyPI. Several Jupyter notebooks are prepared to demonstrate and describe the capabilities of the PyVisualFields package in the categories of data presentation, normalization and deviation analysis, plotting, scoring, and progression analysis. Conclusions: We developed a Python package and demonstrated its functionality for VF analysis and facilitating ophthalmic research in VF statistical analysis, illustration, and progression prediction. Translational Relevance: Using this software package, researchers working on VF analysis can more quickly create algorithms for clinical applications using cutting-edge AI techniques.",
+                "authors": [
+                    {
+                        "name": "Elze T."
+                    },
+                    {
+                        "name": "Eslami M."
+                    },
+                    {
+                        "name": "Fazli M."
+                    },
+                    {
+                        "name": "Kazeminasab S."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Sharma V."
+                    },
+                    {
+                        "name": "Wang M."
+                    },
+                    {
+                        "name": "Zebardast N."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "Translational Vision Science and Technology",
+                "title": "PyVisualFields: A Python Package for Visual Field Analysis"
+            },
+            "pmcid": "PMC9910386",
+            "pmid": "36745440"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/qcloud2/qcloud2.biotools.json b/data/qcloud2/qcloud2.biotools.json
index 1eb9d03616bce..f9530d92edfbd 100644
--- a/data/qcloud2/qcloud2.biotools.json
+++ b/data/qcloud2/qcloud2.biotools.json
@@ -1,10 +1,69 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2021-05-28T06:46:47Z",
     "biotoolsCURIE": "biotools:qcloud2",
     "biotoolsID": "qcloud2",
+    "credit": [
+        {
+            "email": "cristina.chiva@upf.edu",
+            "name": "Cristina Chiva",
+            "orcidid": "https://orcid.org/0000-0001-8150-6203"
+        },
+        {
+            "email": "eduard.sabido@crg.eu",
+            "name": "Eduard Sabidó",
+            "orcidid": "https://orcid.org/0000-0001-6506-7714"
+        },
+        {
+            "email": "roger.olivell@crg.eu",
+            "name": "Roger Olivella",
+            "orcidid": "https://orcid.org/0000-0001-9393-6569",
+            "url": "https://github.com/rolivella"
+        }
+    ],
     "description": "An Improved Cloud-based Quality-Control System for Mass-Spectrometry-based Proteomics Laboratories.",
+    "documentation": [
+        {
+            "note": "Getting started as a lab manager",
+            "type": [
+                "Quick start guide"
+            ],
+            "url": "https://www.dropbox.com/s/5xani1zi7guqez7/getting-started-as-a-lab-manager.mp4"
+        },
+        {
+            "note": "Getting started as a user",
+            "type": [
+                "Quick start guide"
+            ],
+            "url": "https://www.dropbox.com/s/e2mil82vuccvcox/getting-started-as-user.mp4"
+        },
+        {
+            "note": "How to install QCloud local version",
+            "type": [
+                "Installation instructions"
+            ],
+            "url": "https://github.com/proteomicsunitcrg/qcloud2-pipeline/tree/local#readme"
+        },
+        {
+            "note": "QCloud API documentation",
+            "type": [
+                "API documentation"
+            ],
+            "url": "https://documenter.getpostman.com/view/8947947/SVn3qu8K"
+        },
+        {
+            "note": "Thresholds and guidesets",
+            "type": [
+                "Quick start guide"
+            ],
+            "url": "https://www.dropbox.com/s/tqn83u22m7fs1tw/threholds-and-guidesets.mp4"
+        }
+    ],
     "editPermission": {
-        "type": "private"
+        "authors": [
+            "rolivella"
+        ],
+        "type": "group"
     },
     "function": [
         {
@@ -20,21 +79,32 @@
             ]
         }
     ],
-    "homepage": "https://qcloud2.crg.eu/login",
+    "homepage": "https://qcloud2.crg.eu",
     "language": [
-        "Groovy"
+        "Groovy",
+        "Java",
+        "JavaScript"
     ],
-    "lastUpdate": "2021-05-28T06:46:47Z",
+    "lastUpdate": "2023-02-20T15:40:14.360366Z",
     "link": [
         {
+            "note": "For installing QCloud in a local computer",
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/proteomicsunitcrg/qcloud2-pipeline/tree/local"
+        },
+        {
+            "note": "QCloud pipeline repository",
             "type": [
                 "Repository"
             ],
             "url": "https://github.com/proteomicsunitcrg/qcloud2-pipeline"
         }
     ],
+    "maturity": "Mature",
     "name": "QCloud2",
-    "owner": "Niclaskn",
+    "owner": "rolivella",
     "publication": [
         {
             "doi": "10.1021/ACS.JPROTEOME.0C00853",
@@ -81,14 +151,50 @@
                         "name": "Sole A."
                     }
                 ],
+                "citationCount": 5,
                 "date": "2021-04-02T00:00:00Z",
                 "journal": "Journal of Proteome Research",
                 "title": "QCloud2: An Improved Cloud-based Quality-Control System for Mass-Spectrometry-based Proteomics Laboratories"
             },
             "pmid": "33724836"
+        },
+        {
+            "doi": "10.1371/journal.pone.0189209",
+            "metadata": {
+                "abstract": "© 2018 Chiva et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The increasing number of biomedical and translational applications in mass spectrometry-based proteomics poses new analytical challenges and raises the need for automated quality control systems. Despite previous efforts to set standard file formats, data processing workflows and key evaluation parameters for quality control, automated quality control systems are not yet widespread among proteomics laboratories, which limits the acquisition of high-quality results, inter-laboratory comparisons and the assessment of variability of instrumental platforms. Here we present QCloud, a cloud-based system to support proteomics laboratories in daily quality assessment using a user-friendly interface, easy setup, automated data processing and archiving, and unbiased instrument evaluation. QCloud supports the most common targeted and untargeted proteomics workflows, it accepts data formats from different vendors and it enables the annotation of acquired data and reporting incidences. A complete version of the QCloud system has successfully been developed and it is now open to the proteomics community (http://qcloud.crg.eu). QCloud system is an open source project, publicly available under a Creative Commons License Attribution-ShareAlike 4.0.",
+                "authors": [
+                    {
+                        "name": "Borras E."
+                    },
+                    {
+                        "name": "Chiva C."
+                    },
+                    {
+                        "name": "Espadas G."
+                    },
+                    {
+                        "name": "Olivella R."
+                    },
+                    {
+                        "name": "Pastor O."
+                    },
+                    {
+                        "name": "Sabido E."
+                    },
+                    {
+                        "name": "Sole A."
+                    }
+                ],
+                "citationCount": 47,
+                "date": "2018-01-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories"
+            },
+            "pmid": "29324744"
         }
     ],
     "toolType": [
+        "Web API",
         "Web application"
     ],
     "topic": [
@@ -103,10 +209,6 @@
         {
             "term": "Proteomics experiment",
             "uri": "http://edamontology.org/topic_3520"
-        },
-        {
-            "term": "Sequence analysis",
-            "uri": "http://edamontology.org/topic_0080"
         }
     ]
 }
diff --git a/data/qnabpredict/qnabpredict.biotools.json b/data/qnabpredict/qnabpredict.biotools.json
new file mode 100644
index 0000000000000..b07002bedc51e
--- /dev/null
+++ b/data/qnabpredict/qnabpredict.biotools.json
@@ -0,0 +1,128 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T02:22:06.049756Z",
+    "biotoolsCURIE": "biotools:qnabpredict",
+    "biotoolsID": "qnabpredict",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "lkurgan@vcu.edu",
+            "name": "Lukasz Kurgan",
+            "orcidid": "https://orcid.org/0000-0002-7749-0314",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Sushmita Basu"
+        },
+        {
+            "name": "Xuantai Wu",
+            "orcidid": "https://orcid.org/0000-0002-3694-2011"
+        },
+        {
+            "name": "Zhonghua Wu",
+            "orcidid": "https://orcid.org/0000-0002-4191-0241"
+        }
+    ],
+    "description": "Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Protein disorder prediction",
+                    "uri": "http://edamontology.org/operation_3904"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                },
+                {
+                    "term": "RNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3901"
+                },
+                {
+                    "term": "Residue contact prediction",
+                    "uri": "http://edamontology.org/operation_0272"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://biomine.cs.vcu.edu/servers/qNABpredict/",
+    "lastUpdate": "2023-02-15T02:22:06.052398Z",
+    "name": "qNABpredict",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/PRO.4544",
+            "metadata": {
+                "abstract": "© 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.Protein sequence-based predictors of nucleic acid (NA)-binding include methods that predict NA-binding proteins and NA-binding residues. The residue-level tools produce more details but suffer high computational cost since they must predict every amino acid in the input sequence and rely on multiple sequence alignments. We propose an alternative approach that predicts content (fraction) of the NA-binding residues, offering more information than the protein-level prediction and much shorter runtime than the residue-level tools. Our first-of-its-kind content predictor, qNABpredict, relies on a small, rationally designed and fast-to-compute feature set that represents relevant characteristics extracted from the input sequence and a well-parametrized support vector regression model. We provide two versions of qNABpredict, a taxonomy-agnostic model that can be used for proteins of unknown taxonomic origin and more accurate taxonomy-aware models that are tailored to specific taxonomic kingdoms: archaea, bacteria, eukaryota, and viruses. Empirical tests on a low-similarity test dataset show that qNABpredict is 100 times faster and generates statistically more accurate content predictions when compared to the content extracted from results produced by the residue-level predictors. We also show that qNABpredict's content predictions can be used to improve results generated by the residue-level predictors. We release qNABpredict as a convenient webserver and source code at http://biomine.cs.vcu.edu/servers/qNABpredict/. This new tool should be particularly useful to predict details of protein–NA interactions for large protein families and proteomes.",
+                "authors": [
+                    {
+                        "name": "Basu S."
+                    },
+                    {
+                        "name": "Kurgan L."
+                    },
+                    {
+                        "name": "Wu X."
+                    },
+                    {
+                        "name": "Wu Z."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Protein Science",
+                "title": "qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids"
+            },
+            "pmcid": "PMC9798252",
+            "pmid": "36519304"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Nucleic acids",
+            "uri": "http://edamontology.org/topic_0077"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/qvt/qvt.biotools.json b/data/qvt/qvt.biotools.json
new file mode 100644
index 0000000000000..720ce0ec1f556
--- /dev/null
+++ b/data/qvt/qvt.biotools.json
@@ -0,0 +1,99 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T02:12:13.431379Z",
+    "biotoolsCURIE": "biotools:qvt",
+    "biotoolsID": "qvt",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Eduard Montseny"
+        },
+        {
+            "name": "Nelly Gordillo"
+        },
+        {
+            "name": "Pilar Sobrevilla"
+        }
+    ],
+    "description": "Automated hemodynamic assessment for cranial 4D flow MRI.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/uwmri/QVT",
+    "language": [
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-02-15T02:12:13.433899Z",
+    "license": "BSD-2-Clause",
+    "name": "QVT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.MRI.2022.12.016",
+            "metadata": {
+                "abstract": "© 2022 Elsevier Inc.Cranial 4D flow MRI post-processing typically involves manual user interaction which is time-consuming and associated with poor repeatability. The primary goal of this study is to develop a robust quantitative velocity tool (QVT) that utilizes threshold-based segmentation techniques to improve segmentation quality over prior approaches based on centerline processing schemes (CPS) that utilize k-means clustering segmentation. This tool also includes an interactive 3D display designed for simplified vessel selection and automated hemodynamic visualization and quantification. The performances of QVT and CPS were compared in vitro in a flow phantom and in vivo in 10 healthy participants. Vessel segmentations were compared with ground-truth computed tomography in vitro (29 locations) and manual segmentation in vivo (13 locations) using linear regression. Additionally, QVT and CPS MRI flow rates were compared to perivascular ultrasound flow in vitro using linear regression. To assess internal consistency of flow measures in vivo, conservation of flow was assessed at vessel junctions using linear regression and consistency of flow along vessel segments was analyzed by fitting a Gaussian distribution to a histogram of normalized flow values. Post-processing times were compared between the QVT and CPS using paired t-tests. Vessel areas segmented in vitro (CPS: slope = 0.71, r = 0.95 and QVT: slope = 1.03, r = 0.95) and in vivo (CPS: slope = 0.61, r = 0.96 and QVT: slope = 0.93, r = 0.96) were strongly correlated with ground-truth area measurements. However, CPS (using k-means segmentation) consistently underestimated vessel areas. Strong correlations were observed between QVT and ultrasound flow (slope = 0.98, r = 0.96) as well as flow at junctions (slope = 1.05, r = 0.98). Mean and standard deviation of flow along vessel segments was 9.33e-16 ± 3.05%. Additionally, the QVT demonstrated excellent interobserver agreement and significantly reduced post-processing by nearly 10 min (p < 0.001). By completely automating post-processing and providing an easy-to-use 3D visualization interface for interactive vessel selection and hemodynamic quantification, the QVT offers an efficient, robust, and repeatable means to analyze cranial 4D flow MRI. This software is freely available at: https://github.com/uwmri/QVT.",
+                "authors": [
+                    {
+                        "name": "Berman S.E."
+                    },
+                    {
+                        "name": "Eisenmenger L.B."
+                    },
+                    {
+                        "name": "Hoffman C.A."
+                    },
+                    {
+                        "name": "Rivera-Rivera L.A."
+                    },
+                    {
+                        "name": "Roberts G.S."
+                    },
+                    {
+                        "name": "Wieben O."
+                    }
+                ],
+                "date": "2023-04-01T00:00:00Z",
+                "journal": "Magnetic Resonance Imaging",
+                "title": "Automated hemodynamic assessment for cranial 4D flow MRI"
+            },
+            "pmid": "36581214"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Tomography",
+            "uri": "http://edamontology.org/topic_3452"
+        }
+    ]
+}
diff --git a/data/r-sim/r-sim.biotools.json b/data/r-sim/r-sim.biotools.json
new file mode 100644
index 0000000000000..3267f9f610519
--- /dev/null
+++ b/data/r-sim/r-sim.biotools.json
@@ -0,0 +1,97 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T02:08:05.041866Z",
+    "biotoolsCURIE": "biotools:r-sim",
+    "biotoolsID": "r-sim",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Arijit Roy"
+        },
+        {
+            "name": "M. Michael Gromiha"
+        },
+        {
+            "name": "Sowmya Ramaswamy Krishnan"
+        }
+    ],
+    "description": "A Database of Binding Affinities for RNA-small Molecule Interactions.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Sorting",
+                    "uri": "http://edamontology.org/operation_3802"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://web.iitm.ac.in/bioinfo2/R_SIM/index.html",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-15T02:08:05.045143Z",
+    "name": "R-SIM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.JMB.2022.167914",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdRibonucleic acids (RNAs) are involved in a multitude of crucial cellular functions by acting as a central conduit for information transfer. Due to their essential and versatile functional roles in the cell, RNAs have also been implicated in multiple disease conditions of therapeutic relevance including cancers, bacterial and viral infections and neurodegenerative disorders. Recently, several approaches have emerged to tap into the potentially unexplored regions of the druggable genome, which refers to the genes and gene products that are focused during drug development. For example, considering RNAs as viable alternative therapeutic targets for drug development can potentially expand the range of therapeutic targets. Consequently, the availability of adequate binding affinity measurements for RNA-small molecule interactions is essential to understand target selectivity and design more potent RNA-targeting drug-like molecules. To facilitate this growing need, we have curated a database of experimentally validated RNA-small molecule interactions, called RNA-Small molecule Interaction Miner (R-SIM). Each entry in R-SIM provides comprehensive information on sequence, structure and classification of the RNA target, various physicochemical properties of the small molecule, binding affinity value and corresponding experimental conditions, three-dimensional structure (experimental or modelled) of the RNA-small molecule complex, and the literature source for the data. It also provides a user-friendly web interface with several options for search, display, sorting, visualization, download and upload of the data. R-SIM is freely available at: https://web.iitm.ac.in/bioinfo2/R_SIM/index.html. We envisage that R-SIM has several potential applications in understanding and accelerating the development of novel RNA-targeted small molecule therapeutics.",
+                "authors": [
+                    {
+                        "name": "Michael Gromiha M."
+                    },
+                    {
+                        "name": "Ramaswamy Krishnan S."
+                    },
+                    {
+                        "name": "Roy A."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Journal of Molecular Biology",
+                "title": "R-SIM: A Database of Binding Affinities for RNA-small Molecule Interactions"
+            },
+            "pmid": "36495921"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Biotechnology",
+            "uri": "http://edamontology.org/topic_3297"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Proteins",
+            "uri": "http://edamontology.org/topic_0078"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/raagr2-net/raagr2-net.biotools.json b/data/raagr2-net/raagr2-net.biotools.json
new file mode 100644
index 0000000000000..74a98cbb69c8f
--- /dev/null
+++ b/data/raagr2-net/raagr2-net.biotools.json
@@ -0,0 +1,94 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-15T02:03:53.572616Z",
+    "biotoolsCURIE": "biotools:raagr2-net",
+    "biotoolsID": "raagr2-net",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Imran Fareed Nizami"
+        },
+        {
+            "name": "Jihyoung Ryu"
+        },
+        {
+            "name": "Kil To Chong",
+            "orcidid": "https://orcid.org/0000-0002-1952-0001"
+        },
+        {
+            "name": "Mobeen Ur Rehman",
+            "orcidid": "https://orcid.org/0000-0003-0914-7132"
+        }
+    ],
+    "description": "A brain tumor segmentation network using parallel processing of multiple spatial frames.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Rehman1995/RAAGR2-Net",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-15T02:03:53.576195Z",
+    "license": "Not licensed",
+    "name": "RAAGR2-Net",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106426",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdBrain tumors are one of the most fatal cancers. Magnetic Resonance Imaging (MRI) is a non-invasive method that provides multi-modal images containing important information regarding the tumor. Many contemporary techniques employ four modalities: T1-weighted (T1), T1-weighted with contrast (T1c), T2-weighted (T2), and fluid-attenuation-inversion-recovery (FLAIR), each of which provides unique and important characteristics for the location of each tumor. Although several modern procedures provide decent segmentation results on the multimodal brain tumor image segmentation benchmark (BraTS) dataset, they lack performance when evaluated simultaneously on all the regions of MRI images. Furthermore, there is still room for improvement due to parameter limitations and computational complexity. Therefore, in this work, a novel encoder–decoder-based architecture is proposed for the effective segmentation of brain tumor regions. Data pre-processing is performed by applying N4 bias field correction, z-score, and 0 to 1 resampling to facilitate model training. To minimize the loss of location information in different modules, a residual spatial pyramid pooling (RASPP) module is proposed. RASPP is a set of parallel layers using dilated convolution. In addition, an attention gate (AG) module is used to efficiently emphasize and restore the segmented output from extracted feature maps. The proposed modules attempt to acquire rich feature representations by combining knowledge from diverse feature maps and retaining their local information. The performance of the proposed deep network based on RASPP, AG, and recursive residual (R2) block termed RAAGR2-Net is evaluated on the BraTS benchmarks. The experimental results show that the suggested network outperforms existing networks that exhibit the usefulness of the proposed modules for “fine” segmentation. The code for this work is made available online at: https://github.com/Rehman1995/RAAGR2-Net.",
+                "authors": [
+                    {
+                        "name": "Chong K.T."
+                    },
+                    {
+                        "name": "Nizami I.F."
+                    },
+                    {
+                        "name": "Rehman M.U."
+                    },
+                    {
+                        "name": "Ryu J."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames"
+            },
+            "pmid": "36565485"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Human biology",
+            "uri": "http://edamontology.org/topic_2815"
+        },
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        }
+    ]
+}
diff --git a/data/rabbit_in_a_hat/rabbit_in_a_hat.biotools.json b/data/rabbit_in_a_hat/rabbit_in_a_hat.biotools.json
new file mode 100644
index 0000000000000..12c62b9be9123
--- /dev/null
+++ b/data/rabbit_in_a_hat/rabbit_in_a_hat.biotools.json
@@ -0,0 +1,50 @@
+{
+    "additionDate": "2023-01-26T08:45:11.328282Z",
+    "biotoolsCURIE": "biotools:rabbit_in_a_hat",
+    "biotoolsID": "rabbit_in_a_hat",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "Observational Health Data Sciences and Informatics   OHDSI",
+            "typeEntity": "Consortium",
+            "typeRole": [
+                "Documentor"
+            ],
+            "url": "https://www.ohdsi.org/"
+        }
+    ],
+    "description": "Rabbit-In-a-Hat comes with WhiteRabbit and is designed to read and display a WhiteRabbit scan document. WhiteRabbit generates information about the source data while Rabbit-In-a-Hat uses that information and through a graphical user interface to allow a user to connect source data to tables and columns within the CDM. Rabbit-In-a-Hat generates documentation for the ETL process it does not generate code to create an ETL",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://github.com/OHDSI/WhiteRabbit/issues"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "homepage": "http://ohdsi.github.io/WhiteRabbit/RabbitInAHat.html",
+    "lastUpdate": "2023-02-01T12:07:52.542819Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Software catalogue"
+            ],
+            "url": "http://ohdsi.github.io/WhiteRabbit/RabbitInAHat.html"
+        }
+    ],
+    "name": "Rabbit in a Hat",
+    "owner": "iacs-biocomputacion",
+    "relation": [
+        {
+            "biotoolsID": "white_rabbit",
+            "type": "uses"
+        }
+    ],
+    "version": [
+        "CDM version (v4, v5 or v6)"
+    ]
+}
diff --git a/data/rabbitfx/rabbitfx.biotools.json b/data/rabbitfx/rabbitfx.biotools.json
new file mode 100644
index 0000000000000..370f2f877ed22
--- /dev/null
+++ b/data/rabbitfx/rabbitfx.biotools.json
@@ -0,0 +1,115 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T23:05:42.950682Z",
+    "biotoolsCURIE": "biotools:rabbitfx",
+    "biotoolsID": "rabbitfx",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Honglei Song"
+        },
+        {
+            "name": "Bertil Schmidt",
+            "orcidid": "https://orcid.org/0000-0003-2597-8331"
+        },
+        {
+            "name": "Hao Zhang",
+            "orcidid": "https://orcid.org/0000-0001-9548-5239"
+        },
+        {
+            "name": "Weiguo Liu",
+            "orcidid": "https://orcid.org/0000-0001-8834-0453"
+        }
+    ],
+    "description": "Efficient Framework for FASTA/Q File Parsing on Modern Multi-Core Platforms.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://rabbitfx-doc.readthedocs.io/en/latest/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/RabbitBio/RabbitFX",
+    "language": [
+        "C++"
+    ],
+    "lastUpdate": "2023-01-30T23:06:40.730789Z",
+    "license": "GPL-3.0",
+    "name": "RabbitFX",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1109/TCBB.2022.3219114",
+            "metadata": {
+                "abstract": "IEEEThe continuous growth of generated sequencing data leads to the development of a variety of associated bioinformatics tools. However, many of them are not able to fully exploit the resources of modern multi-core systems since they are bottlenecked by parsing files leading to slow execution times. This motivates the design of an efficient method for parsing sequencing data that can exploit the power of modern hardware, especially for modern CPUs with fast storage devices. We have developed RabbitFX, a fast, efficient, and easy-to-use framework for processing biological sequencing data on modern multi-core platforms. It can efficiently read FASTA and FASTQ files by combining a lightweight parsing method by means of an optimized formatting implementation. Furthermore, we provide user-friendly and modularized C++ APIs that can be easily integrated into applications in order to increase their file parsing speed. As proof-of-concept, we have integrated RabbitFX into three I/O-intensive applications: fastp, Ktrim, and Mash. Our evaluation shows that the inclusion of RabbitFX leads to speedups of at least 11.6 (6.6), 2.4 (2.4), and 3.7 (3.2) compared to the original versions on plain (gzip-compressed) files, respectively. These case studies demonstrate that RabbitFX can be easily integrated into a variety of NGS analysis tools to significantly reduce associated runtimes. It is open source software available at <uri>https://github.com/RabbitBio/RabbitFX</uri>.",
+                "authors": [
+                    {
+                        "name": "Chang Q."
+                    },
+                    {
+                        "name": "Liu W."
+                    },
+                    {
+                        "name": "Schmidt B."
+                    },
+                    {
+                        "name": "Song H."
+                    },
+                    {
+                        "name": "Wang M."
+                    },
+                    {
+                        "name": "Wei Y."
+                    },
+                    {
+                        "name": "Xu X."
+                    },
+                    {
+                        "name": "Yin Z."
+                    },
+                    {
+                        "name": "Zhang H."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
+                "title": "RabbitFX: Efficient Framework for FASTA/Q File Parsing on Modern Multi-Core Platforms"
+            },
+            "pmid": "36327193"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/ralps/ralps.biotools.json b/data/ralps/ralps.biotools.json
new file mode 100644
index 0000000000000..a119bdeaae26f
--- /dev/null
+++ b/data/ralps/ralps.biotools.json
@@ -0,0 +1,98 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T01:17:27.600966Z",
+    "biotoolsCURIE": "biotools:ralps",
+    "biotoolsID": "ralps",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "nzamboni@ethz.ch",
+            "name": "Nicola Zamboni",
+            "orcidid": "https://orcid.org/0000-0003-1271-1021",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Andrei Dmitrenko"
+        },
+        {
+            "name": "Michelle Reid"
+        }
+    ],
+    "description": "Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/zamboni-lab/RALPS",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-03-18T01:17:27.604754Z",
+    "license": "GPL-3.0",
+    "name": "RALPS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD096",
+            "metadata": {
+                "abstract": "MOTIVATION: Untargeted metabolomics by mass spectrometry is the method of choice for unbiased analysis of molecules in complex samples of biological, clinical or environmental relevance. The exceptional versatility and sensitivity of modern high-resolution instruments allows profiling of thousands of known and unknown molecules in parallel. Inter-batch differences constitute a common and unresolved problem in untargeted metabolomics, and hinder the analysis of multi-batch studies or the intercomparison of experiments. RESULTS: We present a new method, Regularized Adversarial Learning Preserving Similarity (RALPS), for the normalization of multi-batch untargeted metabolomics data. RALPS builds on deep adversarial learning with a three-term loss function that mitigates batch effects while preserving biological identity, spectral properties and coefficients of variation. Using two large metabolomics datasets, we showcase the superior performance of RALPS as compared with six state-of-the-art methods for batch correction. Further, we demonstrate that RALPS scales well, is robust, deals with missing values and can handle different experimental designs. AVAILABILITY AND IMPLEMENTATION: https://github.com/zamboni-lab/RALPS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Dmitrenko A."
+                    },
+                    {
+                        "name": "Reid M."
+                    },
+                    {
+                        "name": "Zamboni N."
+                    }
+                ],
+                "date": "2023-03-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data"
+            },
+            "pmid": "36825815"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        }
+    ]
+}
diff --git a/data/ramp_script/ramp_script.biotools.json b/data/ramp_script/ramp_script.biotools.json
new file mode 100644
index 0000000000000..8da72bfd3df44
--- /dev/null
+++ b/data/ramp_script/ramp_script.biotools.json
@@ -0,0 +1,86 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T22:46:07.456241Z",
+    "biotoolsCURIE": "biotools:ramp_script",
+    "biotoolsID": "ramp_script",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "seminlee@unist.ac.kr",
+            "name": "Jiwon Seo",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "seojiwon@hanyang.ac.kr",
+            "name": "Semin Lee",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "wkjeong@korea.ac.kr",
+            "name": "Won-Ki Jeong",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Kanggeun Lee"
+        }
+    ],
+    "description": "Response-aware multi-task learning with contrastive regularization for cancer drug response prediction.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/hvcl/RAMP",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T22:46:07.458761Z",
+    "license": "Not licensed",
+    "name": "RAMP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC504",
+            "pmid": "36460623"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/rapidminer/rapidminer.biotools.json b/data/rapidminer/rapidminer.biotools.json
new file mode 100644
index 0000000000000..3ba3197d7e443
--- /dev/null
+++ b/data/rapidminer/rapidminer.biotools.json
@@ -0,0 +1,52 @@
+{
+    "additionDate": "2023-01-31T07:26:43.913068Z",
+    "biotoolsCURIE": "biotools:rapidminer",
+    "biotoolsID": "rapidminer",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "University of Dortmund",
+            "typeEntity": "Institute",
+            "url": "https://rapidminer.com/about-us/"
+        }
+    ],
+    "description": "Two innovators coming together to transform business analytics. Altair acquires RapidMiner.\n\nAltair and RapidMiner share the same vision to make data analytics simple enough for all users, but scalable, governed, and safe enough for all enterprises. RapidMiner is the enterprise-ready data science platform that amplifies the collective impact of your people, expertise and data for breakthrough competitive advanta",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://rapidminer.com/request-a-demo/"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Calculation",
+                    "uri": "http://edamontology.org/operation_3438"
+                },
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://rapidminer.com/",
+    "lastUpdate": "2023-02-01T12:48:46.909454Z",
+    "license": "AGPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Software catalogue"
+            ],
+            "url": "https://rapidminer.com/platform/"
+        }
+    ],
+    "name": "Rapidminer",
+    "owner": "iacs-biocomputacion"
+}
diff --git a/data/razers3/razers3.biotools.json b/data/razers3/razers3.biotools.json
index 0222827bf904e..474e437e2f814 100644
--- a/data/razers3/razers3.biotools.json
+++ b/data/razers3/razers3.biotools.json
@@ -1,7 +1,10 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2021-04-22T01:09:26Z",
     "biotoolsCURIE": "biotools:razers3",
     "biotoolsID": "razers3",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "RazerS 3 is a tool for mapping millions of short genomic reads onto a\nreference genome. It was designed with focus on mapping next-generation\nsequencing reads onto whole DNA genomes. RazerS 3 searches for matches of\nreads with a percent identity above a given threshold (-i X), whereby it\ndetects alignments with mismatches as well as gaps.",
     "editPermission": {
         "type": "private"
@@ -25,9 +28,18 @@
         }
     ],
     "homepage": "https://github.com/seqan/seqan/tree/master/apps/razers3",
-    "lastUpdate": "2022-12-09T21:27:29.443267Z",
+    "lastUpdate": "2023-01-13T02:29:11.821835Z",
+    "license": "GPL-3.0",
     "name": "razers3",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "leipzig",
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "Genomics",
diff --git a/data/rbdtector/rbdtector.biotools.json b/data/rbdtector/rbdtector.biotools.json
new file mode 100644
index 0000000000000..a699ef9ecdc29
--- /dev/null
+++ b/data/rbdtector/rbdtector.biotools.json
@@ -0,0 +1,139 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T22:40:09.904529Z",
+    "biotoolsCURIE": "biotools:rbdtector",
+    "biotoolsID": "rbdtector",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "m.sommerauer@fz-juelich.de",
+            "name": "Michael Sommerauer",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Annika Röthenbacher"
+        },
+        {
+            "name": "Martin Schober"
+        },
+        {
+            "name": "Matteo Cesari"
+        }
+    ],
+    "description": "An open-source software to detect REM sleep without atonia according to visual scoring criteria.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/aroethen/RBDtector",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T22:40:09.907225Z",
+    "license": "MIT",
+    "name": "RBDtector",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41598-022-25163-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).REM sleep without atonia (RSWA) is a key feature for the diagnosis of rapid eye movement (REM) sleep behaviour disorder (RBD). We introduce RBDtector, a novel open-source software to score RSWA according to established SINBAR visual scoring criteria. We assessed muscle activity of the mentalis, flexor digitorum superficialis (FDS), and anterior tibialis (AT) muscles. RSWA was scored manually as tonic, phasic, and any activity by human scorers as well as using RBDtector in 20 subjects. Subsequently, 174 subjects (72 without RBD and 102 with RBD) were analysed with RBDtector to show the algorithm’s applicability. We additionally compared RBDtector estimates to a previously published dataset. RBDtector showed robust conformity with human scorings. The highest congruency was achieved for phasic and any activity of the FDS. Combining mentalis any and FDS any, RBDtector identified RBD subjects with 100% specificity and 96% sensitivity applying a cut-off of 20.6%. Comparable performance was obtained without manual artefact removal. RBD subjects also showed muscle bouts of higher amplitude and longer duration. RBDtector provides estimates of tonic, phasic, and any activity comparable to human scorings. RBDtector, which is freely available, can help identify RBD subjects and provides reliable RSWA metrics.",
+                "authors": [
+                    {
+                        "name": "Bialonski S."
+                    },
+                    {
+                        "name": "Borghammer P."
+                    },
+                    {
+                        "name": "Brune C."
+                    },
+                    {
+                        "name": "Cesari M."
+                    },
+                    {
+                        "name": "Doppler C.E.J."
+                    },
+                    {
+                        "name": "Fink G.R."
+                    },
+                    {
+                        "name": "Hogl B."
+                    },
+                    {
+                        "name": "Lindner M."
+                    },
+                    {
+                        "name": "Okkels N."
+                    },
+                    {
+                        "name": "Rothenbacher A."
+                    },
+                    {
+                        "name": "Schober M."
+                    },
+                    {
+                        "name": "Seger A."
+                    },
+                    {
+                        "name": "Sembowski N."
+                    },
+                    {
+                        "name": "Sommerauer M."
+                    },
+                    {
+                        "name": "Stefani A."
+                    },
+                    {
+                        "name": "Willemsen N."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "RBDtector: an open-source software to detect REM sleep without atonia according to visual scoring criteria"
+            },
+            "pmcid": "PMC9719467",
+            "pmid": "36463304"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Neurology",
+            "uri": "http://edamontology.org/topic_3334"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Psychiatry",
+            "uri": "http://edamontology.org/topic_3419"
+        }
+    ]
+}
diff --git a/data/rbp_image_database/rbp_image_database.biotools.json b/data/rbp_image_database/rbp_image_database.biotools.json
new file mode 100644
index 0000000000000..bf42a73a0c333
--- /dev/null
+++ b/data/rbp_image_database/rbp_image_database.biotools.json
@@ -0,0 +1,152 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T22:59:08.428600Z",
+    "biotoolsCURIE": "biotools:rbp_image_database",
+    "biotoolsID": "rbp_image_database",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "eric.lecuyer@ircm.qc.ca",
+            "name": "Eric Lécuyer",
+            "orcidid": "https://orcid.org/0000-0002-4383-342X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Louis Philip Benoit Bouvrette"
+        },
+        {
+            "name": "Xiaofeng Wang"
+        },
+        {
+            "name": "Gene W Yeo",
+            "orcidid": "https://orcid.org/0000-0002-0799-6037"
+        }
+    ],
+    "description": "A resource for the systematic characterization of the subcellular distribution properties of human RNA binding proteins.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Image annotation",
+                    "uri": "http://edamontology.org/operation_3553"
+                },
+                {
+                    "term": "RNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3901"
+                },
+                {
+                    "term": "Subcellular localisation prediction",
+                    "uri": "http://edamontology.org/operation_2489"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://rnabiology.ircm.qc.ca/RBPImage/",
+    "lastUpdate": "2023-01-30T22:59:08.431028Z",
+    "name": "RBP Image Database",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC971",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.RNA binding proteins (RBPs) are central regulators of gene expression implicated in all facets of RNA metabolism. As such, they play key roles in cellular physiology and disease etiology. Since different steps of post-transcriptional gene expression tend to occur in specific regions of the cell, including nuclear or cytoplasmic locations, defining the subcellular distribution properties of RBPs is an important step in assessing their potential functions. Here, we present the RBP Image Database, a resource that details the subcellular localization features of 301 RBPs in the human HepG2 and HeLa cell lines, based on the results of systematic immuno-fluorescence studies conducted using a highly validated collection of RBP antibodies and a panel of 12 markers for specific organelles and subcellular structures. The unique features of the RBP Image Database include: (i) hosting of comprehensive representative images for each RBP-marker pair, with ∼250,000 microscopy images; (ii) a manually curated controlled vocabulary of annotation terms detailing the localization features of each factor; and (iii) a user-friendly interface allowing the rapid querying of the data by target or annotation. The RBP Image Database is freely available at https://rnabiology.ircm.qc.ca/RBPImage/.",
+                "authors": [
+                    {
+                        "name": "Benoit Bouvrette L.P."
+                    },
+                    {
+                        "name": "Blue S.M."
+                    },
+                    {
+                        "name": "Boulais J."
+                    },
+                    {
+                        "name": "Burge C.B."
+                    },
+                    {
+                        "name": "Fu X.-D."
+                    },
+                    {
+                        "name": "Graveley B.R."
+                    },
+                    {
+                        "name": "Kong J."
+                    },
+                    {
+                        "name": "Lecuyer E."
+                    },
+                    {
+                        "name": "Olson S."
+                    },
+                    {
+                        "name": "Stanton R."
+                    },
+                    {
+                        "name": "Syed E.U."
+                    },
+                    {
+                        "name": "Van Nostrand E.L."
+                    },
+                    {
+                        "name": "Wang X."
+                    },
+                    {
+                        "name": "Wei X."
+                    },
+                    {
+                        "name": "Yee B."
+                    },
+                    {
+                        "name": "Yeo G.W."
+                    },
+                    {
+                        "name": "Zhan L."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "RBP Image Database: A resource for the systematic characterization of the subcellular distribution properties of human RNA binding proteins"
+            },
+            "pmcid": "PMC9825414",
+            "pmid": "36321651"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        }
+    ]
+}
diff --git a/data/rcsb_pdb/rcsb_pdb.biotools.json b/data/rcsb_pdb/rcsb_pdb.biotools.json
index a05bbe1ba9dae..c55e6bac62887 100644
--- a/data/rcsb_pdb/rcsb_pdb.biotools.json
+++ b/data/rcsb_pdb/rcsb_pdb.biotools.json
@@ -60,7 +60,7 @@
         }
     ],
     "homepage": "http://rcsb.org",
-    "lastUpdate": "2022-05-17T17:56:11.993245Z",
+    "lastUpdate": "2023-01-30T22:53:45.393584Z",
     "name": "RCSB Protein Data Bank",
     "operatingSystem": [
         "Linux",
@@ -96,12 +96,149 @@
                         "name": "Westbrook J.D."
                     }
                 ],
+                "citationCount": 10,
                 "date": "2022-03-01T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "RCSB Protein Data Bank: Improved annotation, search and visualization of membrane protein structures archived in the PDB"
             },
             "pmcid": "PMC8826025",
             "pmid": "34864908"
+        },
+        {
+            "doi": "10.1093/NAR/GKAC1077",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.",
+                "authors": [
+                    {
+                        "name": "Bhikadiya C."
+                    },
+                    {
+                        "name": "Bi C."
+                    },
+                    {
+                        "name": "Bittrich S."
+                    },
+                    {
+                        "name": "Burley S.K."
+                    },
+                    {
+                        "name": "Chao H."
+                    },
+                    {
+                        "name": "Chen L."
+                    },
+                    {
+                        "name": "Craig P.A."
+                    },
+                    {
+                        "name": "Crichlow G.V."
+                    },
+                    {
+                        "name": "Dalenberg K."
+                    },
+                    {
+                        "name": "Duarte J.M."
+                    },
+                    {
+                        "name": "Dutta S."
+                    },
+                    {
+                        "name": "Fayazi M."
+                    },
+                    {
+                        "name": "Feng Z."
+                    },
+                    {
+                        "name": "Flatt J.W."
+                    },
+                    {
+                        "name": "Ganesan S."
+                    },
+                    {
+                        "name": "Ghosh S."
+                    },
+                    {
+                        "name": "Goodsell D.S."
+                    },
+                    {
+                        "name": "Green R.K."
+                    },
+                    {
+                        "name": "Guranovic V."
+                    },
+                    {
+                        "name": "Henry J."
+                    },
+                    {
+                        "name": "Hudson B.P."
+                    },
+                    {
+                        "name": "Khokhriakov I."
+                    },
+                    {
+                        "name": "Lawson C.L."
+                    },
+                    {
+                        "name": "Liang Y."
+                    },
+                    {
+                        "name": "Lowe R."
+                    },
+                    {
+                        "name": "Peisach E."
+                    },
+                    {
+                        "name": "Persikova I."
+                    },
+                    {
+                        "name": "Piehl D.W."
+                    },
+                    {
+                        "name": "Rose Y."
+                    },
+                    {
+                        "name": "Sali A."
+                    },
+                    {
+                        "name": "Segura J."
+                    },
+                    {
+                        "name": "Sekharan M."
+                    },
+                    {
+                        "name": "Shao C."
+                    },
+                    {
+                        "name": "Vallat B."
+                    },
+                    {
+                        "name": "Voigt M."
+                    },
+                    {
+                        "name": "Webb B."
+                    },
+                    {
+                        "name": "Westbrook J.D."
+                    },
+                    {
+                        "name": "Whetstone S."
+                    },
+                    {
+                        "name": "Young J.Y."
+                    },
+                    {
+                        "name": "Zalevsky A."
+                    },
+                    {
+                        "name": "Zardecki C."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning"
+            },
+            "pmcid": "PMC9825554",
+            "pmid": "36420884"
         }
     ],
     "toolType": [
@@ -128,5 +265,6 @@
             "term": "Small molecules",
             "uri": "http://edamontology.org/topic_0154"
         }
-    ]
+    ],
+    "validated": 1
 }
diff --git a/data/rd-connect_platform/rd-connect_platform.biotools.json b/data/rd-connect_platform/rd-connect_platform.biotools.json
index 5ff25a9c61a34..2196e035cebd3 100644
--- a/data/rd-connect_platform/rd-connect_platform.biotools.json
+++ b/data/rd-connect_platform/rd-connect_platform.biotools.json
@@ -13,16 +13,388 @@
     "cost": "Free of charge (with restrictions)",
     "credit": [
         {
-            "email": "platform@rd-connect.eu",
+            "email": "carles.hernandez@cnag.crg.eu",
+            "name": "Carles Hernandez-Ferrer",
+            "orcidid": "https://orcid.org/0000-0002-8029-7160",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ],
+            "url": "http://www.carleshf.com"
+        },
+        {
+            "email": "alberto.corvo@cnag.crg.eu",
+            "name": "Alberto Corvó",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "email": "anastasios.papakonstantinou@cnag.crg.eu",
+            "name": "Anastasios Papakonstantinou",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "email": "carles.garcia@cnag.crg.eu",
+            "name": "Carles Garcia-Linares",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "email": "daniel.pico@cnag.crg.eu",
+            "name": "Daniel Picó-Amador",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "email": "davide.piscia@cnag.crg.eu",
+            "name": "Davide Piscia",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "email": "gemma.bullich@cnag.crg.eu",
+            "name": "Gemma Bullich",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "email": "hlochmuller@toh.ca",
+            "name": "Hanns Lochmüller",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "email": "ida.paramonov@cnag.crg.eu",
+            "name": "Ida Paramonov",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "email": "ivo.gut@cnag.crg.eu",
+            "name": "Ivo G. Gut",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "email": "jeanremi.trotta@cnag.crg.eu",
+            "name": "Jean-Rémi Trotta",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "email": "leslie.matalonga@cnag.crg.eu",
+            "name": "Leslie Matalonga",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "email": "marcos.fernandez@cnag.crg.eu",
+            "name": "Marcos Fernández-Callejo",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "email": "raul.tonda@cnag.crg.eu",
+            "name": "Raul Tonda",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "email": "sergi.beltran@cnag.crg.eu",
+            "name": "Sergi Beltran",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ]
+        },
+        {
+            "email": "steven.laurie@cnag.crg.eu",
+            "name": "Steven Laurie",
             "typeEntity": "Person",
             "typeRole": [
                 "Primary contact"
             ]
+        },
+        {
+            "name": "Angel Alonso"
+        },
+        {
+            "name": "Ana Rath",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Ana Töpf",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Andrés Cañada-Pallarés",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Christophe Béroud",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Cristina Luengo",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "name": "Damian Smedley",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "David Salgado",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Deborah Mascalzoni",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Dylan Spalding",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Estrella López-Martín",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Holm Graessner",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Inés Martínez",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "name": "Irina Zaharieva",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Joan Protasio",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "name": "Jordi Camps-Puchadas",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "name": "Jordi Rambla",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "José M. Fernández",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Julius O.B. Jacobsen",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Luana Licata",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Lucia Monaco",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Luis A. Pérez-Jurado",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Manuel Posada de la Paz",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Marcella Attimonelli",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Mats G. Hansson",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Mònica Bayés",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Contributor"
+            ]
+        },
+        {
+            "name": "Olaf Riess",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Orion J. Buske",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Pedro Lopes",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Peter A. C. 't Hoen",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Peter N. Robinson",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Rachel Thompson",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Rajaram Kaliyaperumal",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Rita Horvath",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Séverine Lair-Préterre",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
+        },
+        {
+            "name": "Virginie Bros-Facer",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Support"
+            ]
         }
     ],
     "description": "An online tool for diagnosis and gene discovery in rare disease research. The platform features allow identifying disease-causing mutations in rare disease patients and linking them with detailed clinical information.",
     "editPermission": {
         "authors": [
+            "chernan3",
             "lmatalonga",
             "slaurie",
             "zluca"
@@ -35,10 +407,6 @@
     "function": [
         {
             "operation": [
-                {
-                    "term": "Data retrieval",
-                    "uri": "http://edamontology.org/operation_2422"
-                },
                 {
                     "term": "Genetic variation analysis",
                     "uri": "http://edamontology.org/operation_3197"
@@ -46,12 +414,16 @@
                 {
                     "term": "Sequence analysis",
                     "uri": "http://edamontology.org/operation_2403"
+                },
+                {
+                    "term": "Sequence annotation",
+                    "uri": "http://edamontology.org/operation_0361"
                 }
             ]
         }
     ],
     "homepage": "https://platform.rd-connect.eu/",
-    "lastUpdate": "2022-06-23T13:33:18.825626Z",
+    "lastUpdate": "2023-02-06T15:00:40.563178Z",
     "maturity": "Emerging",
     "name": "RD-Connect Genome-Phenome Analysis Platform (GPAP)",
     "operatingSystem": [
@@ -61,6 +433,80 @@
     ],
     "owner": "lmatalonga",
     "publication": [
+        {
+            "doi": "10.1007/s11606-014-2908-8",
+            "metadata": {
+                "abstract": "Research into rare diseases is typically fragmented by data type and disease. Individual efforts often have poor interoperability and do not systematically connect data across clinical phenotype, genomic data, biomaterial availability, and research/trial data sets. Such data must be linked at both an individual-patient and whole-cohort level to enable researchers to gain a complete view of their disease and patient population of interest. Data access and authorization procedures are required to allow researchers in multiple institutions to securely compare results and gain new insights. Funded by the European Union's Seventh Framework Programme under the International Rare Diseases Research Consortium (IRDiRC), RD-Connect is a global infrastructure project initiated in November 2012 that links genomic data with registries, biobanks, and clinical bioinformatics tools to produce a central research resource for rare diseases. © 2014 Society of General Internal Medicine.",
+                "authors": [
+                    {
+                        "name": "Beroud C."
+                    },
+                    {
+                        "name": "Bushby K."
+                    },
+                    {
+                        "name": "Dawkins H."
+                    },
+                    {
+                        "name": "Ensini M."
+                    },
+                    {
+                        "name": "Gut I.G."
+                    },
+                    {
+                        "name": "Hansson M.G."
+                    },
+                    {
+                        "name": "Heslop E."
+                    },
+                    {
+                        "name": "Johnston L."
+                    },
+                    {
+                        "name": "Koubi D."
+                    },
+                    {
+                        "name": "Lochmuller H."
+                    },
+                    {
+                        "name": "Monaco L."
+                    },
+                    {
+                        "name": "Paschall J.E."
+                    },
+                    {
+                        "name": "Patrinos G.P."
+                    },
+                    {
+                        "name": "Posada M."
+                    },
+                    {
+                        "name": "Robinson P.N."
+                    },
+                    {
+                        "name": "T Hoen P.-B.A."
+                    },
+                    {
+                        "name": "Taruscio D."
+                    },
+                    {
+                        "name": "Thompson R."
+                    },
+                    {
+                        "name": "Zatloukal K."
+                    }
+                ],
+                "citationCount": 133,
+                "date": "2014-01-01T00:00:00Z",
+                "journal": "Journal of General Internal Medicine",
+                "title": "RD-connect: An integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research"
+            },
+            "pmcid": "PMC4124112",
+            "pmid": "25029978",
+            "type": [
+                "Other"
+            ]
+        },
         {
             "doi": "10.1002/humu.24353",
             "metadata": {
@@ -223,7 +669,7 @@
                         "name": "Zaharieva I."
                     }
                 ],
-                "citationCount": 1,
+                "citationCount": 2,
                 "date": "2022-06-01T00:00:00Z",
                 "journal": "Human Mutation",
                 "title": "The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases"
@@ -232,14 +678,6 @@
             "type": [
                 "Primary"
             ]
-        },
-        {
-            "doi": "10.1007/s11606-014-2908-8",
-            "pmcid": "PMC4124112",
-            "pmid": "25029978",
-            "type": [
-                "Other"
-            ]
         }
     ],
     "toolType": [
diff --git a/data/reciprocal_best_structure_hits/reciprocal_best_structure_hits.biotools.json b/data/reciprocal_best_structure_hits/reciprocal_best_structure_hits.biotools.json
new file mode 100644
index 0000000000000..ff6fb5b501422
--- /dev/null
+++ b/data/reciprocal_best_structure_hits/reciprocal_best_structure_hits.biotools.json
@@ -0,0 +1,92 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T22:42:00.160816Z",
+    "biotoolsCURIE": "biotools:reciprocal_best_structure_hits",
+    "biotoolsID": "reciprocal_best_structure_hits",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "vmonzon@ebi.ac.uk",
+            "name": "Vivian Monzon",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alex Bateman"
+        },
+        {
+            "name": "Typhaine Paysan-Lafosse"
+        },
+        {
+            "name": "Valerie Wood"
+        }
+    ],
+    "description": "Using AlphaFold models to discover distant homologues.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Protein function prediction",
+                    "uri": "http://edamontology.org/operation_1777"
+                },
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/VivianMonzon/Reciprocal_Best_Structure_Hits",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-30T22:42:00.164622Z",
+    "license": "GPL-3.0",
+    "name": "Reciprocal best structure hits",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOADV/VBAC072",
+            "pmcid": "PMC9666668",
+            "pmid": "36408459"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene and protein families",
+            "uri": "http://edamontology.org/topic_0623"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Protein disordered structure",
+            "uri": "http://edamontology.org/topic_3538"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        }
+    ]
+}
diff --git a/data/recsai/recsai.biotools.json b/data/recsai/recsai.biotools.json
new file mode 100644
index 0000000000000..6cf55ead66256
--- /dev/null
+++ b/data/recsai/recsai.biotools.json
@@ -0,0 +1,101 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T22:34:18.581467Z",
+    "biotoolsCURIE": "biotools:recsai",
+    "biotoolsID": "recsai",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Philip.Kollmannsberger@uni-wuerzburg.de",
+            "name": "Philip Kollmannsberger",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Dominic A. Helmerich"
+        },
+        {
+            "name": "Dominik Boras"
+        },
+        {
+            "name": "Markus Sauer"
+        },
+        {
+            "name": "Sebastian Reinhard"
+        }
+    ],
+    "description": "Recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/super-resolution/ReCSAI",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T22:34:18.583888Z",
+    "license": "MIT",
+    "name": "ReCSAI",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05071-5",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method. Results: Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality. Conclusions: Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.",
+                "authors": [
+                    {
+                        "name": "Boras D."
+                    },
+                    {
+                        "name": "Helmerich D.A."
+                    },
+                    {
+                        "name": "Kollmannsberger P."
+                    },
+                    {
+                        "name": "Reinhard S."
+                    },
+                    {
+                        "name": "Sauer M."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy"
+            },
+            "pmcid": "PMC9732995",
+            "pmid": "36482307"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/redash/redash.biotools.json b/data/redash/redash.biotools.json
new file mode 100644
index 0000000000000..ce6af061e21d0
--- /dev/null
+++ b/data/redash/redash.biotools.json
@@ -0,0 +1,35 @@
+{
+    "additionDate": "2023-01-27T12:45:51.303827Z",
+    "biotoolsCURIE": "biotools:redash",
+    "biotoolsID": "redash",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "description": "Redash helps you make sense of your data\nConnect and query your data sources, build dashboards to visualize data and share them with your company.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://redash.io/",
+    "lastUpdate": "2023-02-01T12:39:11.340435Z",
+    "license": "BSD-3-Clause",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://redash.io/help/open-source/setup"
+        }
+    ],
+    "name": "Redash",
+    "owner": "iacs-biocomputacion"
+}
diff --git a/data/reddb/reddb.biotools.json b/data/reddb/reddb.biotools.json
new file mode 100644
index 0000000000000..ca60cabefb21c
--- /dev/null
+++ b/data/reddb/reddb.biotools.json
@@ -0,0 +1,131 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T22:35:51.518147Z",
+    "biotoolsCURIE": "biotools:reddb",
+    "biotoolsID": "reddb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "s.er@differ.nl",
+            "name": "Süleyman Er",
+            "orcidid": "https://orcid.org/0000-0002-5005-3894",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Abhishek Khetan"
+        },
+        {
+            "name": "Elif Sorkun"
+        },
+        {
+            "name": "Qi Zhang"
+        },
+        {
+            "name": "Murat Cihan Sorkun",
+            "orcidid": "http://orcid.org/0000-0002-5531-0802"
+        }
+    ],
+    "description": "Computational database of electroactive molecules for aqueous redox flow batteries.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.amdlab.nl/reddb",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-30T22:35:51.520993Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/ergroup/RedDB"
+        }
+    ],
+    "name": "RedDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41597-022-01832-2",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).An increasing number of electroactive compounds have recently been explored for their use in high-performance redox flow batteries for grid-scale energy storage. Given the vast and highly diverse chemical space of the candidate compounds, it is alluring to access their physicochemical properties in a speedy way. High-throughput virtual screening approaches, which use powerful combinatorial techniques for systematic enumerations of large virtual chemical libraries and respective property evaluations, are indispensable tools for an agile exploration of the designated chemical space. Herein, RedDB: a computational database that contains 31,618 molecules from two prominent classes of organic electroactive compounds, quinones and aza-aromatics, has been presented. RedDB incorporates miscellaneous physicochemical property information of the compounds that can potentially be employed as battery performance descriptors. RedDB’s development steps, including: (i) chemical library generation, (ii) molecular property prediction based on quantum chemical calculations, (iii) aqueous solubility prediction using machine learning, and (iv) data processing and database creation, have been described.",
+                "authors": [
+                    {
+                        "name": "Er S."
+                    },
+                    {
+                        "name": "Khetan A."
+                    },
+                    {
+                        "name": "Sorkun E."
+                    },
+                    {
+                        "name": "Sorkun M.C."
+                    },
+                    {
+                        "name": "Zhang Q."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Data",
+                "title": "RedDB, a computational database of electroactive molecules for aqueous redox flow batteries"
+            },
+            "pmcid": "PMC9705518",
+            "pmid": "36443329"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Cheminformatics",
+            "uri": "http://edamontology.org/topic_2258"
+        },
+        {
+            "term": "Chemistry",
+            "uri": "http://edamontology.org/topic_3314"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/redundans/redundans.biotools.json b/data/redundans/redundans.biotools.json
new file mode 100644
index 0000000000000..c0fce6db1b357
--- /dev/null
+++ b/data/redundans/redundans.biotools.json
@@ -0,0 +1,349 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-07T11:33:59.368666Z",
+    "biotoolsCURIE": "biotools:redundans",
+    "biotoolsID": "redundans",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "cost": "Free of charge",
+    "description": "Redundans is a pipeline that assists the assembly of heterozygous genomes. It takes as input assembled contigs, sequencing libraries and/or reference sequence and returns scaffolded homozygous genome assembly. Final assembly should be less fragmented and with total size smaller than the input contigs. In addition, Redundans will automatically close the gaps resulting from genome assembly or scaffolding.",
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://github.com/Gabaldonlab/redundans/tags"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "elixirCommunity": [
+        "Marine Metagenomics",
+        "Microbial Biotechnology",
+        "Plant Sciences"
+    ],
+    "elixirNode": [
+        "Spain"
+    ],
+    "elixirPlatform": [
+        "Tools"
+    ],
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence assembly",
+                        "uri": "http://edamontology.org/data_0925"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "note": "Return assembly stats: N50, L50, N's, # contigs, % heterozygous contigs...",
+            "operation": [
+                {
+                    "term": "Sequence assembly validation",
+                    "uri": "http://edamontology.org/operation_3180"
+                }
+            ]
+        },
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence assembly",
+                        "uri": "http://edamontology.org/data_0925"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "note": "Perform Gap closing using GapCloser",
+            "operation": [
+                {
+                    "term": "Scaffold gap completion",
+                    "uri": "http://edamontology.org/operation_3217"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Sequence assembly",
+                        "uri": "http://edamontology.org/data_0925"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        },
+                        {
+                            "term": "GFA 2",
+                            "uri": "http://edamontology.org/format_3976"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence assembly",
+                        "uri": "http://edamontology.org/data_0925"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "note": "Perform a reduction step using LASTal or Minimap2 to detect and remove the heterozygous contigs. Use SSPACE for the scaffolding of short reads",
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Sequence assembly",
+                        "uri": "http://edamontology.org/data_0925"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        },
+                        {
+                            "term": "GFA 2",
+                            "uri": "http://edamontology.org/format_3976"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence assembly",
+                        "uri": "http://edamontology.org/data_0925"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "note": "Scaffolding based on a reference genome (LAST or Minimap2 ) or long reads (custom code or by generating a Miniasm assembly of the long reads and using reference-based scaffolding).",
+            "operation": [
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Sequence assembly",
+                        "uri": "http://edamontology.org/data_0925"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        },
+                        {
+                            "term": "GFA 2",
+                            "uri": "http://edamontology.org/format_3976"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence assembly",
+                        "uri": "http://edamontology.org/data_0925"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "note": "Use Meryl and Merqury to plot k-mer composition and analysis of the assembly",
+            "operation": [
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Plot",
+                        "uri": "http://edamontology.org/data_2884"
+                    }
+                }
+            ]
+        },
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Sequence trace",
+                        "uri": "http://edamontology.org/data_0924"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTQ",
+                            "uri": "http://edamontology.org/format_1930"
+                        }
+                    ]
+                }
+            ],
+            "note": "Optionally run Platanus to generate an assembly out of your raw short reads",
+            "operation": [
+                {
+                    "term": "Genome assembly",
+                    "uri": "http://edamontology.org/operation_0525"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Sequence assembly",
+                        "uri": "http://edamontology.org/data_0925"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Gabaldonlab/redundans",
+    "language": [
+        "Perl",
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-07T12:09:05.125495Z",
+    "license": "Other",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/Gabaldonlab/redundans"
+        }
+    ],
+    "maturity": "Mature",
+    "name": "Redundans",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "Gabaldonlab",
+    "publication": [
+        {
+            "doi": "10.1093/nar/gkw294",
+            "metadata": {
+                "abstract": "© 2016 The Author(s).Many genomes display high levels of heterozygosity (i.e. presence of different alleles at the same loci in homologous chromosomes), being those of hybrid organisms an extreme such case. The assembly of highly heterozygous genomes from short sequencing reads is a challenging task because it is difficult to accurately recover the different haplotypes. When confronted with highly heterozygous genomes, the standard assembly process tends to collapse homozygous regions and reports heterozygous regions in alternative contigs. The boundaries between homozygous and heterozygous regions result in multiple assembly paths that are hard to resolve, which leads to highly fragmented assemblies with a total size larger than expected. This, in turn, causes numerous problems in downstream analyses such as fragmented gene models, wrong gene copy number, or broken synteny. To circumvent these caveats we have developed a pipeline that specifically deals with the assembly of heterozygous genomes by introducing a step to recognise and selectively remove alternative heterozygous contigs. We tested our pipeline on simulated and naturally-occurring heterozygous genomes and compared its accuracy to other existing tools. Our method is freely available at https://github.com/Gabaldonlab/redundans.",
+                "authors": [
+                    {
+                        "name": "Gabaldon T."
+                    },
+                    {
+                        "name": "Pryszcz L.P."
+                    }
+                ],
+                "citationCount": 231,
+                "date": "2016-07-08T00:00:00Z",
+                "journal": "Nucleic Acids Research",
+                "title": "Redundans: An assembly pipeline for highly heterozygous genomes"
+            },
+            "pmcid": "PMC4937319",
+            "pmid": "27131372",
+            "type": [
+                "Method"
+            ]
+        }
+    ],
+    "relation": [
+        {
+            "biotoolsID": "bwa",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "gfastats",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "merqury",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "meryl",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "miniasm",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "minimap2",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "snap-align",
+            "type": "uses"
+        },
+        {
+            "biotoolsID": "sspace",
+            "type": "uses"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Comparative genomics",
+            "uri": "http://edamontology.org/topic_0797"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Sequence composition, complexity and repeats",
+            "uri": "http://edamontology.org/topic_0157"
+        }
+    ],
+    "version": [
+        "1.01"
+    ]
+}
diff --git a/data/refhic/refhic.biotools.json b/data/refhic/refhic.biotools.json
new file mode 100644
index 0000000000000..23a273983745a
--- /dev/null
+++ b/data/refhic/refhic.biotools.json
@@ -0,0 +1,92 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T21:47:32.263801Z",
+    "biotoolsCURIE": "biotools:refhic",
+    "biotoolsID": "refhic",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "blanchem@cs.mcgill.ca",
+            "name": "Mathieu Blanchette",
+            "orcidid": "https://orcid.org/0000-0002-9555-860X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Yanlin Zhang"
+        }
+    ],
+    "description": "Reference panel guided topological structure annotation of Hi-C data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Loop modelling",
+                    "uri": "http://edamontology.org/operation_0481"
+                },
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/BlanchetteLab/RefHiC",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T21:47:32.266352Z",
+    "license": "MIT",
+    "name": "RefHiC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41467-022-35231-3",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Accurately annotating topological structures (e.g., loops and topologically associating domains) from Hi-C data is critical for understanding the role of 3D genome organization in gene regulation. This is a challenging task, especially at high resolution, in part due to the limited sequencing coverage of Hi-C data. Current approaches focus on the analysis of individual Hi-C data sets of interest, without taking advantage of the facts that (i) several hundred Hi-C contact maps are publicly available, and (ii) the vast majority of topological structures are conserved across multiple cell types. Here, we present RefHiC, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate topological structure annotation from a given study sample. We compare RefHiC against tools that do not use reference samples and find that RefHiC outperforms other programs at both topological associating domain and loop annotation across different cell types, species, and sequencing depths.",
+                "authors": [
+                    {
+                        "name": "Blanchette M."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "Reference panel guided topological structure annotation of Hi-C data"
+            },
+            "pmcid": "PMC9718747",
+            "pmid": "36460680"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Informatics",
+            "uri": "http://edamontology.org/topic_0605"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/refinem/refinem.biotools.json b/data/refinem/refinem.biotools.json
index 586ca94990f90..629d75b4a248d 100644
--- a/data/refinem/refinem.biotools.json
+++ b/data/refinem/refinem.biotools.json
@@ -1,7 +1,10 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2021-05-27T09:07:28Z",
     "biotoolsCURIE": "biotools:refinem",
     "biotoolsID": "refinem",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "RefineM is a set of tools for improving population genomes. It provides methods designed to improve the completeness of a genome along with methods for identifying and removing contamination.",
     "editPermission": {
         "authors": [
@@ -13,10 +16,15 @@
     "language": [
         "Python"
     ],
-    "lastUpdate": "2022-12-09T21:29:48.668121Z",
+    "lastUpdate": "2023-01-13T02:24:47.330904Z",
     "license": "GPL-3.0",
     "maturity": "Legacy",
     "name": "RefineM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "Kigaard",
     "toolType": [
         "Command-line tool"
diff --git a/data/reframed/reframed.biotools.json b/data/reframed/reframed.biotools.json
index 5c231b2536825..28b2e7029f7a4 100644
--- a/data/reframed/reframed.biotools.json
+++ b/data/reframed/reframed.biotools.json
@@ -6,20 +6,16 @@
     "collectionID": [
         "PerMedCoE"
     ],
+    "confidence_flag": "tool",
     "cost": "Free of charge",
-    "description": "ReFramed implements many constraint-based simulation methods (see list below), and contains interfaces to other libraries of the COBRA ecosystem including escher, cobrapy, and optlang.",
+    "description": "ReFramed is a Python 3 library for metabolic model simulation.\n\nIt currently supports 15 different constraint-based simulation methods (FBA variants), including knockout simulation, thermodynamic analysis, protein allocation, transcriptomics integration, and community simulation.",
     "documentation": [
         {
             "type": [
-                "General"
+                "General",
+                "Installation instructions"
             ],
-            "url": "https://github.com/cdanielmachado/reframed"
-        }
-    ],
-    "download": [
-        {
-            "type": "Source code",
-            "url": "https://github.com/cdanielmachado/reframed"
+            "url": "https://reframed.readthedocs.io/en/latest/"
         }
     ],
     "editPermission": {
@@ -29,8 +25,16 @@
     "language": [
         "Python"
     ],
-    "lastUpdate": "2022-04-28T15:39:08.442209Z",
+    "lastUpdate": "2023-01-13T02:20:38.809559Z",
     "license": "Apache-2.0",
     "name": "reFramed",
-    "owner": "tntiniak"
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "tntiniak",
+    "toolType": [
+        "Library"
+    ]
 }
diff --git a/data/regtools/regtools.biotools.json b/data/regtools/regtools.biotools.json
new file mode 100644
index 0000000000000..49a3cb965e9cb
--- /dev/null
+++ b/data/regtools/regtools.biotools.json
@@ -0,0 +1,46 @@
+{
+    "additionDate": "2023-01-24T13:41:32.753177Z",
+    "biotoolsCURIE": "biotools:regtools",
+    "biotoolsID": "regtools",
+    "description": "Integrate DNA-seq and RNA-seq data to identify mutations that are associated with regulatory effects on gene expression.",
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                },
+                {
+                    "term": "Splicing analysis",
+                    "uri": "http://edamontology.org/operation_2499"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://regtools.readthedocs.io/en/latest/",
+    "language": [
+        "C",
+        "C++"
+    ],
+    "lastUpdate": "2023-01-24T13:41:38.999372Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/griffithlab/regtools"
+        }
+    ],
+    "name": "regtools",
+    "owner": "alexcorm",
+    "topic": [
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/remm_score/remm_score.biotools.json b/data/remm_score/remm_score.biotools.json
new file mode 100644
index 0000000000000..873351d418dbe
--- /dev/null
+++ b/data/remm_score/remm_score.biotools.json
@@ -0,0 +1,159 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-03T14:28:36.818779Z",
+    "biotoolsCURIE": "biotools:remm_score",
+    "biotoolsID": "remm_score",
+    "collectionID": [
+        "Rare Disease"
+    ],
+    "credit": [
+        {
+            "email": "lusine.nazaretyan@bih-charite.de",
+            "name": "Lusiné Nazaretyan",
+            "orcidid": "https://orcid.org/0000-0001-5820-4143"
+        },
+        {
+            "email": "martin.kircher@bih-charite.de",
+            "name": "Martin Kircher",
+            "orcidid": "https://orcid.org/0000-0001-9278-5471"
+        },
+        {
+            "email": "max.schubach@bih-charite.de",
+            "name": "Max Schubach",
+            "orcidid": "https://orcid.org/0000-0002-2032-6679"
+        }
+    ],
+    "description": "ReMM score is a tool that scores the positions in the human genome in terms of their regulatory probability.\n\nWe use curated regulatory variants involved in Mendelian disease and contrast them with proxy-neutral variants that survived natural selection in a machine learning framework. We use an algorithm for highly imbalanced data, called hyperSMURF, to differentiate deleterious from neutral variants.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://remm.bihealth.org/"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://doi.org/10.5281/zenodo.6576087",
+            "version": "v0.4"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://remm.bihealth.org/",
+    "lastUpdate": "2023-01-03T14:28:36.822941Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/kircherlab/ReMM"
+        }
+    ],
+    "name": "ReMM score",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "visze",
+    "publication": [
+        {
+            "doi": "10.1101/2022.03.14.484240"
+        },
+        {
+            "doi": "10.1016/j.ajhg.2016.07.005",
+            "metadata": {
+                "abstract": "© 2016 American Society of Human GeneticsThe interpretation of non-coding variants still constitutes a major challenge in the application of whole-genome sequencing in Mendelian disease, especially for single-nucleotide and other small non-coding variants. Here we present Genomiser, an analysis framework that is able not only to score the relevance of variation in the non-coding genome, but also to associate regulatory variants to specific Mendelian diseases. Genomiser scores variants through either existing methods such as CADD or a bespoke machine learning method and combines these with allele frequency, regulatory sequences, chromosomal topological domains, and phenotypic relevance to discover variants associated to specific Mendelian disorders. Overall, Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes, allowing effective detection and discovery of regulatory variants in Mendelian disease.",
+                "authors": [
+                    {
+                        "name": "Groza T."
+                    },
+                    {
+                        "name": "Haendel M.A."
+                    },
+                    {
+                        "name": "Hochheiser H."
+                    },
+                    {
+                        "name": "Jacobsen J.O.B."
+                    },
+                    {
+                        "name": "Jager M."
+                    },
+                    {
+                        "name": "Kohler S."
+                    },
+                    {
+                        "name": "Lewis S.E."
+                    },
+                    {
+                        "name": "McMurry J.A."
+                    },
+                    {
+                        "name": "Mungall C.J."
+                    },
+                    {
+                        "name": "Robinson P.N."
+                    },
+                    {
+                        "name": "Schubach M."
+                    },
+                    {
+                        "name": "Smedley D."
+                    },
+                    {
+                        "name": "Spielmann M."
+                    },
+                    {
+                        "name": "Valentini G."
+                    },
+                    {
+                        "name": "Washington N.L."
+                    },
+                    {
+                        "name": "Zemojtel T."
+                    }
+                ],
+                "citationCount": 133,
+                "date": "2016-09-01T00:00:00Z",
+                "journal": "American Journal of Human Genetics",
+                "title": "A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease"
+            },
+            "pmcid": "PMC5011059",
+            "pmid": "27569544",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "toolType": [
+        "Bioinformatics portal",
+        "Web API",
+        "Web application",
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Functional genomics",
+            "uri": "http://edamontology.org/topic_0085"
+        },
+        {
+            "term": "Human genetics",
+            "uri": "http://edamontology.org/topic_3574"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ],
+    "version": [
+        "v0.3.1.post1",
+        "v0.4"
+    ]
+}
diff --git a/data/remode/remode.biotools.json b/data/remode/remode.biotools.json
new file mode 100644
index 0000000000000..696cbdf9e05ca
--- /dev/null
+++ b/data/remode/remode.biotools.json
@@ -0,0 +1,144 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T21:43:26.319592Z",
+    "biotoolsCURIE": "biotools:remode",
+    "biotoolsID": "remode",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hlli@ecust.edu.cn",
+            "name": "Honglin Li",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "kimhsieh@tencent.com",
+            "name": "Chang-Yu Hsieh",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "tingjunhou@zju.edu.cn",
+            "name": "Tingjun Hou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Mingyang Wang"
+        }
+    ],
+    "description": "A deep learning-based web server for target-specific drug design.",
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://github.com/micahwang/ReMode/tree/master"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular docking",
+                    "uri": "http://edamontology.org/operation_0478"
+                },
+                {
+                    "term": "Protein design",
+                    "uri": "http://edamontology.org/operation_4008"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://cadd.zju.edu.cn/relation/remode/",
+    "lastUpdate": "2023-02-13T21:43:26.322217Z",
+    "name": "ReMODE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13321-022-00665-W",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior developments in de novo drug design. However, data availability, deep data processing, and the lack of user-friendly DL tools and interfaces make it difficult to apply these DL techniques to drug design. We hereby present ReMODE (Receptor-based MOlecular DEsign), a new web server based on DL algorithm for target-specific ligand design, which integrates different functional modules to enable users to develop customizable drug design tasks. As designed, the ReMODE sever can construct the target-specific tasks toward the protein targets selected by users. Meanwhile, the server also provides some extensions: users can optimize the drug-likeness or synthetic accessibility of the generated molecules, and control other physicochemical properties; users can also choose a sub-structure/scaffold as a starting point for fragment-based drug design. The ReMODE server also enables users to optimize the pharmacophore matching and docking conformations of the generated molecules. We believe that the ReMODE server will benefit researchers for drug discovery. ReMODE is publicly available at http://cadd.zju.edu.cn/relation/remode/. Graphical Abstract: [Figure not available: see fulltext.]",
+                "authors": [
+                    {
+                        "name": "Deng Y."
+                    },
+                    {
+                        "name": "Hou T."
+                    },
+                    {
+                        "name": "Hsieh C.-Y."
+                    },
+                    {
+                        "name": "Kang Y."
+                    },
+                    {
+                        "name": "Li D."
+                    },
+                    {
+                        "name": "Li H."
+                    },
+                    {
+                        "name": "Pan P."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Wang M."
+                    },
+                    {
+                        "name": "Weng G."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Journal of Cheminformatics",
+                "title": "ReMODE: a deep learning-based web server for target-specific drug design"
+            },
+            "pmcid": "PMC9743675",
+            "pmid": "36510307"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Compound libraries and screening",
+            "uri": "http://edamontology.org/topic_3343"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/repac/repac.biotools.json b/data/repac/repac.biotools.json
new file mode 100644
index 0000000000000..cc31758231976
--- /dev/null
+++ b/data/repac/repac.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T01:14:35.245644Z",
+    "biotoolsCURIE": "biotools:repac",
+    "biotoolsID": "repac",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "eimada@med.cornell.edu",
+            "name": "Eddie L. Imada",
+            "orcidid": "https://orcid.org/0000-0001-9527-3703",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "marchion@med.cornell.edu",
+            "name": "Luigi Marchionni",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ben Langmead"
+        },
+        {
+            "name": "Christopher Wilks"
+        }
+    ],
+    "description": "Analysis of alternative polyadenylation from RNA-sequencing data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Enrichment analysis",
+                    "uri": "http://edamontology.org/operation_3501"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "PolyA signal detection",
+                    "uri": "http://edamontology.org/operation_0428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/eddieimada/REPAC",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-18T01:14:35.249919Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/eddieimada/REPAC_paper"
+        }
+    ],
+    "name": "REPAC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13059-023-02865-5",
+            "metadata": {
+                "abstract": "Alternative polyadenylation (APA) is an important post-transcriptional mechanism that has major implications in biological processes and diseases. Although specialized sequencing methods for polyadenylation exist, availability of these data are limited compared to RNA-sequencing data. We developed REPAC, a framework for the analysis of APA from RNA-sequencing data. Using REPAC, we investigate the landscape of APA caused by activation of B cells. We also show that REPAC is faster than alternative methods by at least 7-fold and that it scales well to hundreds of samples. Overall, the REPAC method offers an accurate, easy, and convenient solution for the exploration of APA.",
+                "authors": [
+                    {
+                        "name": "Imada E.L."
+                    },
+                    {
+                        "name": "Langmead B."
+                    },
+                    {
+                        "name": "Marchionni L."
+                    },
+                    {
+                        "name": "Wilks C."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Genome Biology",
+                "title": "REPAC: analysis of alternative polyadenylation from RNA-sequencing data"
+            },
+            "pmcid": "PMC9912678",
+            "pmid": "36759904"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/repair/repair.biotools.json b/data/repair/repair.biotools.json
index 2c96a1a6f3f27..d202c5c256ac1 100644
--- a/data/repair/repair.biotools.json
+++ b/data/repair/repair.biotools.json
@@ -3,6 +3,7 @@
     "additionDate": "2021-03-19T13:56:30Z",
     "biotoolsCURIE": "biotools:repair",
     "biotoolsID": "repair",
+    "confidence_flag": "tool",
     "cost": "Free of charge",
     "credit": [
         {
@@ -37,7 +38,7 @@
         "type": "private"
     },
     "homepage": "https://utrecht-university.shinyapps.io/repair/",
-    "lastUpdate": "2022-12-09T21:36:08.890945Z",
+    "lastUpdate": "2023-01-13T02:13:28.728709Z",
     "license": "CC-BY-4.0",
     "link": [
         {
@@ -49,6 +50,11 @@
     ],
     "maturity": "Emerging",
     "name": "RePAIR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "valeriabonapersona",
     "publication": [
         {
@@ -117,7 +123,7 @@
                         "name": "Yam K.Y."
                     }
                 ],
-                "citationCount": 14,
+                "citationCount": 15,
                 "date": "2021-04-01T00:00:00Z",
                 "journal": "Nature Neuroscience",
                 "title": "Increasing the statistical power of animal experiments with historical control data"
diff --git a/data/repeatsdb/repeatsdb.biotools.json b/data/repeatsdb/repeatsdb.biotools.json
index d132834e8f426..27d8ba34fd651 100644
--- a/data/repeatsdb/repeatsdb.biotools.json
+++ b/data/repeatsdb/repeatsdb.biotools.json
@@ -120,8 +120,8 @@
             ]
         }
     ],
-    "homepage": "http://repeatsdb.bio.unipd.it",
-    "lastUpdate": "2019-01-13T18:10:16Z",
+    "homepage": "https://repeatsdb.org/",
+    "lastUpdate": "2023-02-27T13:46:42.130813Z",
     "maturity": "Mature",
     "name": "RepeatsDB",
     "operatingSystem": [
@@ -183,6 +183,6 @@
     ],
     "validated": 1,
     "version": [
-        "2.0"
+        "3"
     ]
 }
diff --git a/data/revana/revana.biotools.json b/data/revana/revana.biotools.json
new file mode 100644
index 0000000000000..bad5b40cdde18
--- /dev/null
+++ b/data/revana/revana.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T20:42:01.895835Z",
+    "biotoolsCURIE": "biotools:revana",
+    "biotoolsID": "revana",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "elias.ulrich@kitz-heidelberg.de",
+            "name": "Elias Ulrich",
+            "orcidid": "https://orcid.org/0000-0003-2489-7265",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Natalie Jäger"
+        },
+        {
+            "name": "Stefan M Pfister"
+        }
+    ],
+    "description": "A comprehensive tool for regulatory variant analysis and visualization of cancer genomes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Genetic variation analysis",
+                    "uri": "http://edamontology.org/operation_3197"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/KiTZ-Heidelberg/revana",
+    "language": [
+        "JavaScript",
+        "R"
+    ],
+    "lastUpdate": "2023-02-13T20:42:51.003351Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/KiTZ-Heidelberg/revana-demo-data"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/KiTZ-Heidelberg/revana-demo-report"
+        }
+    ],
+    "name": "Revana",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC831",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: As non-coding driver mutations move more into the focus of cancer research, a comprehensive and easy-to-use software solution for regulatory variant analysis and data visualization is highly relevant. The interpretation of regulatory variants in large tumor genome cohorts requires specialized analysis and visualization of multiple layers of data, including for example breakpoints of structural variants, enhancer elements and additional available gene locus annotation, in the context of changes in gene expression. RESULTS: We introduce a user-friendly tool, Revana (REgulatory Variant ANAlysis), that can aggregate and visually represent regulatory variants from cancer genomes in a gene-centric manner. It requires whole-genome and RNA sequencing data of a cohort of tumor samples and creates interactive HTML reports summarizing the most important regulatory events. AVAILABILITY AND IMPLEMENTATION: Revana is implemented in R and JavaScript. It is available for download as an R package under <https://github.com/KiTZ-Heidelberg/revana>. Sample results can be viewed under <https://github.com/KiTZ-Heidelberg/revana-demo-report> and a short walkthrough is available under <https://github.com/KiTZ-Heidelberg/revana-demo-data>. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Jager N."
+                    },
+                    {
+                        "name": "Pfister S.M."
+                    },
+                    {
+                        "name": "Ulrich E."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Revana: a comprehensive tool for regulatory variant analysis and visualization of cancer genomes"
+            },
+            "pmcid": "PMC9822537",
+            "pmid": "36576005"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/rexdb/rexdb.biotools.json b/data/rexdb/rexdb.biotools.json
index 597a05800ba2a..fb131cb4ddcfa 100644
--- a/data/rexdb/rexdb.biotools.json
+++ b/data/rexdb/rexdb.biotools.json
@@ -11,7 +11,17 @@
     "cost": "Free of charge",
     "credit": [
         {
-            "name": "ELIXIR-CZ",
+            "name": "Nina Hoštáková"
+        },
+        {
+            "name": "Petr Novák"
+        },
+        {
+            "name": "Pavel Neumann",
+            "orcidid": "http://orcid.org/0000-0001-6711-6639"
+        },
+        {
+            "name": "Jiří Macas",
             "typeEntity": "Institute"
         }
     ],
@@ -42,7 +52,7 @@
         "Data"
     ],
     "homepage": "http://repeatexplorer.org/?page_id=918",
-    "lastUpdate": "2022-12-09T21:42:13.872439Z",
+    "lastUpdate": "2023-01-13T02:09:25.460825Z",
     "license": "Not licensed",
     "link": [
         {
@@ -54,6 +64,11 @@
     ],
     "maturity": "Emerging",
     "name": "REXdb",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "kavonrtep",
     "publication": [
         {
@@ -74,7 +89,7 @@
                         "name": "Novak P."
                     }
                 ],
-                "citationCount": 108,
+                "citationCount": 114,
                 "date": "2019-01-03T00:00:00Z",
                 "journal": "Mobile DNA",
                 "title": "Systematic survey of plant LTR-retrotransposons elucidates phylogenetic relationships of their polyprotein domains and provides a reference for element classification"
diff --git a/data/rexposome/rexposome.biotools.json b/data/rexposome/rexposome.biotools.json
index 205931b319699..efd7f2664f688 100644
--- a/data/rexposome/rexposome.biotools.json
+++ b/data/rexposome/rexposome.biotools.json
@@ -10,56 +10,89 @@
         {
             "email": "carles.hernandez@isglobal.org",
             "name": "Carles Hernandez-Ferrer",
+            "orcidid": "https://orcid.org/0000-0002-8029-7160",
             "typeEntity": "Person",
             "typeRole": [
-                "Primary contact"
+                "Developer"
+            ],
+            "url": "http://www.carleshf.com"
+        },
+        {
+            "email": "xavier.escriba@isglobal.org",
+            "name": "Xavier Escribà Montagut",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Maintainer"
             ]
+        },
+        {
+            "email": "juanr.gonzalez@isglobal.org",
+            "name": "Juan R Gonzalez",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ],
+            "url": "https://brge.isglobal.org/"
         }
     ],
     "description": "Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes.",
     "documentation": [
         {
             "type": [
-                "User manual"
+                "API documentation"
             ],
             "url": "http://bioconductor.org/packages/release/bioc/manuals/rexposome/man/rexposome.pdf"
+        },
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://isglobal-brge.github.io/rexposome"
         }
     ],
     "download": [
         {
             "type": "Software package",
-            "url": "http://bioconductor.org/packages/release/bioc/src/contrib/rexposome_1.2.0.tar.gz"
+            "url": "https://bioconductor.org/packages/release/bioc/src/contrib/rexposome_1.20.0.tar.gz",
+            "version": "1.20.0"
         }
     ],
     "editPermission": {
         "authors": [
             "brgelab",
+            "chernan3",
             "sergitobara"
         ],
         "type": "group"
     },
     "function": [
         {
+            "cmd": "imputation, pca, correlation, clustering, exwas, mexwas",
+            "note": "Impute missing data, perform PCA on the multidimensional space, compute correlations across the multiples variables, group samples according the variables, perform GWAS-like association studies against a significant phenotype.",
             "operation": [
                 {
-                    "term": "Classification",
-                    "uri": "http://edamontology.org/operation_2990"
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
                 }
             ]
         },
         {
+            "cmd": "plotHistogram, plotFamily, plotPCA, plotCorrelation, plotClassification, plotPHE, plotEXP, plotExwas, plotEffect",
+            "note": "Plots histograms of single or family of variables, plot results of applying a PCA to the set, plot the correlations computed across the variables, plot samples classification after performing a clustering based on the variables, plot the score of correlation between PCs and phenotypes of interest, plot the score of correlation between the PCs and the variables, plot the p-values of the GWAS-like analysis, plot the effects of association.",
             "operation": [
                 {
-                    "term": "Clustering",
-                    "uri": "http://edamontology.org/operation_3432"
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
                 }
             ]
         },
         {
+            "cmd": "readExposome, loadExposome",
+            "note": "Load both tsv-like files and data.frame-like R objects..",
             "operation": [
                 {
-                    "term": "Comparison",
-                    "uri": "http://edamontology.org/operation_2424"
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
                 }
             ]
         }
@@ -68,14 +101,14 @@
     "language": [
         "R"
     ],
-    "lastUpdate": "2022-10-19T07:22:44.373806Z",
+    "lastUpdate": "2023-02-07T14:26:54.550586Z",
     "license": "MIT",
     "link": [
         {
             "type": [
                 "Mirror"
             ],
-            "url": "http://bioconductor.org/packages/rexposome/"
+            "url": "https://bioconductor.org/packages/rexposome/"
         }
     ],
     "name": "rexposome",
@@ -86,6 +119,133 @@
     ],
     "owner": "shadi.m",
     "publication": [
+        {
+            "doi": "10.1038/s41467-022-34422-2",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Environmental exposures during early life play a critical role in life-course health, yet the molecular phenotypes underlying environmental effects on health are poorly understood. In the Human Early Life Exposome (HELIX) project, a multi-centre cohort of 1301 mother-child pairs, we associate individual exposomes consisting of >100 chemical, outdoor, social and lifestyle exposures assessed in pregnancy and childhood, with multi-omics profiles (methylome, transcriptome, proteins and metabolites) in childhood. We identify 1170 associations, 249 in pregnancy and 921 in childhood, which reveal potential biological responses and sources of exposure. Pregnancy exposures, including maternal smoking, cadmium and molybdenum, are predominantly associated with child DNA methylation changes. In contrast, childhood exposures are associated with features across all omics layers, most frequently the serum metabolome, revealing signatures for diet, toxic chemical compounds, essential trace elements, and weather conditions, among others. Our comprehensive and unique resource of all associations (https://helixomics.isglobal.org/) will serve to guide future investigation into the biological imprints of the early life exposome.",
+                "authors": [
+                    {
+                        "name": "Andrusaityte S."
+                    },
+                    {
+                        "name": "Borras E."
+                    },
+                    {
+                        "name": "Bustamante M."
+                    },
+                    {
+                        "name": "Cadiou S."
+                    },
+                    {
+                        "name": "Carracedo A."
+                    },
+                    {
+                        "name": "Casas M."
+                    },
+                    {
+                        "name": "Chatzi L."
+                    },
+                    {
+                        "name": "Coen M."
+                    },
+                    {
+                        "name": "Estivill X."
+                    },
+                    {
+                        "name": "Gonzalez J.R."
+                    },
+                    {
+                        "name": "Grazuleviciene R."
+                    },
+                    {
+                        "name": "Gutzkow K.B."
+                    },
+                    {
+                        "name": "Hernandez-Ferrer C."
+                    },
+                    {
+                        "name": "Heude B."
+                    },
+                    {
+                        "name": "Keun H.C."
+                    },
+                    {
+                        "name": "Lau C.-H.E."
+                    },
+                    {
+                        "name": "Maitre L."
+                    },
+                    {
+                        "name": "Mason D."
+                    },
+                    {
+                        "name": "Nieuwenhuijsen M."
+                    },
+                    {
+                        "name": "Papadopoulou E.Z."
+                    },
+                    {
+                        "name": "Pelegri-Siso D."
+                    },
+                    {
+                        "name": "Quintela I."
+                    },
+                    {
+                        "name": "Robinson O."
+                    },
+                    {
+                        "name": "Ruiz-Arenas C."
+                    },
+                    {
+                        "name": "Sabido E."
+                    },
+                    {
+                        "name": "Sakhi A.K."
+                    },
+                    {
+                        "name": "Siskos A.P."
+                    },
+                    {
+                        "name": "Slama R."
+                    },
+                    {
+                        "name": "Sunyer J."
+                    },
+                    {
+                        "name": "Tamayo I."
+                    },
+                    {
+                        "name": "Thiel D."
+                    },
+                    {
+                        "name": "Thomsen C."
+                    },
+                    {
+                        "name": "Urquiza J."
+                    },
+                    {
+                        "name": "Vafeiadi M."
+                    },
+                    {
+                        "name": "Vives-Usano M."
+                    },
+                    {
+                        "name": "Vrijheid M."
+                    },
+                    {
+                        "name": "Wright J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "Multi-omics signatures of the human early life exposome"
+            },
+            "pmcid": "PMC9678903",
+            "pmid": "36411288",
+            "type": [
+                "Usage"
+            ]
+        },
         {
             "doi": "10.1093/bioinformatics/btz526",
             "metadata": {
@@ -113,7 +273,7 @@
                         "name": "Wellenius G.A."
                     }
                 ],
-                "citationCount": 9,
+                "citationCount": 10,
                 "date": "2019-12-15T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "Comprehensive study of the exposome and omic data using rexposome Bioconductor Packages"
@@ -124,13 +284,27 @@
             ]
         }
     ],
+    "relation": [
+        {
+            "biotoolsID": "ctdquerier",
+            "type": "usedBy"
+        },
+        {
+            "biotoolsID": "multidataset",
+            "type": "usedBy"
+        },
+        {
+            "biotoolsID": "omicrexposome",
+            "type": "usedBy"
+        }
+    ],
     "toolType": [
         "Library"
     ],
     "topic": [
         {
-            "term": "Medical informatics",
-            "uri": "http://edamontology.org/topic_3063"
+            "term": "Biomedical science",
+            "uri": "http://edamontology.org/topic_3344"
         }
     ],
     "validated": 1,
diff --git a/data/rfsc/rfsc.biotools.json b/data/rfsc/rfsc.biotools.json
index 72da6d92a985a..27189d77bf09f 100644
--- a/data/rfsc/rfsc.biotools.json
+++ b/data/rfsc/rfsc.biotools.json
@@ -1,7 +1,10 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2021-09-23T18:41:28.258166Z",
     "biotoolsCURIE": "biotools:rfsc",
     "biotoolsID": "rfsc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "Reference-Free Sequence Classification Tool for DNA sequences in metagenomic samples.",
     "editPermission": {
         "type": "private"
@@ -31,9 +34,22 @@
         }
     ],
     "homepage": "https://github.com/cobilab/RFSC",
-    "lastUpdate": "2022-12-09T21:47:51.381518Z",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-13T02:01:51.664527Z",
+    "license": "GPL-3.0",
     "name": "RFSC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "Alexandre",
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "DNA",
diff --git a/data/rg4detector/rg4detector.biotools.json b/data/rg4detector/rg4detector.biotools.json
new file mode 100644
index 0000000000000..ed343db24c932
--- /dev/null
+++ b/data/rg4detector/rg4detector.biotools.json
@@ -0,0 +1,95 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T09:34:54.626317Z",
+    "biotoolsCURIE": "biotools:rg4detector",
+    "biotoolsID": "rg4detector",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Yaron.Orenstein@biu.ac.il",
+            "name": "Yaron Orenstein",
+            "orcidid": "https://orcid.org/0000-0002-3583-3112",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "Eran.Hornstein@weizmann.ac.il",
+            "name": "Eran Hornstein",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Maor Turner"
+        },
+        {
+            "name": "Yehuda M Danino",
+            "orcidid": "https://orcid.org/0000-0001-5295-0487"
+        }
+    ],
+    "description": "A convolutional neural network for predicting rG4 folding of any given sequence based on rG4-seq data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "RNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3901"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/OrensteinLab/rG4detector",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-30T09:34:54.628747Z",
+    "license": "Not licensed",
+    "name": "rG4detector",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC950",
+            "pmcid": "PMC9723610",
+            "pmid": "36350614"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/rgcn/rgcn.biotools.json b/data/rgcn/rgcn.biotools.json
new file mode 100644
index 0000000000000..caf84d172640f
--- /dev/null
+++ b/data/rgcn/rgcn.biotools.json
@@ -0,0 +1,99 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T09:29:42.677908Z",
+    "biotoolsCURIE": "biotools:rgcn",
+    "biotoolsID": "rgcn",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "t.singam@uva.nl",
+            "name": "Thiviyan Thanapalasingam",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Lucas van Berkel"
+        },
+        {
+            "name": "Paul Groth"
+        },
+        {
+            "name": "Peter Bloem"
+        }
+    ],
+    "description": "A PyTorch implementation of the Relational Graph Convolutional Network (RGCN).",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/thiviyanT/torch-rgcn",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-30T09:29:42.680348Z",
+    "license": "MIT",
+    "name": "RGCN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.7717/PEERJ-CS.1073",
+            "metadata": {
+                "abstract": "© Copyright 2022 Thanapalasingam et al.In this article, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model. Our reproduction results empirically validate the correctness of our implementations using benchmark Knowledge Graph datasets on node classification and link prediction tasks. Our explanation provides a friendly understanding of the different components of the RGCN for both users and researchers extending the RGCN approach. Furthermore, we introduce two new configurations of the RGCN that are more parameter efficient. The code and datasets are available at https://github.com/thiviyanT/torch-rgcn.",
+                "authors": [
+                    {
+                        "name": "Bloem P."
+                    },
+                    {
+                        "name": "Groth P."
+                    },
+                    {
+                        "name": "Thanapalasingam T."
+                    },
+                    {
+                        "name": "van Berkel L."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "PeerJ Computer Science",
+                "title": "Relational graph convolutional networks: a closer look"
+            },
+            "pmcid": "PMC9680895",
+            "pmid": "36426239"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Laboratory techniques",
+            "uri": "http://edamontology.org/topic_3361"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/rho/rho.biotools.json b/data/rho/rho.biotools.json
new file mode 100644
index 0000000000000..73457531c5369
--- /dev/null
+++ b/data/rho/rho.biotools.json
@@ -0,0 +1,125 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T01:10:19.503395Z",
+    "biotoolsCURIE": "biotools:rho",
+    "biotoolsID": "rho",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "elena.bidnenko@inrae.fr",
+            "name": "Elena Bidnenko",
+            "orcidid": "https://orcid.org/0000-0002-9945-0946",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Vladimir Bidnenko"
+        },
+        {
+            "name": "Matthieu Jules",
+            "orcidid": "https://orcid.org/0000-0003-0817-8949"
+        },
+        {
+            "name": "Pierre Nicolas",
+            "orcidid": "https://orcid.org/0000-0002-1133-0609"
+        }
+    ],
+    "description": "Termination factor Rho mediates transcriptional reprogramming of Bacillus subtilis stationary phase.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "DNA transcription",
+                    "uri": "http://edamontology.org/operation_0372"
+                },
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://genoscapist.migale.inrae.fr/seb_rho/",
+    "lastUpdate": "2023-03-18T01:11:23.789534Z",
+    "name": "Rho",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PGEN.1010618",
+            "metadata": {
+                "abstract": "Transcription termination factor Rho is known for its ubiquitous role in suppression of pervasive, mostly antisense, transcription. In the model Gram-positive bacterium Bacillus subtilis, de-repression of pervasive transcription by inactivation of rho revealed the role of Rho in the regulation of post-exponential differentiation programs. To identify other aspects of the regulatory role of Rho during adaptation to starvation, we have constructed a B. subtilis strain (Rho+) that expresses rho at a relatively stable high level in order to compensate for its decrease in the wild-type cells entering stationary phase. The RNAseq analysis of Rho+, WT and Δrho strains (expression profiles can be visualized at http://genoscapist.migale.inrae.fr/seb_rho/) shows that Rho over-production enhances the termination efficiency of Rho-sensitive terminators, thus reducing transcriptional read-through and antisense transcription genome-wide. Moreover, the Rho+ strain exhibits global alterations of sense transcription with the most significant changes observed for the AbrB, CodY, and stringent response regulons, forming the pathways governing the transition to stationary phase. Subsequent physiological analyses demonstrated that maintaining rho expression at a stable elevated level modifies stationary phase-specific physiology of B. subtilis cells, weakens stringent response, and thereby negatively affects the cellular adaptation to nutrient limitations and other stresses, and blocks the development of genetic competence and sporulation. These results highlight the Rho-specific termination of transcription as a novel element controlling stationary phase. The release of this control by decreasing Rho levels during the transition to stationary phase appears crucial for the functionality of complex gene networks ensuring B. subtilis survival in stationary phase.",
+                "authors": [
+                    {
+                        "name": "Bidnenko E."
+                    },
+                    {
+                        "name": "Bidnenko V."
+                    },
+                    {
+                        "name": "Chastanet A."
+                    },
+                    {
+                        "name": "Dairou J."
+                    },
+                    {
+                        "name": "Derozier S."
+                    },
+                    {
+                        "name": "Guerin C."
+                    },
+                    {
+                        "name": "Jules M."
+                    },
+                    {
+                        "name": "Nicolas P."
+                    },
+                    {
+                        "name": "Redko-Hamel Y."
+                    }
+                ],
+                "date": "2023-02-03T00:00:00Z",
+                "journal": "PLoS Genetics",
+                "title": "Termination factor Rho mediates transcriptional reprogramming of Bacillus subtilis stationary phase"
+            },
+            "pmcid": "PMC9931155",
+            "pmid": "36735730"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene regulation",
+            "uri": "http://edamontology.org/topic_0204"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/ribo-uorf/ribo-uorf.biotools.json b/data/ribo-uorf/ribo-uorf.biotools.json
new file mode 100644
index 0000000000000..e17b2706472fe
--- /dev/null
+++ b/data/ribo-uorf/ribo-uorf.biotools.json
@@ -0,0 +1,146 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T09:23:01.528377Z",
+    "biotoolsCURIE": "biotools:ribo-uorf",
+    "biotoolsID": "ribo-uorf",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "rgregory@enders.tch.harvard.edu",
+            "name": "Richard I Gregory",
+            "orcidid": "https://orcid.org/0000-0001-8090-8673",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Xin Peng"
+        },
+        {
+            "name": "Mengyuan Shen",
+            "orcidid": "https://orcid.org/0000-0002-2038-1774"
+        },
+        {
+            "name": "Qi Liu",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A comprehensive data resource of upstream open reading frames (uORFs) based on ribosome profiling.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Ensembl ID",
+                        "uri": "http://edamontology.org/data_2610"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Gene symbol",
+                        "uri": "http://edamontology.org/data_1026"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://rnainformatics.org.cn/RiboUORF",
+    "language": [
+        "Perl"
+    ],
+    "lastUpdate": "2023-01-30T09:23:01.530845Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/rnainformatics/ribo-uORF"
+        }
+    ],
+    "name": "Ribo-uORF",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1094",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Upstream open reading frames (uORFs) are typically defined as translation sites located within the 5' untranslated region upstream of the main protein coding sequence (CDS) of messenger RNAs (mRNAs). Although uORFs are prevalent in eukaryotic mRNAs and modulate the translation of downstream CDSs, a comprehensive resource for uORFs is currently lacking. We developed Ribo-uORF (http://rnainformatics.org.cn/RiboUORF) to serve as a comprehensive functional resource for uORF analysis based on ribosome profiling (Ribo-seq) data. Ribo-uORF currently supports six species: human, mouse, rat, zebrafish, fruit fly, and worm. Ribo-uORF includes 501 554 actively translated uORFs and 107 914 upstream translation initiation sites (uTIS), which were identified from 1495 Ribo-seq and 77 quantitative translation initiation sequencing (QTI-seq) datasets, respectively. We also developed mRNAbrowse to visualize items such as uORFs, cis-regulatory elements, genetic variations, eQTLs, GWAS-based associations, RNA modifications, and RNA editing. Ribo-uORF provides a very intuitive web interface for conveniently browsing, searching, and visualizing uORF data. Finally, uORFscan and UTR5var were developed in Ribo-uORF to precisely identify uORFs and analyze the influence of genetic mutations on uORFs using user-uploaded datasets. Ribo-uORF should greatly facilitate studies of uORFs and their roles in mRNA translation and posttranscriptional control of gene expression.",
+                "authors": [
+                    {
+                        "name": "Gregory R.I."
+                    },
+                    {
+                        "name": "Li C."
+                    },
+                    {
+                        "name": "Liu Q."
+                    },
+                    {
+                        "name": "Peng X."
+                    },
+                    {
+                        "name": "Qian Q."
+                    },
+                    {
+                        "name": "Shen M."
+                    },
+                    {
+                        "name": "Xing J."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "Ribo-uORF: a comprehensive data resource of upstream open reading frames (uORFs) based on ribosome profiling"
+            },
+            "pmcid": "PMC9825487",
+            "pmid": "36440758"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        }
+    ]
+}
diff --git a/data/ricu/ricu.biotools.json b/data/ricu/ricu.biotools.json
new file mode 100644
index 0000000000000..b19043819a250
--- /dev/null
+++ b/data/ricu/ricu.biotools.json
@@ -0,0 +1,16 @@
+{
+    "additionDate": "2023-03-05T08:16:07.998300Z",
+    "biotoolsCURIE": "biotools:ricu",
+    "biotoolsID": "ricu",
+    "description": "Focused on (but not exclusive to) data sets hosted on PhysioNet (https://physionet.org), 'ricu' provides utilities for download, setup and access of intensive care unit (ICU) data sets. In addition to functions for running arbitrary queries against available data sets, a system for defining clinical concepts and encoding their representations in tabular ICU data is presented.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/eth-mds/ricu",
+    "lastUpdate": "2023-03-05T08:16:08.001853Z",
+    "name": "ricu",
+    "owner": "nbenn",
+    "version": [
+        "0.5.4"
+    ]
+}
diff --git a/data/ridao/ridao.biotools.json b/data/ridao/ridao.biotools.json
new file mode 100644
index 0000000000000..aecc193d7cce8
--- /dev/null
+++ b/data/ridao/ridao.biotools.json
@@ -0,0 +1,99 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T09:17:47.034224Z",
+    "biotoolsCURIE": "biotools:ridao",
+    "biotoolsID": "ridao",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "vuversky@usf.edu",
+            "name": "Vladimir N. Uversky",
+            "orcidid": "https://orcid.org/0000-0002-4037-5857",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "gdayhoff@usf.edu",
+            "name": "Guy W. Dayhoff",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Rapid prediction and analysis of protein intrinsic disorder.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Information extraction",
+                    "uri": "http://edamontology.org/operation_3907"
+                },
+                {
+                    "term": "Protein disorder prediction",
+                    "uri": "http://edamontology.org/operation_3904"
+                },
+                {
+                    "term": "Protein secondary structure prediction (coils)",
+                    "uri": "http://edamontology.org/operation_0470"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ridao.app",
+    "lastUpdate": "2023-01-30T09:17:47.036802Z",
+    "name": "RIDAO",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/PRO.4496",
+            "metadata": {
+                "abstract": "© 2022 The Protein Society.Protein intrinsic disorder is found in all kingdoms of life and is known to underpin numerous physiological and pathological processes. Computational methods play an important role in characterizing and identifying intrinsically disordered proteins and protein regions. Herein, we present a new high-efficiency web-based disorder predictor named Rapid Intrinsic Disorder Analysis Online (RIDAO) that is designed to facilitate the application of protein intrinsic disorder analysis in genome-scale structural bioinformatics and comparative genomics/proteomics. RIDAO integrates six established disorder predictors into a single, unified platform that reproduces the results of individual predictors with near-perfect fidelity. To demonstrate the potential applications, we construct a test set containing more than one million sequences from one hundred organisms comprising over 420 million residues. Using this test set, we compare the efficiency and accessibility (i.e., ease of use) of RIDAO to five well-known and popular disorder predictors, namely: AUCpreD, IUPred3, metapredict V2, flDPnn, and SPOT-Disorder2. We show that RIDAO yields per-residue predictions at a rate two to six orders of magnitude greater than the other predictors and completely processes the test set in under an hour. RIDAO can be accessed free of charge at https://ridao.app.",
+                "authors": [
+                    {
+                        "name": "Dayhoff G.W."
+                    },
+                    {
+                        "name": "Uversky V.N."
+                    }
+                ],
+                "citationCount": 2,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Protein Science",
+                "title": "Rapid prediction and analysis of protein intrinsic disorder"
+            },
+            "pmcid": "PMC9679974",
+            "pmid": "36334049"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        },
+        {
+            "term": "Protein disordered structure",
+            "uri": "http://edamontology.org/topic_3538"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
+}
diff --git a/data/rimeta/rimeta.biotools.json b/data/rimeta/rimeta.biotools.json
new file mode 100644
index 0000000000000..a1c07b65649b8
--- /dev/null
+++ b/data/rimeta/rimeta.biotools.json
@@ -0,0 +1,114 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T01:06:20.687123Z",
+    "biotoolsCURIE": "biotools:rimeta",
+    "biotoolsID": "rimeta",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Fateh Bazerbachi"
+        },
+        {
+            "name": "Haitao Chu",
+            "orcidid": "https://orcid.org/0000-0003-0932-598X"
+        },
+        {
+            "name": "Lianne Siegel",
+            "orcidid": "https://orcid.org/0000-0001-9440-9146"
+        },
+        {
+            "name": "Wenhao Cao",
+            "orcidid": "https://orcid.org/0000-0001-9213-3705"
+        },
+        {
+            "name": "Ziren Jiang",
+            "orcidid": "https://orcid.org/0000-0002-5830-327X"
+        }
+    ],
+    "description": "An R shiny tool for estimating the reference interval from a meta-analysis.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cers.shinyapps.io/RIMeta/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-18T01:06:20.690342Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/liannesiegel/RIMeta"
+        }
+    ],
+    "name": "RIMeta",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/JRSM.1626",
+            "metadata": {
+                "abstract": "A reference interval, or an interval in which a prespecified proportion of measurements from a healthy population are expected to fall, is used to determine whether a person's measurement is typical of a healthy individual. For a specific biomarker, multiple published studies may provide data collected from healthy participants. A reference interval estimated by combining the data across these studies is typically more generalizable than a reference interval based on a single study. Methods for estimating reference intervals from random effects meta-analysis and fixed-effects meta-analysis have been recently proposed and implemented using R software. We present an R Shiny tool, RIMeta, implementing these methods, which allows users not proficient in R to estimate a reference interval from a meta-analysis using aggregate data (mean, standard deviation, and sample size) from each study. RIMeta (https://cers.shinyapps.io/RIMeta/) provides users a convenient way to estimate a reference interval from a meta-analysis and to generate the reference interval plot to visualize the results. The use of this web-based R Shiny tool does not require the installation of R or any background knowledge of programming. We explain all functions of the R Shiny tool and illustrate how to use it with a real data example.",
+                "authors": [
+                    {
+                        "name": "Bazerbachi F."
+                    },
+                    {
+                        "name": "Cao W."
+                    },
+                    {
+                        "name": "Chu H."
+                    },
+                    {
+                        "name": "Jiang Z."
+                    },
+                    {
+                        "name": "Siegel L."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Research Synthesis Methods",
+                "title": "RIMeta: An R shiny tool for estimating the reference interval from a meta-analysis"
+            },
+            "pmid": "36725922"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/rlbind/rlbind.biotools.json b/data/rlbind/rlbind.biotools.json
new file mode 100644
index 0000000000000..2b32b550615c7
--- /dev/null
+++ b/data/rlbind/rlbind.biotools.json
@@ -0,0 +1,99 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T09:15:07.205041Z",
+    "biotoolsCURIE": "biotools:rlbind",
+    "biotoolsID": "rlbind",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "limin@mail.csu.edu.cn",
+            "name": "Min Li",
+            "orcidid": "https://orcid.org/0000-0002-0188-1394",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Kaili Wang"
+        },
+        {
+            "name": "Renyi Zhou"
+        },
+        {
+            "name": "Yifan Wu"
+        }
+    ],
+    "description": "A deep learning method to predict RNA-ligand binding sites.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Binding site prediction",
+                    "uri": "http://edamontology.org/operation_2575"
+                },
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "Molecular docking",
+                    "uri": "http://edamontology.org/operation_0478"
+                },
+                {
+                    "term": "RNA structure prediction",
+                    "uri": "http://edamontology.org/operation_2441"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/KailiWang1/RLBind",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-30T09:15:07.207614Z",
+    "license": "Apache-2.0",
+    "name": "RLBind",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC486",
+            "pmid": "36398911"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/rmechdb/rmechdb.biotools.json b/data/rmechdb/rmechdb.biotools.json
new file mode 100644
index 0000000000000..146564f50ee16
--- /dev/null
+++ b/data/rmechdb/rmechdb.biotools.json
@@ -0,0 +1,98 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T00:59:59.265863Z",
+    "biotoolsCURIE": "biotools:rmechdb",
+    "biotoolsID": "rmechdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Mohammadamin Tavakoli"
+        },
+        {
+            "name": "Ann Marie Carlton",
+            "orcidid": "https://orcid.org/0000-0002-8574-1507"
+        },
+        {
+            "name": "David Van Vranken",
+            "orcidid": "https://orcid.org/0000-0001-5964-7042"
+        },
+        {
+            "name": "Pierre Baldi",
+            "orcidid": "https://orcid.org/0000-0001-8752-4664"
+        },
+        {
+            "name": "Yin Ting T. Chiu",
+            "orcidid": "https://orcid.org/0000-0001-5449-8391"
+        }
+    ],
+    "description": "A Public Database of Elementary Radical Reaction Steps.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://deeprxn.ics.uci.edu/rmechdb/",
+    "lastUpdate": "2023-03-18T00:59:59.270109Z",
+    "name": "RMechDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCIM.2C01359",
+            "metadata": {
+                "abstract": "We introduce RMechDB, an open-access platform for aggregating, curating, and distributing reliable data about elementary radical reaction steps for computational radical reaction modeling and prediction. RMechDB contains over 5,300 elementary radical reaction steps, each with a single transition state at or around room temperature. These elementary step reactions are manually curated plausible arrow-pushing steps for organic radical reactions. The steps were taken from a variety of sources. Over 2,000 mechanistic steps were extracted from textbooks and/or constructed from research publications. Another 3,000 were taken from gas-phase atmospheric reactions of isoprene and other organic molecules on the MCM (Master Chemical Mechanism) Web site. Reactions are encoded in the SMIRKS format with accurate atom mapping and annotations for arrow-pushing mechanisms. At its core, RMechDB consists of a database schema with an online interactive search interface and a request portal for downloading the raw form of elementary step reactions with their metadata. It also offers an interface for submitting new reactions to RMechDB and expanding the data set through community contributions. Although there are several applications for RMechDB, it is primarily designed as a central platform of radical elementary steps with a unified and structured representation. We believe that this open access to this data and platform enables the extension of data-driven models for chemical reaction predictions and other chemoinformatics predictive tasks.",
+                "authors": [
+                    {
+                        "name": "Baldi P."
+                    },
+                    {
+                        "name": "Carlton A.M."
+                    },
+                    {
+                        "name": "Chiu Y.T.T."
+                    },
+                    {
+                        "name": "Tavakoli M."
+                    },
+                    {
+                        "name": "Van Vranken D."
+                    }
+                ],
+                "date": "2023-02-27T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "RMechDB: A Public Database of Elementary Radical Reaction Steps"
+            },
+            "pmid": "36799778"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Cheminformatics",
+            "uri": "http://edamontology.org/topic_2258"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        }
+    ]
+}
diff --git a/data/rname/rname.biotools.json b/data/rname/rname.biotools.json
new file mode 100644
index 0000000000000..d682c82c7a306
--- /dev/null
+++ b/data/rname/rname.biotools.json
@@ -0,0 +1,130 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T09:01:47.094392Z",
+    "biotoolsCURIE": "biotools:rname",
+    "biotoolsID": "rname",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "greatchen@ncst.edu.cn",
+            "name": "Wei Chen",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Fulei Nie"
+        },
+        {
+            "name": "Qiang Tang"
+        },
+        {
+            "name": "Pengmian Feng",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A comprehensive database of RNA modification enzymes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene symbol",
+                        "uri": "http://edamontology.org/data_1026"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Organism name",
+                        "uri": "http://edamontology.org/data_2909"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://chenweilab.cn/rname/",
+    "lastUpdate": "2023-01-30T09:01:47.097879Z",
+    "name": "RNAME",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.11.022",
+            "metadata": {
+                "abstract": "© 2022 The Author(s)The dynamic RNA modifications were orchestrated by a series of enzymes, namely “writer”, “reader” and “eraser”, which can install, recognize and remove the modifications, respectively. However, only a very small number of experimentally validated RNA modification enzymes have been identified and reported. Therefore, there is an urgent need to develop a database to deposit RNA modification enzymes. In the present work, we developed the RNAME database (https://chenweilab.cn/rname/) to provide a comprehensive resource for RNA modification enzymes. The current version of RNAME deposits more than 21,000 manually curated RNA modification enzymes, which are from 456 species and covers the 7 common kinds of RNA modifications (i.e., adenosine to inosine, N1-methyladenosine, N6-methyladenosine, 5-methylcytidine, N7-methylguanosine, mRNA cap modification, and pseudouridine). The 3D structures, domains, subcellular locations, and biological functions of these enzymes were also integrated in RNAME. It is anticipated that RNAME will facilitate the researches on RNA modifications.",
+                "authors": [
+                    {
+                        "name": "Chen W."
+                    },
+                    {
+                        "name": "Feng P."
+                    },
+                    {
+                        "name": "Liu S."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Nie F."
+                    },
+                    {
+                        "name": "Qin H."
+                    },
+                    {
+                        "name": "Tang Q."
+                    },
+                    {
+                        "name": "Wu M."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "RNAME: A comprehensive database of RNA modification enzymes"
+            },
+            "pmcid": "PMC9678767",
+            "pmid": "36420165"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Enzymes",
+            "uri": "http://edamontology.org/topic_0821"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        }
+    ]
+}
diff --git a/data/rnasequest/rnasequest.biotools.json b/data/rnasequest/rnasequest.biotools.json
new file mode 100644
index 0000000000000..a03ab3047fab9
--- /dev/null
+++ b/data/rnasequest/rnasequest.biotools.json
@@ -0,0 +1,89 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T00:54:26.833911Z",
+    "biotoolsCURIE": "biotools:rnasequest",
+    "biotoolsID": "rnasequest",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Baohong Zhang"
+        },
+        {
+            "name": "Fergal Casey"
+        },
+        {
+            "name": "Jing Zhu"
+        },
+        {
+            "name": "Yu H. Sun"
+        }
+    ],
+    "description": "An end-to-end reproducible RNAseq data analysis and publishing framework.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://interactivereport.github.io/RNASequest/tutorial/docs/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                },
+                {
+                    "term": "RNA-Seq analysis",
+                    "uri": "http://edamontology.org/operation_3680"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/interactivereport/RNASequest",
+    "language": [
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2023-03-18T00:54:26.838539Z",
+    "license": "MIT",
+    "name": "RNASequest",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.JMB.2023.168017",
+            "pmid": "36806691"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Database management",
+            "uri": "http://edamontology.org/topic_3489"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/rnasmc/rnasmc.biotools.json b/data/rnasmc/rnasmc.biotools.json
new file mode 100644
index 0000000000000..c8d49f2591b04
--- /dev/null
+++ b/data/rnasmc/rnasmc.biotools.json
@@ -0,0 +1,105 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T00:49:12.480164Z",
+    "biotoolsCURIE": "biotools:rnasmc",
+    "biotoolsID": "rnasmc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "qujia@eye.ac.cn",
+            "name": "Jia Qu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xuld@eye.ac.cn",
+            "name": "Liangde Xu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Hong Wang"
+        },
+        {
+            "name": "Xiaoyan Lu"
+        }
+    ],
+    "description": "A integrated tool for comparing RNA secondary structure and evaluating allosteric effects.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://cran.r-project.org/web/packages/RNAsmc/RNAsmc.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "RNA secondary structure alignment",
+                    "uri": "http://edamontology.org/operation_0502"
+                },
+                {
+                    "term": "RNA structure covariance model generation",
+                    "uri": "http://edamontology.org/operation_3469"
+                },
+                {
+                    "term": "Structure visualisation",
+                    "uri": "http://edamontology.org/operation_0570"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://CRAN.R-project.org/package=RNAsmc",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-18T00:49:12.484832Z",
+    "license": "GPL-2.0",
+    "name": "RNAsmc",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2023.01.007",
+            "pmcid": "PMC9876829",
+            "pmid": "36733704"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Nucleic acid structure analysis",
+            "uri": "http://edamontology.org/topic_0097"
+        },
+        {
+            "term": "Protein structural motifs and surfaces",
+            "uri": "http://edamontology.org/topic_0166"
+        }
+    ]
+}
diff --git a/data/roiformsi/roiformsi.biotools.json b/data/roiformsi/roiformsi.biotools.json
new file mode 100644
index 0000000000000..ddaa237ef188d
--- /dev/null
+++ b/data/roiformsi/roiformsi.biotools.json
@@ -0,0 +1,19 @@
+{
+    "additionDate": "2023-02-13T03:15:28.020344Z",
+    "biotoolsCURIE": "biotools:roiformsi",
+    "biotoolsID": "roiformsi",
+    "credit": [
+        {
+            "email": "ang.guo@siat.ac.cn",
+            "name": "Ang Guo"
+        }
+    ],
+    "description": "Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. Here we report a multimodal fusion strategy to enable an objective and trustworthy selection of \\#Clusters by utilizing additional information from corresponding histology images. Our work will greatly facilitate MSI-mediated spatial lipidomics, metabolomics, and proteomics research by providing intelligent software to automatically and reliably generate ROIs.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/guoang4github/ROIforMSI/",
+    "lastUpdate": "2023-02-13T03:18:50.062985Z",
+    "name": "ROIforMSI",
+    "owner": "guoang681"
+}
diff --git a/data/rv-esa/rv-esa.biotools.json b/data/rv-esa/rv-esa.biotools.json
new file mode 100644
index 0000000000000..37530a5606ad3
--- /dev/null
+++ b/data/rv-esa/rv-esa.biotools.json
@@ -0,0 +1,114 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T20:26:36.931326Z",
+    "biotoolsCURIE": "biotools:rv-esa",
+    "biotoolsID": "rv-esa",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Honggang Zhang"
+        },
+        {
+            "name": "Jinkai Li"
+        },
+        {
+            "name": "Wai Keung Wong"
+        },
+        {
+            "name": "Xiaoling Luo"
+        },
+        {
+            "name": "Yong Xu"
+        },
+        {
+            "name": "Jingyong Su",
+            "orcidid": "https://orcid.org/0000-0003-3216-7027"
+        }
+    ],
+    "description": "A novel computer-aided elastic shape analysis system for retinal vessels in diabetic retinopathy.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Principal component analysis",
+                    "uri": "http://edamontology.org/operation_3960"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/XiaolingLuo/RV-ESA",
+    "language": [
+        "C++",
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-02-13T20:26:36.933966Z",
+    "license": "Not licensed",
+    "name": "RV-ESA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106406",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdDiabetic retinopathy (DR), one of the most common and serious complications of diabetes, has become one of the main blindness diseases. The retinal vasculature is the only part of the human circulatory system that allows direct noninvasive visualization of the body's microvasculature, which provides the opportunity to detect the structural and functional changes before DR becomes unable to intervene. For decades, as the fundamental step in computer-assisted analysis of retinopathy, retinal vascular extraction methods have been largely developed. However, further research focusing on retinal vascular analysis is still in its infancy. Meanwhile, due to the complexity of retinal vascular structure, the relationship between vascular geometry and DR has never been concluded. This paper aims to provide a novel computer-aided shape analysis system for retinal vessels. To perform retinal vascular shape analysis, a mathematical geometric representation is firstly generated by utilizing the proposed shape modeling method. Then, several useful statistical tools (e.g. Graph Mean, Graph PCA) are adopted to quantitatively analyze the vascular shape. Besides, in order to visualize the changes in vascular shape in the progression of DR, a geodesic tool is used to display the deformation process for ophthalmologists to observe. The efficacy of this analysis system is demonstrated in the EyePACS dataset and the subsequent visit records of 98 patients from the proprietary dataset. The experimental results show that there is a certain correlation between the variation of retinal vascular shape and DR progression, and the Graph PCA scores of retinal vessels are negatively correlated with DR grades. The code of our RV-ESA system can be publicly available at github.com/XiaolingLuo/RV-ESA.",
+                "authors": [
+                    {
+                        "name": "Li J."
+                    },
+                    {
+                        "name": "Luo X."
+                    },
+                    {
+                        "name": "Su J."
+                    },
+                    {
+                        "name": "Wong W.K."
+                    },
+                    {
+                        "name": "Xu Y."
+                    },
+                    {
+                        "name": "Zhang H."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "RV-ESA: A novel computer-aided elastic shape analysis system for retinal vessels in diabetic retinopathy"
+            },
+            "pmid": "36521357"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Human biology",
+            "uri": "http://edamontology.org/topic_2815"
+        },
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/h4rvs/h4rvs.biotools.json b/data/rvs_software/rvs_software.biotools.json
similarity index 93%
rename from data/h4rvs/h4rvs.biotools.json
rename to data/rvs_software/rvs_software.biotools.json
index 7e9ba3007956b..4e6b1653fde47 100644
--- a/data/h4rvs/h4rvs.biotools.json
+++ b/data/rvs_software/rvs_software.biotools.json
@@ -1,7 +1,7 @@
 {
-    "additionDate": "2021-01-20T10:56:52Z",
-    "biotoolsCURIE": "biotools:h4rvs",
-    "biotoolsID": "h4rvs",
+    "additionDate": "2023-02-13T20:20:42.025598Z",
+    "biotoolsCURIE": "biotools:rvs_software",
+    "biotoolsID": "rvs_software",
     "collectionID": [
         "Rare Disease"
     ],
@@ -39,10 +39,10 @@
     "language": [
         "R"
     ],
-    "lastUpdate": "2021-05-17T06:51:18Z",
+    "lastUpdate": "2023-02-13T20:20:42.028161Z",
     "license": "GPL-2.0",
-    "name": "h4RVS",
-    "owner": "Niclaskn",
+    "name": "RVS",
+    "owner": "Jennifer",
     "publication": [
         {
             "doi": "10.1093/BIOINFORMATICS/BTY976",
@@ -65,6 +65,7 @@
                         "name": "Sherman T."
                     }
                 ],
+                "citationCount": 3,
                 "date": "2019-07-15T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "Detection of rare disease variants in extended pedigrees using RVS"
diff --git a/data/s2d/s2d.biotools.json b/data/s2d/s2d.biotools.json
index cf4b0dc4e2bed..2a6b03590ee7a 100644
--- a/data/s2d/s2d.biotools.json
+++ b/data/s2d/s2d.biotools.json
@@ -1,7 +1,10 @@
 {
+    "accessibility": "Open access (with restrictions)",
     "additionDate": "2022-08-30T08:04:38.115656Z",
     "biotoolsCURIE": "biotools:s2d",
     "biotoolsID": "s2d",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "credit": [
         {
             "email": "mv245@cam.ac.uk",
@@ -26,8 +29,22 @@
         ],
         "type": "group"
     },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Prediction and recognition",
+                    "uri": "http://edamontology.org/operation_2423"
+                },
+                {
+                    "term": "Structure analysis",
+                    "uri": "http://edamontology.org/operation_2480"
+                }
+            ]
+        }
+    ],
     "homepage": "http://www-mvsoftware.ch.cam.ac.uk/index.php/s2D",
-    "lastUpdate": "2022-12-09T21:49:24.815015Z",
+    "lastUpdate": "2023-01-13T01:59:03.396548Z",
     "license": "GPL-3.0",
     "name": "s2D",
     "operatingSystem": [
diff --git a/data/salmobase2/salmobase2.biotools.json b/data/salmobase2/salmobase2.biotools.json
index 906cacccd8d88..a835784354195 100644
--- a/data/salmobase2/salmobase2.biotools.json
+++ b/data/salmobase2/salmobase2.biotools.json
@@ -1,7 +1,10 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2019-10-02T11:59:28Z",
     "biotoolsCURIE": "biotools:salmobase2",
     "biotoolsID": "salmobase2",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "credit": [
         {
             "email": "amine.namouchi@nmbu.no",
@@ -17,17 +20,30 @@
     "editPermission": {
         "type": "private"
     },
+    "elixirNode": [
+        "Norway"
+    ],
     "homepage": "https://salmobase.org/",
-    "lastUpdate": "2019-10-02T12:00:47Z",
+    "lastUpdate": "2023-01-13T15:46:45.931725Z",
     "name": "salmobase2",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "aminenamouchi",
     "publication": [
         {
-            "doi": "10.7490/f1000research.1117096.1",
-            "type": [
-                "Other"
-            ],
-            "version": "2.0"
+            "doi": "10.7490/f1000research.1117096.1"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
         }
     ],
     "validated": 1,
diff --git a/data/sam-dta/sam-dta.biotools.json b/data/sam-dta/sam-dta.biotools.json
new file mode 100644
index 0000000000000..782f1d4bfd8bf
--- /dev/null
+++ b/data/sam-dta/sam-dta.biotools.json
@@ -0,0 +1,88 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T20:17:21.265506Z",
+    "biotoolsCURIE": "biotools:sam-dta",
+    "biotoolsID": "sam-dta",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "huzhiqiang@sensetime.com",
+            "name": "Zhiqiang Hu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "song.ke@matwings.com",
+            "name": "Song Ke",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Liang Hong"
+        },
+        {
+            "name": "Wenfeng Liu"
+        }
+    ],
+    "description": "A sequence-agnostic model for drug-target binding affinity prediction.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein feature detection",
+                    "uri": "http://edamontology.org/operation_3092"
+                },
+                {
+                    "term": "Scatter plot plotting",
+                    "uri": "http://edamontology.org/operation_2940"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/huzqatpku/SAM-DTA",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T20:17:21.268125Z",
+    "license": "Apache-2.0",
+    "name": "SAM-DTA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC533",
+            "pmid": "36545795"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Pharmacology",
+            "uri": "http://edamontology.org/topic_0202"
+        },
+        {
+            "term": "Protein targeting and localisation",
+            "uri": "http://edamontology.org/topic_0140"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/sampling/sampling.biotools.json b/data/sampling/sampling.biotools.json
new file mode 100644
index 0000000000000..c9a76b8686f63
--- /dev/null
+++ b/data/sampling/sampling.biotools.json
@@ -0,0 +1,42 @@
+{
+    "additionDate": "2023-02-11T07:33:12.088013Z",
+    "biotoolsCURIE": "biotools:sampling",
+    "biotoolsID": "sampling",
+    "confidence_flag": "tool",
+    "credit": [
+        {
+            "name": "Daniel Winter Heck"
+        }
+    ],
+    "description": "New iOS Application for Assessment of Damage by Diseases and Insect Pests Using Sequential Sampling Plans.",
+    "editPermission": {
+        "type": "public"
+    },
+    "homepage": "https://apple.co/3pUiYKy",
+    "lastUpdate": "2023-02-11T07:33:29.443317Z",
+    "name": "Sampling",
+    "owner": "Chan019",
+    "publication": [
+        {
+            "doi": "10.1094/PDIS-04-22-0800-SR",
+            "pmid": "36428257"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Sample collections",
+            "uri": "http://edamontology.org/topic_3277"
+        }
+    ]
+}
diff --git a/data/samppred-gat/samppred-gat.biotools.json b/data/samppred-gat/samppred-gat.biotools.json
new file mode 100644
index 0000000000000..ffd220581ee3a
--- /dev/null
+++ b/data/samppred-gat/samppred-gat.biotools.json
@@ -0,0 +1,149 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-30T08:49:30.384492Z",
+    "biotoolsCURIE": "biotools:samppred-gat",
+    "biotoolsID": "samppred-gat",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "bliu@bliulab.net",
+            "name": "Bin Liu",
+            "orcidid": "https://orcid.org/0000-0003-3685-9469",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Hongwu Lv"
+        },
+        {
+            "name": "Ke Yan"
+        },
+        {
+            "name": "Wei Peng"
+        },
+        {
+            "name": "Yichen Guo"
+        }
+    ],
+    "description": "Prediction of antimicrobial peptide by graph attention network and predicted peptide structure.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                },
+                {
+                    "term": "Protein structure prediction",
+                    "uri": "http://edamontology.org/operation_0474"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bliulab.net/sAMPpred-GAT",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-30T08:49:30.388327Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "http://bliulab.net/sAMPpred-GAT/data/"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/HongWuL/sAMPpred-GAT/"
+        }
+    ],
+    "name": "sAMPpred-GAT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC715",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Antimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis. Unfortunately, the predicted peptide structure information has not been applied to the field of AMP prediction so as to improve the predictive performance. RESULTS: In this study, we proposed a computational predictor called sAMPpred-GAT for AMP identification. To the best of our knowledge, sAMPpred-GAT is the first approach based on the predicted peptide structures for AMP prediction. The sAMPpred-GAT predictor constructs the graphs based on the predicted peptide structures, sequence information and evolutionary information. The Graph Attention Network (GAT) is then performed on the graphs to learn the discriminative features. Finally, the full connection networks are utilized as the output module to predict whether the peptides are AMP or not. Experimental results show that sAMPpred-GAT outperforms the other state-of-the-art methods in terms of AUC, and achieves better or highly comparable performance in terms of the other metrics on the eight independent test datasets, demonstrating that the predicted peptide structure information is important for AMP prediction. AVAILABILITY AND IMPLEMENTATION: A user-friendly webserver of sAMPpred-GAT can be accessed at http://bliulab.net/sAMPpred-GAT and the source code is available at https://github.com/HongWuL/sAMPpred-GAT/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Guo Y."
+                    },
+                    {
+                        "name": "Liu B."
+                    },
+                    {
+                        "name": "Lv H."
+                    },
+                    {
+                        "name": "Peng W."
+                    },
+                    {
+                        "name": "Yan K."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure"
+            },
+            "pmcid": "PMC9805557",
+            "pmid": "36342186"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        }
+    ]
+}
diff --git a/data/samtools/samtools.biotools.json b/data/samtools/samtools.biotools.json
index a7e0355b0861d..9357bb7eff53d 100644
--- a/data/samtools/samtools.biotools.json
+++ b/data/samtools/samtools.biotools.json
@@ -164,7 +164,7 @@
     "language": [
         "C"
     ],
-    "lastUpdate": "2022-10-14T14:29:52.662706Z",
+    "lastUpdate": "2023-02-21T14:52:33.543128Z",
     "license": "MIT",
     "link": [
         {
@@ -228,7 +228,7 @@
                         "name": "Wysoker A."
                     }
                 ],
-                "citationCount": 29863,
+                "citationCount": 31575,
                 "date": "2009-08-01T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "The Sequence Alignment/Map format and SAMtools"
@@ -243,7 +243,7 @@
         {
             "doi": "10.1093/gigascience/giab008",
             "metadata": {
-                "abstract": "© The Author(s) 2021. Published by Oxford University Press GigaScience.BACKGROUND: SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. FINDINGS: The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. CONCLUSION: Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.",
+                "abstract": "© 2021 The Author(s). Published by Oxford University Press GigaScience.Background: SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. Findings: The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. Conclusion: Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.",
                 "authors": [
                     {
                         "name": "Bonfield J.K."
@@ -257,9 +257,6 @@
                     {
                         "name": "Keane T."
                     },
-                    {
-                        "name": "Li H."
-                    },
                     {
                         "name": "Liddle J."
                     },
@@ -279,8 +276,8 @@
                         "name": "Whitwham A."
                     }
                 ],
-                "citationCount": 595,
-                "date": "2021-02-16T00:00:00Z",
+                "citationCount": 978,
+                "date": "2021-02-01T00:00:00Z",
                 "journal": "GigaScience",
                 "title": "Twelve years of SAMtools and BCFtools"
             },
@@ -300,7 +297,7 @@
                         "name": "Li H."
                     }
                 ],
-                "citationCount": 2902,
+                "citationCount": 3133,
                 "date": "2011-11-01T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data"
@@ -350,6 +347,7 @@
         "1.15.1",
         "1.16",
         "1.16.1",
+        "1.17",
         "1.2",
         "1.3",
         "1.3.1",
diff --git a/data/sars-cov-2-network-analysis/sars-cov-2-network-analysis.biotools.json b/data/sars-cov-2-network-analysis/sars-cov-2-network-analysis.biotools.json
index 7553c00588850..7d7f41db34136 100644
--- a/data/sars-cov-2-network-analysis/sars-cov-2-network-analysis.biotools.json
+++ b/data/sars-cov-2-network-analysis/sars-cov-2-network-analysis.biotools.json
@@ -1,7 +1,13 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2021-11-11T00:21:23.837989Z",
     "biotoolsCURIE": "biotools:sars-cov-2-network-analysis",
     "biotoolsID": "sars-cov-2-network-analysis",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "Predict human proteins that interact with SARS-CoV-2 and trace provenance of predictions",
     "editPermission": {
         "type": "private"
@@ -39,9 +45,21 @@
         }
     ],
     "homepage": "https://github.com/Murali-group/SARS-CoV-2-network-analysis",
-    "lastUpdate": "2022-12-09T21:54:45.911486Z",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-13T01:36:26.743528Z",
+    "license": "GPL-3.0",
     "name": "SARS-CoV-2 Protein Interaction Network Analysis",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "tmmurali",
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "Human biology",
diff --git a/data/sav-pred/sav-pred.biotools.json b/data/sav-pred/sav-pred.biotools.json
new file mode 100644
index 0000000000000..715dd313aa670
--- /dev/null
+++ b/data/sav-pred/sav-pred.biotools.json
@@ -0,0 +1,110 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T00:41:34.389062Z",
+    "biotoolsCURIE": "biotools:sav-pred",
+    "biotoolsID": "sav-pred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "alexey.lagunin@ibmc.msk.ru",
+            "name": "Alexey A. Lagunin",
+            "orcidid": "https://orcid.org/0000-0003-1757-8004",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Dmitry A. Filimonov"
+        },
+        {
+            "name": "Anastasia V. Rudik",
+            "orcidid": "https://orcid.org/0000-0002-8916-9675"
+        },
+        {
+            "name": "Anton D. Zadorozhny",
+            "orcidid": "https://orcid.org/0000-0002-0160-8793"
+        }
+    ],
+    "description": "A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Variant classification",
+                    "uri": "http://edamontology.org/operation_3225"
+                },
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                },
+                {
+                    "term": "Virulence prediction",
+                    "uri": "http://edamontology.org/operation_3461"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.way2drug.com/SAV-Pred/",
+    "lastUpdate": "2023-03-18T00:41:34.394714Z",
+    "name": "SAV-Pred",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/IJMS24032463",
+            "metadata": {
+                "abstract": "Next Generation Sequencing (NGS) technologies are rapidly entering clinical practice. A promising area for their use lies in the field of newborn screening. The mass screening of newborns using NGS technology leads to the discovery of a large number of new missense variants that need to be assessed for association with the development of hereditary diseases. Currently, the primary analysis and identification of pathogenic variations is carried out using bioinformatic tools. Although extensive efforts have been made in the computational approach to variant interpretation, there is currently no generally accepted pathogenicity predictor. In this study, we used the sequence–structure–property relationships (SSPR) approach, based on the representation of protein fragments by molecular structural formula. The approach predicts the pathogenic effect of single amino acid substitutions in proteins related with twenty-five monogenic heritable diseases from the Uniform Screening Panel for Major Conditions recommended by the Advisory Committee on Hereditary Disorders in Newborns and Children. In order to create SSPR models of classification, we modified a piece of cheminformatics software, MultiPASS, that was originally developed for the prediction of activity spectra for drug-like substances. The created SSPR models were compared with traditional bioinformatic tools (SIFT 4G, Polyphen-2 HDIV, MutationAssessor, PROVEAN and FATHMM). The average AUC of our approach was 0.804 ± 0.040. Better quality scores were achieved for 15 from 25 proteins with a significantly higher accuracy for some proteins (IVD, HADHB, HBB). The best SSPR models of classification are freely available in the online resource SAV-Pred (Single Amino acid Variants Predictor).",
+                "authors": [
+                    {
+                        "name": "Filimonov D.A."
+                    },
+                    {
+                        "name": "Lagunin A.A."
+                    },
+                    {
+                        "name": "Rudik A.V."
+                    },
+                    {
+                        "name": "Zadorozhny A.D."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "International Journal of Molecular Sciences",
+                "title": "SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening"
+            },
+            "pmcid": "PMC9917004",
+            "pmid": "36768784"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cheminformatics",
+            "uri": "http://edamontology.org/topic_2258"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/sbmldiagrams/sbmldiagrams.biotools.json b/data/sbmldiagrams/sbmldiagrams.biotools.json
new file mode 100644
index 0000000000000..5997d23333163
--- /dev/null
+++ b/data/sbmldiagrams/sbmldiagrams.biotools.json
@@ -0,0 +1,125 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T03:02:21.657696Z",
+    "biotoolsCURIE": "biotools:sbmldiagrams",
+    "biotoolsID": "sbmldiagrams",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hsauro@uw.edu",
+            "name": "Herbert M Sauro",
+            "orcidid": "https://orcid.org/0000-0002-3659-6817",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jessie Jiang"
+        },
+        {
+            "name": "Jin Xu",
+            "orcidid": "https://orcid.org/0000-0001-6738-9979"
+        }
+    ],
+    "description": "A python package to process and visualize SBML layout and render.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://sys-bio.github.io/SBMLDiagrams/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/sys-bio/SBMLDiagrams",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-26T03:02:21.660316Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://www.youtube.com/watch?v=zF3_fkDp2Xk&ab_channel=SunnyXu"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "http://pypi.org/project/SBMLDiagrams/"
+        }
+    ],
+    "name": "SBMLDiagrams",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC730",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: The systems biology markup language (SBML) is an extensible standard format for exchanging biochemical models. One of the extensions for SBML is the SBML Layout and Render package. This allows modelers to describe a biochemical model as a pathway diagram. However, up to now, there has been little support to help users easily add and retrieve such information from SBML. In this application note, we describe a new Python package called SBMLDiagrams. This package allows a user to add a layout and render information or retrieve it using a straightforward Python API. The package uses skia-python to support the rendering of the diagrams, allowing export to commons formats such as PNG or PDF. AVAILABILITY AND IMPLEMENTATION: SBMLDiagrams is publicly available and licensed under the liberal MIT open-source license. The package is available for all major platforms. The source code has been deposited at GitHub (github.com/sys-bio/SBMLDiagrams). Users can install the package using the standard pip installation mechanism: pip install SBMLDiagrams. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Jiang J."
+                    },
+                    {
+                        "name": "Sauro H.M."
+                    },
+                    {
+                        "name": "Xu J."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "SBMLDiagrams: a python package to process and visualize SBML layout and render"
+            },
+            "pmcid": "PMC9805581",
+            "pmid": "36370074"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Software engineering",
+            "uri": "http://edamontology.org/topic_3372"
+        },
+        {
+            "term": "Systems biology",
+            "uri": "http://edamontology.org/topic_2259"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/sc2mol/sc2mol.biotools.json b/data/sc2mol/sc2mol.biotools.json
new file mode 100644
index 0000000000000..89af6ac85c25c
--- /dev/null
+++ b/data/sc2mol/sc2mol.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T20:12:05.905549Z",
+    "biotoolsCURIE": "biotools:sc2mol",
+    "biotoolsID": "sc2mol",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhusf@fudan.edu.cn",
+            "name": "Shanfeng Zhu",
+            "orcidid": "https://orcid.org/0000-0002-6067-5312",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Hiroshi Mamitsuka"
+        },
+        {
+            "name": "Lei Xie"
+        },
+        {
+            "name": "Zhirui Liao"
+        }
+    ],
+    "description": "A scaffold-based two-step molecule generator with variational autoencoder and transformer.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Backbone modelling",
+                    "uri": "http://edamontology.org/operation_0479"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/zhiruiliao/Sc2Mol",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T20:12:05.908177Z",
+    "license": "Not licensed",
+    "name": "Sc2Mol",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC814",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Finding molecules with desired pharmaceutical properties is crucial in drug discovery. Generative models can be an efficient tool to find desired molecules through the distribution learned by the model to approximate given training data. Existing generative models (i) do not consider backbone structures (scaffolds), resulting in inefficiency or (ii) need prior patterns for scaffolds, causing bias. Scaffolds are reasonable to use, and it is imperative to design a generative model without any prior scaffold patterns. RESULTS: We propose a generative model-based molecule generator, Sc2Mol, without any prior scaffold patterns. Sc2Mol uses SMILES strings for molecules. It consists of two steps: scaffold generation and scaffold decoration, which are carried out by a variational autoencoder and a transformer, respectively. The two steps are powerful for implementing random molecule generation and scaffold optimization. Our empirical evaluation using drug-like molecule datasets confirmed the success of our model in distribution learning and molecule optimization. Also, our model could automatically learn the rules to transform coarse scaffolds into sophisticated drug candidates. These rules were consistent with those for current lead optimization. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/zhiruiliao/Sc2Mol. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Liao Z."
+                    },
+                    {
+                        "name": "Mamitsuka H."
+                    },
+                    {
+                        "name": "Xie L."
+                    },
+                    {
+                        "name": "Zhu S."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer"
+            },
+            "pmcid": "PMC9835482",
+            "pmid": "36576008"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/sc3s/sc3s.biotools.json b/data/sc3s/sc3s.biotools.json
new file mode 100644
index 0000000000000..a0baf2f9da98c
--- /dev/null
+++ b/data/sc3s/sc3s.biotools.json
@@ -0,0 +1,97 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T20:08:51.468842Z",
+    "biotoolsCURIE": "biotools:sc3s",
+    "biotoolsID": "sc3s",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mhemberg@bwh.harvard.edu",
+            "name": "Martin Hemberg",
+            "orcidid": "https://orcid.org/0000-0001-8895-5239",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Fu Xiang Quah",
+            "orcidid": "https://orcid.org/0000-0002-4616-5324"
+        }
+    ],
+    "description": "Efficient scaling of single cell consensus clustering to millions of cells.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/hemberg-lab/sc3s/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T20:08:51.471323Z",
+    "license": "GPL-3.0",
+    "name": "SC3s",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05085-Z",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Today it is possible to profile the transcriptome of individual cells, and a key step in the analysis of these datasets is unsupervised clustering. For very large datasets, efficient algorithms are required to ensure that analyses can be conducted with reasonable time and memory requirements. Results: Here, we present a highly efficient k-means based approach, and we demonstrate that it scales favorably with the number of cells with regards to time and memory. Conclusions: We have demonstrated that our streaming k-means clustering algorithm gives state-of-the-art performance while resource requirements scale favorably for up to 2 million cells.",
+                "authors": [
+                    {
+                        "name": "Hemberg M."
+                    },
+                    {
+                        "name": "Quah F.X."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "SC3s: efficient scaling of single cell consensus clustering to millions of cells"
+            },
+            "pmcid": "PMC9743492",
+            "pmid": "36503522"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/scab/scab.biotools.json b/data/scab/scab.biotools.json
new file mode 100644
index 0000000000000..c0a8c49a47050
--- /dev/null
+++ b/data/scab/scab.biotools.json
@@ -0,0 +1,110 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T02:47:01.521581Z",
+    "biotoolsCURIE": "biotools:scab",
+    "biotoolsID": "scab",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "sqjin@whu.edu.cn",
+            "name": "Suoqin Jin",
+            "orcidid": "https://orcid.org/0000-0002-5131-0215",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xfzou@whu.edu.cn",
+            "name": "Xiufen Zou",
+            "orcidid": "https://orcid.org/0000-0001-5294-0764",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Qinran Zhang",
+            "orcidid": "https://orcid.org/0000-0001-8920-586X"
+        }
+    ],
+    "description": "scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Cell migration analysis",
+                    "uri": "http://edamontology.org/operation_3446"
+                },
+                {
+                    "term": "Deisotoping",
+                    "uri": "http://edamontology.org/operation_3629"
+                },
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/jinworks/scAB",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-26T02:47:01.525682Z",
+    "license": "GPL-3.0",
+    "name": "scAB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1109",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Although single-cell sequencing has provided a powerful tool to deconvolute cellular heterogeneity of diseases like cancer, extrapolating clinical significance or identifying clinically-relevant cells remains challenging. Here, we propose a novel computational method scAB, which integrates single-cell genomics data with clinically annotated bulk sequencing data via a knowledge- and graph-guided matrix factorization model. Once combined, scAB provides a coarse- and fine-grain multiresolution perspective of phenotype-associated cell states and prognostic signatures previously not visible by single-cell genomics. We use scAB to enhance live cancer single-cell RNA-seq data, identifying clinically-relevant previously unrecognized cancer and stromal cell subsets whose signatures show a stronger poor-survival association. The identified fine-grain cell subsets are associated with distinct cancer hallmarks and prognosis power. Furthermore, scAB demonstrates its utility as a biomarker identification tool, with the ability to predict immunotherapy, drug responses and survival when applied to melanoma single-cell RNA-seq datasets and glioma single-cell ATAC-seq datasets. Across multiple single-cell and bulk datasets from different cancer types, we also demonstrate the superior performance of scAB in generating prognosis signatures and survival predictions over existing models. Overall, scAB provides an efficient tool for prioritizing clinically-relevant cell subsets and predictive signatures, utilizing large publicly available databases to improve prognosis and treatments.",
+                "authors": [
+                    {
+                        "name": "Jin S."
+                    },
+                    {
+                        "name": "Zhang Q."
+                    },
+                    {
+                        "name": "Zou X."
+                    }
+                ],
+                "date": "2022-11-28T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data"
+            },
+            "pmcid": "PMC9757078",
+            "pmid": "36440766"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/scaffold_generator/scaffold_generator.biotools.json b/data/scaffold_generator/scaffold_generator.biotools.json
new file mode 100644
index 0000000000000..9062cbad1127c
--- /dev/null
+++ b/data/scaffold_generator/scaffold_generator.biotools.json
@@ -0,0 +1,124 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T02:41:20.826962Z",
+    "biotoolsCURIE": "biotools:scaffold_generator",
+    "biotoolsID": "scaffold_generator",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "christoph.steinbeck@uni-jena.de",
+            "name": "Christoph Steinbeck",
+            "orcidid": "https://orcid.org/0000-0001-6966-0814",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Achim Zielesny",
+            "orcidid": "http://orcid.org/0000-0003-0722-4229"
+        },
+        {
+            "name": "Jonas Schaub",
+            "orcidid": "http://orcid.org/0000-0003-1554-6666"
+        },
+        {
+            "name": "Julian Zander",
+            "orcidid": "http://orcid.org/0000-0001-8197-076X"
+        }
+    ],
+    "description": "A Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK).",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Backbone modelling",
+                    "uri": "http://edamontology.org/operation_0479"
+                },
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "S/MAR prediction",
+                    "uri": "http://edamontology.org/operation_0444"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Julian-Z98/ScaffoldGenerator",
+    "language": [
+        "Java"
+    ],
+    "lastUpdate": "2023-01-26T02:41:20.829388Z",
+    "license": "LGPL-2.1",
+    "name": "Scaffold Generator",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13321-022-00656-X",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT (COlleCtion of Open Natural prodUcTs) database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.",
+                "authors": [
+                    {
+                        "name": "Schaub J."
+                    },
+                    {
+                        "name": "Steinbeck C."
+                    },
+                    {
+                        "name": "Zander J."
+                    },
+                    {
+                        "name": "Zielesny A."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Journal of Cheminformatics",
+                "title": "Scaffold Generator: a Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK)"
+            },
+            "pmcid": "PMC9650898",
+            "pmid": "36357931"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Anatomy",
+            "uri": "http://edamontology.org/topic_3067"
+        },
+        {
+            "term": "Cheminformatics",
+            "uri": "http://edamontology.org/topic_2258"
+        },
+        {
+            "term": "Chemistry",
+            "uri": "http://edamontology.org/topic_3314"
+        },
+        {
+            "term": "DNA binding sites",
+            "uri": "http://edamontology.org/topic_3125"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/scanalyzer/scanalyzer.biotools.json b/data/scanalyzer/scanalyzer.biotools.json
new file mode 100644
index 0000000000000..f79c070ef861d
--- /dev/null
+++ b/data/scanalyzer/scanalyzer.biotools.json
@@ -0,0 +1,110 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T20:04:57.076263Z",
+    "biotoolsCURIE": "biotools:scanalyzer",
+    "biotoolsID": "scanalyzer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "dwhho@hku.hk",
+            "name": "Daniel Wai-Hung Ho",
+            "orcidid": "https://orcid.org/0000-0003-3884-296X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "GS Chuwdhury, MPhil"
+        },
+        {
+            "name": "Irene Oi-Lin Ng, MD, PhD"
+        }
+    ],
+    "description": "A Comprehensive Software Package With Graphical User Interface for Single-Cell RNA Sequencing Analysis and its Application on Liver Cancer.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/sarwarchy20/scAnalyzeR",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-13T20:04:57.079005Z",
+    "license": "GPL-3.0",
+    "name": "scAnalyzeR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1177/15330338221142729",
+            "metadata": {
+                "abstract": "© The Author(s) 2022.Introduction: The application of single-cell RNA sequencing to delineate tissue heterogeneity and complexity has become increasingly popular. Given its tremendous resolution and high-dimensional capacity for in-depth investigation, single-cell RNA sequencing offers an unprecedented research power. Although some popular software packages are available for single-cell RNA sequencing data analysis and visualization, it is still a big challenge for their usage, as they provide only a command-line interface and require significant level of bioinformatics skills. Methods: We have developed scAnalyzeR, which is a single-cell RNA sequencing analysis pipeline with an interactive and user-friendly graphical interface for analyzing and visualizing single-cell RNA sequencing data. It accepts single-cell RNA sequencing data from various technology platforms and different model organisms (human and mouse) and allows flexibility in input file format. It provides functionalities for data preprocessing, quality control, basic summary statistics, dimension reduction, unsupervised clustering, differential gene expression, gene set enrichment analysis, correlation analysis, pseudotime cell trajectory inference, and various visualization plots. It also provides default parameters for easy usage and allows a wide range of flexibility and optimization by accepting user-defined options. It has been developed as a docker image that can be run in any docker-supported environment including Linux, Mac, and Windows, without installing any dependencies. Results: We compared the performance of scAnalyzeR with 2 other graphical tools that are popular for analyzing single-cell RNA sequencing data. The comparison was based on the comprehensiveness of functionalities, ease of usage and flexibility, and execution time. In general, scAnalyzeR outperformed the other tested counterparts in various aspects, demonstrating its superior overall performance. To illustrate the usefulness of scAnalyzeR in cancer research, we have analyzed the in-house liver cancer single-cell RNA sequencing dataset. Liver cancer tumor cells were revealed to have multiple subpopulations with distinctive gene expression signatures. Conclusion: scAnalyzeR has comprehensive functionalities and demonstrated usability. We anticipate more functionalities to be adopted in the future development.",
+                "authors": [
+                    {
+                        "name": "Chuwdhury G.S."
+                    },
+                    {
+                        "name": "Ho D.W.-H."
+                    },
+                    {
+                        "name": "Ng I.O.-L."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Technology in Cancer Research and Treatment",
+                "title": "scAnalyzeR: A Comprehensive Software Package With Graphical User Interface for Single-Cell RNA Sequencing Analysis and its Application on Liver Cancer"
+            },
+            "pmcid": "PMC9742707",
+            "pmid": "36476060"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/scawmv/scawmv.biotools.json b/data/scawmv/scawmv.biotools.json
new file mode 100644
index 0000000000000..99909774ef408
--- /dev/null
+++ b/data/scawmv/scawmv.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T02:35:57.444194Z",
+    "biotoolsCURIE": "biotools:scawmv",
+    "biotoolsID": "scawmv",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhixianglin@cuhk.edu.hk",
+            "name": "Zhixiang Lin",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Pengcheng Zeng",
+            "orcidid": "https://orcid.org/0000-0002-0724-5313"
+        },
+        {
+            "name": "Yuanyuan Ma",
+            "orcidid": "https://orcid.org/0000-0001-9103-5513"
+        }
+    ],
+    "description": "An adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Expression profile clustering",
+                    "uri": "http://edamontology.org/operation_0313"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/pengchengzeng/scAWMV",
+    "language": [
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-01-26T02:35:57.447126Z",
+    "license": "Not licensed",
+    "name": "scAWMV",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC739",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Technological advances have enabled us to profile single-cell multi-omics data from the same cells, providing us with an unprecedented opportunity to understand the cellular phenotype and links to its genotype. The available protocols and multi-omics datasets [including parallel single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data profiled from the same cell] are growing increasingly. However, such data are highly sparse and tend to have high level of noise, making data analysis challenging. The methods that integrate the multi-omics data can potentially improve the capacity of revealing the cellular heterogeneity. RESULTS: We propose an adaptively weighted multi-view learning (scAWMV) method for the integrative analysis of parallel scRNA-seq and scATAC-seq data profiled from the same cell. scAWMV considers both the difference in importance across different modalities in multi-omics data and the biological connection of the features in the scRNA-seq and scATAC-seq data. It generates biologically meaningful low-dimensional representations for the transcriptomic and epigenomic profiles via unsupervised learning. Application to four real datasets demonstrates that our framework scAWMV is an efficient method to dissect cellular heterogeneity for single-cell multi-omics data. AVAILABILITY AND IMPLEMENTATION: The software and datasets are available at https://github.com/pengchengzeng/scAWMV. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Lin Z."
+                    },
+                    {
+                        "name": "Ma Y."
+                    },
+                    {
+                        "name": "Zeng P."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data"
+            },
+            "pmcid": "PMC9805575",
+            "pmid": "36383176"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/scbgeda/scbgeda.biotools.json b/data/scbgeda/scbgeda.biotools.json
new file mode 100644
index 0000000000000..6abf2be05d55b
--- /dev/null
+++ b/data/scbgeda/scbgeda.biotools.json
@@ -0,0 +1,125 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-18T00:33:32.107520Z",
+    "biotoolsCURIE": "biotools:scbgeda",
+    "biotoolsID": "scbgeda",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "lixt314@jlu.edu.cn",
+            "name": "Xiangtao Li",
+            "orcidid": "https://orcid.org/0000-0002-8716-9823",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Yunhe Wang"
+        },
+        {
+            "name": "Zhuohan Yu"
+        },
+        {
+            "name": "Ka-Chun Wong",
+            "orcidid": "https://orcid.org/0000-0001-6062-733X"
+        }
+    ],
+    "description": "Deep Single-cell Clustering Analysis via Dual Denoising Autoencoder with Bipartite Graph Ensemble Clustering.",
+    "download": [
+        {
+            "type": "Other",
+            "url": "https://figshare.com/articles/software/scBGEDA/19657911"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/wangyh082/scBGEDA",
+    "language": [
+        "MATLAB",
+        "Python"
+    ],
+    "lastUpdate": "2023-03-18T00:33:32.112079Z",
+    "license": "Not licensed",
+    "name": "scBGEDA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD075",
+            "metadata": {
+                "abstract": "MOTIVATION: Single-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that facilitates accurate identification of cell types and the study of the characteristics of their transcripts. Recently, several computational models based on a deep autoencoder and the ensemble clustering have been developed to analyze scRNA-seq data. However, current deep autoencoders are not sufficient to learn the latent representations of scRNA-seq data, and obtaining consensus partitions from these feature representations remains under-explored. RESULTS: To address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature representation via explicit modeling of synergistic optimization of the zero-inflated negative binomial reconstruction loss and denoising reconstruction loss. Then, a bipartite graph ensemble clustering algorithm is designed to exploit the relationships between cells and the learned latent embedded space by means of a graph-based consensus function. Multiple comparison experiments were conducted on 20 scRNA-seq datasets from different sequencing platforms using a variety of clustering metrics. The experimental results indicated that scBGEDA outperforms other state-of-the-art methods on these datasets, and also demonstrated its scalability to large-scale scRNA-seq datasets. Moreover, scBGEDA was able to identify cell-type specific marker genes and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into identifying cell types and characterizing the scRNA-seq data from different perspectives. AVAILABILITY AND IMPLEMENTATION: The source code of scBGEDA is available at https://github.com/wangyh082/scBGEDA. The software and the supporting data can be downloaded from https://figshare.com/articles/software/scBGEDA/19657911. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Bian C."
+                    },
+                    {
+                        "name": "Li S."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Liang Y."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Wong K.-C."
+                    },
+                    {
+                        "name": "Yu Z."
+                    }
+                ],
+                "date": "2023-02-14T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering"
+            },
+            "pmcid": "PMC9925104",
+            "pmid": "36734596"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/scdec-hi-c/scdec-hi-c.biotools.json b/data/scdec-hi-c/scdec-hi-c.biotools.json
new file mode 100644
index 0000000000000..74dfb36c81c2b
--- /dev/null
+++ b/data/scdec-hi-c/scdec-hi-c.biotools.json
@@ -0,0 +1,125 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T14:24:56.204280Z",
+    "biotoolsCURIE": "biotools:scdec-hi-c",
+    "biotoolsID": "scdec-hi-c",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "muzhou@sensebrain.site",
+            "name": "Mu Zhou",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "ruijiang@tsinghua.edu.cn",
+            "name": "Rui Jiang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhangshaoting@pjlab.org.cn",
+            "name": "Shaoting Zhang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Qiao Liu"
+        }
+    ],
+    "description": "Deep generative modeling and clustering of single cell Hi-C data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/kimmo1019/scDEC-Hi-C",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-27T14:24:56.206743Z",
+    "license": "MIT",
+    "name": "scDEC-Hi-C",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bib/bbac494",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Deciphering 3D genome conformation is important for understanding gene regulation and cellular function at a spatial level. The recent advances of single cell Hi-C technologies have enabled the profiling of the 3D architecture of DNA within individual cell, which allows us to study the cell-to-cell variability of 3D chromatin organization. Computational approaches are in urgent need to comprehensively analyze the sparse and heterogeneous single cell Hi-C data. Here, we proposed scDEC-Hi-C, a new framework for single cell Hi-C analysis with deep generative neural networks. scDEC-Hi-C outperforms existing methods in terms of single cell Hi-C data clustering and imputation. Moreover, the generative power of scDEC-Hi-C could help unveil the differences of chromatin architecture across cell types. We expect that scDEC-Hi-C could shed light on deepening our understanding of the complex mechanism underlying the formation of chromatin contacts.",
+                "authors": [
+                    {
+                        "name": "Chen H."
+                    },
+                    {
+                        "name": "Jiang R."
+                    },
+                    {
+                        "name": "Liu Q."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Zeng W."
+                    },
+                    {
+                        "name": "Zhang S."
+                    },
+                    {
+                        "name": "Zhang W."
+                    },
+                    {
+                        "name": "Zhou M."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "Deep generative modeling and clustering of single cell Hi-C data"
+            },
+            "pmid": "36458445"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Chromosome conformation capture",
+            "uri": "http://edamontology.org/topic_3940"
+        },
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/scdrug/scdrug.biotools.json b/data/scdrug/scdrug.biotools.json
new file mode 100644
index 0000000000000..39fb15878c33b
--- /dev/null
+++ b/data/scdrug/scdrug.biotools.json
@@ -0,0 +1,128 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T19:55:25.706915Z",
+    "biotoolsCURIE": "biotools:scdrug",
+    "biotoolsID": "scdrug",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hsuancheng@nycu.edu.tw",
+            "name": "Hsuan-Cheng Huang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "jiahsin.huang@ailabs.tw",
+            "name": "Jia-Hsin Huang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "yukijuan@ntu.edu.tw",
+            "name": "Hsueh-Fen Juan",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Chiao-Yu Hsieh"
+        }
+    ],
+    "description": "From single-cell RNA-seq to drug response prediction.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ailabstw/scDrug",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T19:55:25.709597Z",
+    "license": "Not licensed",
+    "name": "scDrug",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.11.055",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsSingle-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://github.com/ailabstw/scDrug.",
+                "authors": [
+                    {
+                        "name": "Hsieh C.-Y."
+                    },
+                    {
+                        "name": "Huang H.-C."
+                    },
+                    {
+                        "name": "Huang J.-H."
+                    },
+                    {
+                        "name": "Juan H.-F."
+                    },
+                    {
+                        "name": "Lin S.-M."
+                    },
+                    {
+                        "name": "Tseng T.-Y."
+                    },
+                    {
+                        "name": "Wen J.-H."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "scDrug: From single-cell RNA-seq to drug response prediction"
+            },
+            "pmcid": "PMC9747355",
+            "pmid": "36544472"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Pharmacogenomics",
+            "uri": "http://edamontology.org/topic_0208"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/scehr/scehr.biotools.json b/data/scehr/scehr.biotools.json
new file mode 100644
index 0000000000000..3e3b8c4db04a7
--- /dev/null
+++ b/data/scehr/scehr.biotools.json
@@ -0,0 +1,81 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T02:30:32.166112Z",
+    "biotoolsCURIE": "biotools:scehr",
+    "biotoolsID": "scehr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Chengxi Zang"
+        },
+        {
+            "name": "Fei Wang"
+        }
+    ],
+    "description": "Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/calvin-zcx/SCEHR",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-26T02:30:32.169216Z",
+    "license": "Not licensed",
+    "name": "SCEHR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1109/ICDM51629.2021.00097",
+            "metadata": {
+                "abstract": "© 2021 IEEE.Contrastive learning has demonstrated promising performance in image and text domains either in a self-supervised or a supervised manner. In this work, we extend the supervised contrastive learning framework to clinical risk prediction problems based on longitudinal electronic health records (EHR). We propose a general supervised contrastive loss {L}Contrastive Cross Entropy+\\lambda {L}Supervised Contrastive Regularizer for learning both binary classification (e.g. in-hospital mortality prediction) and multi-label classification (e.g. phenotyping) in a unified framework. Our supervised contrastive loss practices the key idea of contrastive learning, namely, pulling similar samples closer and pushing dissimilar ones apart from each other, simultaneously by its two components: {L}Contrastive Cross Entropy tries to contrast samples with learned anchors which represent positive and negative clusters, and {L}Supervised Contrastive Regularizer tries to contrast samples with each other according to their supervised labels. We propose two versions of the above supervised contrastive loss and our experiments on real-world EHR data demonstrate that our proposed loss functions show benefits in improving the performance of strong baselines and even state-of-the-art models on benchmarking tasks for clinical risk predictions. Our loss functions work well with extremely imbalanced data which are common for clinical risk prediction problems. Our loss functions can be easily used to replace (binary or multi-label) cross-entropy loss adopted in existing clinical predictive models. The Pytorch code is released at https://github.com/calvin-zcx/SCEHR.",
+                "authors": [
+                    {
+                        "name": "Wang F."
+                    },
+                    {
+                        "name": "Zang C."
+                    }
+                ],
+                "date": "2021-01-01T00:00:00Z",
+                "journal": "Proceedings - IEEE International Conference on Data Mining, ICDM",
+                "title": "SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records"
+            },
+            "pmcid": "PMC9692209",
+            "pmid": "36438203"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        }
+    ]
+}
diff --git a/data/scfates/scfates.biotools.json b/data/scfates/scfates.biotools.json
new file mode 100644
index 0000000000000..446c183d2f095
--- /dev/null
+++ b/data/scfates/scfates.biotools.json
@@ -0,0 +1,124 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T14:16:25.000223Z",
+    "biotoolsCURIE": "biotools:scfates",
+    "biotoolsID": "scfates",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "igor.adameyko@meduniwien.ac.at",
+            "name": "Igor Adameyko",
+            "orcidid": "http://orcid.org/0000-0001-5471-0356",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Louis Faure",
+            "orcidid": "http://orcid.org/0000-0003-4621-586X"
+        },
+        {
+            "name": "Peter V. Kharchenko",
+            "orcidid": "http://orcid.org/0000-0002-6036-5875"
+        },
+        {
+            "name": "Ruslan Soldatov",
+            "orcidid": "http://orcid.org/0000-0002-5670-5881"
+        }
+    ],
+    "description": "A scalable python package for advanced pseudotime and bifurcation analysis from single cell data.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://scfates.readthedocs.io/en/latest/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pypi.org/project/scFates/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-27T14:16:46.328830Z",
+    "license": "BSD-3-Clause",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/LouisFaure/scFates/"
+        }
+    ],
+    "name": "scFates",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac746",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: scFates provides an extensive toolset for the analysis of dynamic trajectories comprising tree learning, feature association testing, branch differential expression and with a focus on cell biasing and fate splits at the level of bifurcations. It is meant to be fully integrated into the scanpy ecosystem for seamless analysis of trajectories from single-cell data of various modalities (e.g. RNA and ATAC). AVAILABILITY AND IMPLEMENTATION: scFates is released as open-source software under the BSD 3-Clause 'New' License and is available from the Python Package Index at https://pypi.org/project/scFates/. The source code is available on GitHub at https://github.com/LouisFaure/scFates/. Code reproduction and tutorials on published datasets are available on GitHub at https://github.com/LouisFaure/scFates_notebooks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Adameyko I."
+                    },
+                    {
+                        "name": "Faure L."
+                    },
+                    {
+                        "name": "Kharchenko P.V."
+                    },
+                    {
+                        "name": "Soldatov R."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data"
+            },
+            "pmcid": "PMC9805561",
+            "pmid": "36394263"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/scgcl/scgcl.biotools.json b/data/scgcl/scgcl.biotools.json
new file mode 100644
index 0000000000000..34dd21e665ab1
--- /dev/null
+++ b/data/scgcl/scgcl.biotools.json
@@ -0,0 +1,92 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T14:33:00.646680Z",
+    "biotoolsCURIE": "biotools:scgcl",
+    "biotoolsID": "scgcl",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xiongzehao@hnu.edu.cn",
+            "name": "Zehao Xiong",
+            "orcidid": "https://orcid.org/0000-0002-6549-9610",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "luojiawei@hnu.edu.cn",
+            "name": "Jiawei Luo",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Bo Wang"
+        },
+        {
+            "name": "Wanwan Shi"
+        },
+        {
+            "name": "Ying Liu"
+        },
+        {
+            "name": "Zhongyuan Xu",
+            "orcidid": "https://orcid.org/0000-0003-1759-9665"
+        }
+    ],
+    "description": "An imputation method for scRNA-seq data based on Graph Contrastive Learning.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Expression profile clustering",
+                    "uri": "http://edamontology.org/operation_0313"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/zehaoxiong123/scGCL",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T14:33:00.651524Z",
+    "license": "Not licensed",
+    "name": "scGCL",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD098",
+            "pmid": "36825817"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/schumannet/schumannet.biotools.json b/data/schumannet/schumannet.biotools.json
new file mode 100644
index 0000000000000..12af893065862
--- /dev/null
+++ b/data/schumannet/schumannet.biotools.json
@@ -0,0 +1,130 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T10:58:59.768335Z",
+    "biotoolsCURIE": "biotools:schumannet",
+    "biotoolsID": "schumannet",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mhemberg@bwh.harvard.edu",
+            "name": "Insuk Lee",
+            "orcidid": "http://orcid.org/0000-0003-3146-6180",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "insuklee@yonsei.ac.kr",
+            "name": "Martin Hemberg",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jae-Won Cho"
+        },
+        {
+            "name": "Jiwon Yu"
+        },
+        {
+            "name": "Junha Cha"
+        }
+    ],
+    "description": "A single-cell network analysis platform for the study of cell-type specificity of disease genes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deisotoping",
+                    "uri": "http://edamontology.org/operation_3629"
+                },
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/netbiolab/scHumanNet",
+    "language": [
+        "C",
+        "C++",
+        "R"
+    ],
+    "lastUpdate": "2023-02-26T10:58:59.770878Z",
+    "license": "GPL-3.0",
+    "name": "scHumanNet",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/nar/gkac1042",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.A major challenge in single-cell biology is identifying cell-type-specific gene functions, which may substantially improve precision medicine. Differential expression analysis of genes is a popular, yet insufficient approach, and complementary methods that associate function with cell type are required. Here, we describe scHumanNet (https://github.com/netbiolab/scHumanNet), a single-cell network analysis platform for resolving cellular heterogeneity across gene functions in humans. Based on cell-type-specific gene networks (CGNs) constructed under the guidance of the HumanNet reference interactome, scHumanNet displayed higher functional relevance to the cellular context than CGNs built by other methods on single-cell transcriptome data. Cellular deconvolution of gene signatures based on network compactness across cell types revealed breast cancer prognostic markers associated with T cells. scHumanNet could also prioritize genes associated with particular cell types using CGN centrality and identified the differential hubness of CGNs between disease and healthy conditions. We demonstrated the usefulness of scHumanNet by uncovering T-cell-specific functional effects of GITR, a prognostic gene for breast cancer, and functional defects in autism spectrum disorder genes specific for inhibitory neurons. These results suggest that scHumanNet will advance our understanding of cell-type specificity across human disease genes.",
+                "authors": [
+                    {
+                        "name": "Cha J."
+                    },
+                    {
+                        "name": "Cho J.-W."
+                    },
+                    {
+                        "name": "Hemberg M."
+                    },
+                    {
+                        "name": "Lee I."
+                    },
+                    {
+                        "name": "Yu J."
+                    }
+                ],
+                "date": "2023-01-25T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes"
+            },
+            "pmcid": "PMC9881140",
+            "pmid": "36350625"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        }
+    ]
+}
diff --git a/data/sciber/sciber.biotools.json b/data/sciber/sciber.biotools.json
new file mode 100644
index 0000000000000..27a8880d9b14d
--- /dev/null
+++ b/data/sciber/sciber.biotools.json
@@ -0,0 +1,117 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T19:51:17.207639Z",
+    "biotoolsCURIE": "biotools:sciber",
+    "biotoolsID": "sciber",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jun.li@nd.edu",
+            "name": "Jun Li",
+            "orcidid": "https://orcid.org/0000-0003-4353-5761",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Dailin Gan",
+            "orcidid": "https://orcid.org/0000-0003-0535-1259"
+        }
+    ],
+    "description": "A simple method for removing batch effects from single-cell RNA-sequencing data.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://cran.r-project.org/web/packages/SCIBER/SCIBER.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Expression profile clustering",
+                    "uri": "http://edamontology.org/operation_0313"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cran.r-project.org/web/packages/SCIBER/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-13T19:51:17.210147Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/RavenGan/SCIBER"
+        }
+    ],
+    "name": "SCIBER",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC819",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Integrative analysis of multiple single-cell RNA-sequencing datasets allows for more comprehensive characterizations of cell types, but systematic technical differences between datasets, known as 'batch effects', need to be removed before integration to avoid misleading interpretation of the data. Although many batch-effect-removal methods have been developed, there is still a large room for improvement: most existing methods only give dimension-reduced data instead of expression data of individual genes, are based on computationally demanding models and are black-box models and thus difficult to interpret or tune. RESULTS: Here, we present a new batch-effect-removal method called SCIBER (Single-Cell Integrator and Batch Effect Remover) and study its performance on real datasets. SCIBER matches cell clusters across batches according to the overlap of their differentially expressed genes. As a simple algorithm that has better scalability to data with a large number of cells and is easy to tune, SCIBER shows comparable and sometimes better accuracy in removing batch effects on real datasets compared to the state-of-the-art methods, which are much more complicated. Moreover, SCIBER outputs expression data in the original space, that is, the expression of individual genes, which can be used directly for downstream analyses. Additionally, SCIBER is a reference-based method, which assigns one of the batches as the reference batch and keeps it untouched during the process, making it especially suitable for integrating user-generated datasets with standard reference data such as the Human Cell Atlas. AVAILABILITY AND IMPLEMENTATION: SCIBER is publicly available as an R package on CRAN: https://cran.r-project.org/web/packages/SCIBER/. A vignette is included in the CRAN R package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Gan D."
+                    },
+                    {
+                        "name": "Li J."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data"
+            },
+            "pmcid": "PMC9848058",
+            "pmid": "36548380"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/sciga/sciga.biotools.json b/data/sciga/sciga.biotools.json
index 4bc5195b11d04..167280bb29245 100644
--- a/data/sciga/sciga.biotools.json
+++ b/data/sciga/sciga.biotools.json
@@ -1,7 +1,10 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2021-03-15T01:47:37Z",
     "biotoolsCURIE": "biotools:sciga",
     "biotoolsID": "sciga",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "SCIGA is a software for 10X single cell immunoglobulin repertoires analysis. It uses raw reads or output of Cellranger as input, and performs reads quality control, immunoglobulin sequence assembly, sequence annotation, heavy- and light- chain pairing, computing statistics and visualizing.",
     "editPermission": {
         "type": "private"
@@ -35,9 +38,21 @@
         }
     ],
     "homepage": "https://github.com/sciensic/SCIGA",
-    "lastUpdate": "2022-12-09T22:01:39.982143Z",
+    "language": [
+        "Perl"
+    ],
+    "lastUpdate": "2023-01-13T01:32:58.110399Z",
+    "license": "GPL-3.0",
     "name": "SCIGA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "yehaocheng",
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "Cell biology",
diff --git a/data/scikit-learn/scikit-learn.biotools.json b/data/scikit-learn/scikit-learn.biotools.json
index b4c0581d05d19..01b6395773026 100644
--- a/data/scikit-learn/scikit-learn.biotools.json
+++ b/data/scikit-learn/scikit-learn.biotools.json
@@ -3,6 +3,9 @@
     "additionDate": "2022-05-31T21:31:30.438374Z",
     "biotoolsCURIE": "biotools:scikit-learn",
     "biotoolsID": "scikit-learn",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
     "cost": "Free of charge",
     "description": "scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.",
     "documentation": [
@@ -60,7 +63,7 @@
     "language": [
         "Python"
     ],
-    "lastUpdate": "2022-05-31T21:31:30.440697Z",
+    "lastUpdate": "2023-01-31T07:51:45.634266Z",
     "license": "BSD-3-Clause",
     "link": [
         {
diff --git a/data/sciluigi/sciluigi.biotools.json b/data/sciluigi/sciluigi.biotools.json
index 6ae6c447bad87..b2f481c2edddd 100644
--- a/data/sciluigi/sciluigi.biotools.json
+++ b/data/sciluigi/sciluigi.biotools.json
@@ -3,6 +3,7 @@
     "biotoolsCURIE": "biotools:sciluigi",
     "biotoolsID": "sciluigi",
     "collectionID": [
+        "IMPaCT-Data",
         "Luigi"
     ],
     "cost": "Free of charge",
@@ -36,7 +37,10 @@
         }
     ],
     "editPermission": {
-        "type": "private"
+        "authors": [
+            "iacs-biocomputacion"
+        ],
+        "type": "group"
     },
     "function": [
         {
@@ -53,7 +57,7 @@
     "language": [
         "Python"
     ],
-    "lastUpdate": "2019-01-13T18:11:47Z",
+    "lastUpdate": "2023-02-01T12:55:31.045349Z",
     "license": "MIT",
     "link": [
         {
@@ -100,7 +104,7 @@
                         "name": "Spjuth O."
                     }
                 ],
-                "citationCount": 16,
+                "citationCount": 20,
                 "date": "2016-11-24T00:00:00Z",
                 "journal": "Journal of Cheminformatics",
                 "title": "Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles"
diff --git a/data/sclc/sclc.biotools.json b/data/sclc/sclc.biotools.json
new file mode 100644
index 0000000000000..55bbe3c06fa81
--- /dev/null
+++ b/data/sclc/sclc.biotools.json
@@ -0,0 +1,165 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T02:25:51.126383Z",
+    "biotoolsCURIE": "biotools:sclc",
+    "biotoolsID": "sclc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "pommier@nih.gov",
+            "name": "Yves Pommier",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Camille Tlemsani"
+        },
+        {
+            "name": "Jane E. Johnson"
+        },
+        {
+            "name": "Lorinc S. Pongor"
+        }
+    ],
+    "description": "SCLC CellMiner Cross Database (SclcCellMinerCDB) allows translational researchers to conduct analyses across all major SCLC cancer cell line pharmacogenomic data sources from NCI, Sanger GDSC, and Broad CCLE/CTRP.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Gene methylation analysis",
+                    "uri": "http://edamontology.org/operation_3207"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                },
+                {
+                    "term": "Promoter prediction",
+                    "uri": "http://edamontology.org/operation_0440"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://discover.nci.nih.gov/SclcCellMinerCDB/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-26T02:25:51.128967Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/pongorlorinc/SCLC_methylation"
+        }
+    ],
+    "name": "SCLC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.ISCI.2022.105338",
+            "metadata": {
+                "abstract": "© 2022DNA methylation is a key regulator of gene expression and a clinical therapeutic predictor. We examined global DNA methylation beyond the generally used promoter areas in human small cell lung cancer (SCLC) and find that gene body methylation is a robust positive predictor of gene expression. Combining promoter and gene body methylation better predicts gene expression than promoter methylation alone including genes involved in the neuroendocrine classification of SCLC and the expression of therapeutically relevant genes including MGMT, SLFN11, and DLL3. Importantly, for super-enhancer (SE) covered genes such as NEUROD1 or MYC, using H3K27ac and NEUROD1, ASCL1, and POU2F3 ChIP-seq data, we show that genic methylation is inversely proportional to expression, thus providing a new approach to identify potential SE regulated genes involved in SCLC pathogenesis. To advance SCLC transitional research, these data are integrated into our web portal (https://discover.nci.nih.gov/SclcCellMinerCDB/) for open and easy access to basic and clinical investigators.",
+                "authors": [
+                    {
+                        "name": "Aladjem M.I."
+                    },
+                    {
+                        "name": "Arakawa Y."
+                    },
+                    {
+                        "name": "Elloumi F."
+                    },
+                    {
+                        "name": "Gross J.M."
+                    },
+                    {
+                        "name": "Jo U."
+                    },
+                    {
+                        "name": "Johnson J.E."
+                    },
+                    {
+                        "name": "Kollipara R.K."
+                    },
+                    {
+                        "name": "Minna J.D."
+                    },
+                    {
+                        "name": "Mosavarpour S."
+                    },
+                    {
+                        "name": "Pommier Y."
+                    },
+                    {
+                        "name": "Pongor L.S."
+                    },
+                    {
+                        "name": "Reinhold W."
+                    },
+                    {
+                        "name": "Roper N."
+                    },
+                    {
+                        "name": "Teicher B.A."
+                    },
+                    {
+                        "name": "Thomas A."
+                    },
+                    {
+                        "name": "Tlemsani C."
+                    },
+                    {
+                        "name": "Varma S."
+                    }
+                ],
+                "date": "2022-11-18T00:00:00Z",
+                "journal": "iScience",
+                "title": "Integrative epigenomic analyses of small cell lung cancer cells demonstrates the clinical translational relevance of gene body methylation"
+            },
+            "pmcid": "PMC9619308",
+            "pmid": "36325065"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pharmacogenomics",
+            "uri": "http://edamontology.org/topic_0208"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/scmags/scmags.biotools.json b/data/scmags/scmags.biotools.json
new file mode 100644
index 0000000000000..55ba9635e41df
--- /dev/null
+++ b/data/scmags/scmags.biotools.json
@@ -0,0 +1,89 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T14:23:32.556746Z",
+    "biotoolsCURIE": "biotools:scmags",
+    "biotoolsID": "scmags",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Berat Doğan"
+        },
+        {
+            "name": "Yusuf Baran",
+            "orcidid": "https://orcid.org/0000-0003-3423-0771"
+        }
+    ],
+    "description": "Marker gene selection from scRNA-seq data for spatial transcriptomics studies.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://scmags.readthedocs.io/en/latest/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/doganlab/scmags",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T14:26:39.170768Z",
+    "license": "Apache-2.0",
+    "name": "scMAGS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2023.106634",
+            "metadata": {
+                "abstract": "Single-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expression and thus uncovering regulatory relationships between genes at the single-cell level. However, scRNA-seq relies on isolating cells from tissues. Therefore, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows for the measurement of gene expression while preserving spatial information. An initial step in the spatial transcriptomic analysis is to identify the cell type, which requires a careful selection of cell-specific marker genes. For this purpose, currently, scRNA-seq data is used to select a limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a novel method for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are identified before the marker gene selection step. For the selection of marker genes, cluster validity indices, the Silhouette index, or the Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast, and accurate. Even for large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with fewer memory requirements. scMAGS is made freely available at https://github.com/doganlab/scmags and can be downloaded from the Python Package Directory (PyPI) software repository with the command pip install scmags.",
+                "authors": [
+                    {
+                        "name": "Baran Y."
+                    },
+                    {
+                        "name": "Dogan B."
+                    }
+                ],
+                "date": "2023-03-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies"
+            },
+            "pmid": "36774895"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/scmcluster/scmcluster.biotools.json b/data/scmcluster/scmcluster.biotools.json
new file mode 100644
index 0000000000000..8fbd1d26b4b0e
--- /dev/null
+++ b/data/scmcluster/scmcluster.biotools.json
@@ -0,0 +1,92 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T14:18:04.461924Z",
+    "biotoolsCURIE": "biotools:scmcluster",
+    "biotoolsID": "scmcluster",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "haowu@sdu.edu.cn",
+            "name": "Haoru Zhou",
+            "orcidid": "https://orcid.org/0000-0003-1524-0093",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "wml@nwsuaf.edu.cn",
+            "name": "Meili Wang",
+            "orcidid": "https://orcid.org/0000-0001-7901-1789",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Bing Zhou",
+            "orcidid": "https://orcid.org/0000-0002-5872-8358"
+        },
+        {
+            "name": "Hao Wu",
+            "orcidid": "https://orcid.org/0000-0003-2340-9258"
+        }
+    ],
+    "description": "A high-precision cell clustering algorithm integrating marker gene set with single-cell RNA sequencing data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/HaoWuLab-Bioinformatics/SCMcluster",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T14:18:04.467110Z",
+    "license": "Not licensed",
+    "name": "SCMcluster",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BFGP/ELAD004",
+            "pmid": "36848584"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/scomap/scomap.biotools.json b/data/scomap/scomap.biotools.json
index c148f92755ca8..3aa34b6a986e5 100644
--- a/data/scomap/scomap.biotools.json
+++ b/data/scomap/scomap.biotools.json
@@ -7,7 +7,21 @@
         "KU Leuven",
         "VIB"
     ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "credit": [
+        {
+            "name": "Carmen Bravo González-Blas",
+            "orcidid": "https://orcid.org/0000-0003-0973-9410"
+        },
+        {
+            "name": "Sara Aibar",
+            "orcidid": "https://orcid.org/0000-0001-6104-7134"
+        },
+        {
+            "name": "Xiao-Jiang Quan",
+            "orcidid": "https://orcid.org/0000-0001-7359-0564"
+        },
         {
             "name": "Stein Aerts",
             "orcidid": "https://orcid.org/0000-0002-8006-0315",
@@ -25,20 +39,29 @@
             "url": "https://rawcdn.githack.com/aertslab/ScoMAP/f6cd6724682d4b6c2a8f44b2a18824a56cff2146/vignettes/Vignette.html"
         }
     ],
-    "download": [
-        {
-            "type": "Source code",
-            "url": "https://github.com/aertslab/ScoMAP",
-            "version": "0.1.0"
-        }
-    ],
     "editPermission": {
         "type": "private"
     },
     "homepage": "https://github.com/aertslab/ScoMAP",
-    "lastUpdate": "2022-12-09T22:08:49.039437Z",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-13T01:27:28.247425Z",
     "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://drive.google.com/drive/folders/1wH-2VHbKaDEKANByS_jW78Gq4mi8wifb"
+        }
+    ],
     "name": "ScoMAP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "bits@vib.be",
     "publication": [
         {
@@ -89,7 +112,7 @@
                         "name": "de Waegeneer M."
                     }
                 ],
-                "citationCount": 26,
+                "citationCount": 27,
                 "date": "2020-05-01T00:00:00Z",
                 "journal": "Molecular Systems Biology",
                 "title": "Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics"
@@ -102,6 +125,9 @@
             "version": "0.1.0"
         }
     ],
+    "toolType": [
+        "Library"
+    ],
     "topic": [
         {
             "term": "Mapping",
diff --git a/data/scorpios/scorpios.biotools.json b/data/scorpios/scorpios.biotools.json
new file mode 100644
index 0000000000000..70977e8d5fe39
--- /dev/null
+++ b/data/scorpios/scorpios.biotools.json
@@ -0,0 +1,175 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-03T10:25:03.255599Z",
+    "biotoolsCURIE": "biotools:scorpios",
+    "biotoolsID": "scorpios",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "camille.berthelot@bio.ens.psl.eu",
+            "name": "Camille Berthelot",
+            "orcidid": "https://orcid.org/0000-0001-5054-2690",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ]
+        },
+        {
+            "email": "hrc@ens.fr",
+            "name": "Hugues Roest Crollius",
+            "orcidid": "https://orcid.org/0000-0002-8209-173X",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ]
+        },
+        {
+            "email": "parey@bio.ens.psl.eu",
+            "name": "Elise Parey",
+            "orcidid": "https://orcid.org/0000-0003-3394-2697",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Developer"
+            ]
+        },
+        {
+            "name": "IBENS - DYOGEN Team",
+            "typeEntity": "Institute",
+            "url": "http://www.ibens.ens.fr/?rubrique43"
+        }
+    ],
+    "description": "SCORPiOs - Synteny-guided CORrection of Paralogies and Orthologies\n\nSCORPiOs is a synteny-guided gene tree correction pipeline for clades that have undergone a whole-genome duplication event. SCORPiOs identifies gene trees where the whole-genome duplication is missing or incorrectly placed, based on the genomic locations of the duplicated genes across the different species. SCORPiOs then builds an optimized gene tree consistent with the known WGD event, the species tree, local synteny context, as well as gene sequence evolution.",
+    "documentation": [
+        {
+            "type": [
+                "Quick start guide"
+            ],
+            "url": "https://scorpios.readthedocs.io/en/latest/"
+        }
+    ],
+    "download": [
+        {
+            "type": "Software package",
+            "url": "https://github.com/DyogenIBENS/SCORPIOS/",
+            "version": "2.0"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phylogenetic tree editing",
+                    "uri": "http://edamontology.org/operation_0326"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/DyogenIBENS/SCORPIOS",
+    "language": [
+        "Python",
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2023-03-03T10:42:31.574072Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "note": "GitHub repository",
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/DyogenIBENS/SCORPIOS"
+        },
+        {
+            "note": "Software Heritage",
+            "type": [
+                "Repository"
+            ],
+            "url": "https://archive.softwareheritage.org/swh:1:dir:95ed6b42a8b0c3ecfbc259428f722f6e1fedcb90;origin=https://github.com/DyogenIBENS/SCORPIOS;visit=swh:1:snp:b940763c20dedeeda4434db70ecd76e8669190b4;anchor=swh:1:rev:5996f93da6129c3b6490e13c29128f9700657a9f"
+        }
+    ],
+    "maturity": "Mature",
+    "name": "SCORPiOs",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "alouis",
+    "publication": [
+        {
+            "doi": "10.1093/molbev/msaa149",
+            "metadata": {
+                "abstract": "Whole-genome duplications (WGDs) have major impacts on the evolution of species, as they produce new gene copies contributing substantially to adaptation, isolation, phenotypic robustness, and evolvability. They result in large, complex gene families with recurrent gene losses in descendant species that sequence-based phylogenetic methods fail to reconstruct accurately. As a result, orthologs and paralogs are difficult to identify reliably in WGD-descended species, which hinders the exploration of functional consequences of WGDs. Here, we present Synteny-guided CORrection of Paralogies and Orthologies (SCORPiOs), a novel method to reconstruct gene phylogenies in the context of a known WGD event. WGDs generate large duplicated syntenic regions, which SCORPiOs systematically leverages as a complement to sequence evolution to infer the evolutionary history of genes. We applied SCORPiOs to the 320-My-old WGD at the origin of teleost fish. We find that almost one in four teleost gene phylogenies in the Ensembl database (3,394) are inconsistent with their syntenic contexts. For 70% of these gene families (2,387), we were able to propose an improved phylogenetic tree consistent with both the molecular substitution distances and the local syntenic information. We show that these synteny-guided phylogenies are more congruent with the species tree, with sequence evolution and with expected expression conservation patterns than those produced by state-of-the-art methods. Finally, we show that synteny-guided gene trees emphasize contributions of WGD paralogs to evolutionary innovations in the teleost clade.",
+                "authors": [
+                    {
+                        "name": "Berthelot C."
+                    },
+                    {
+                        "name": "Cabau C."
+                    },
+                    {
+                        "name": "Guiguen Y."
+                    },
+                    {
+                        "name": "Louis A."
+                    },
+                    {
+                        "name": "Parey E."
+                    },
+                    {
+                        "name": "Roest Crollius H."
+                    }
+                ],
+                "citationCount": 15,
+                "date": "2020-11-01T00:00:00Z",
+                "journal": "Molecular Biology and Evolution",
+                "title": "Synteny-guided resolution of gene trees clarifies the functional impact of whole-genome duplications"
+            },
+            "pmid": "32556216",
+            "type": [
+                "Primary"
+            ]
+        },
+        {
+            "doi": "10.1007/978-1-0716-2561-3_8",
+            "metadata": {
+                "abstract": "Phylogenetic gene trees recapitulate the evolutionary history of genes across species, forming an essential framework for comparative genomic studies. In particular, within the context of whole-genome duplications (WGDs), they serve as a basis to investigate patterns of duplicate gene retention and loss, timing of genome rediploidization, and, more generally, to explore the functional consequences of the duplication in descending species. Yet, despite ever more sophisticated models to describe the evolution of gene sequences, building accurate gene trees remains a challenge in ancient polyploid taxons. WGDs generate complex gene families with many duplicated copies and recurrent gene losses, which complicate this task even more. Here, we describe how to use SCORPiOs, a novel method that leverages synteny conservation to provide more accurate phylogenies in the presence of a known WGD event.",
+                "authors": [
+                    {
+                        "name": "Berthelot C."
+                    },
+                    {
+                        "name": "Parey E."
+                    },
+                    {
+                        "name": "Roest Crollius H."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Methods in Molecular Biology",
+                "title": "SCORPiOs, a Novel Method to Reconstruct Gene Phylogenies in the Context of a Known WGD Event"
+            },
+            "type": [
+                "Method"
+            ]
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        }
+    ],
+    "version": [
+        "2.0"
+    ]
+}
diff --git a/data/screenwerk/screenwerk.biotools.json b/data/screenwerk/screenwerk.biotools.json
new file mode 100644
index 0000000000000..42e4dfc2ab63b
--- /dev/null
+++ b/data/screenwerk/screenwerk.biotools.json
@@ -0,0 +1,129 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T19:46:42.242089Z",
+    "biotoolsCURIE": "biotools:screenwerk",
+    "biotoolsID": "screenwerk",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jorrit.enserink@ibv.uio.no",
+            "name": "Jorrit M Enserink",
+            "orcidid": "https://orcid.org/0000-0002-2394-5387",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Eivind Hovig"
+        },
+        {
+            "name": "Manuela Zucknick"
+        },
+        {
+            "name": "Robert Hanes"
+        }
+    ],
+    "description": "A modular tool for the design and analysis of drug combination screens.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Enserink-lab/screenwerk",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-13T19:46:42.244492Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Galaxy service"
+            ],
+            "url": "https://oncotools.elixir.no"
+        }
+    ],
+    "name": "screenwerk",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC840",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data and generate data files that can be analyzed by synergy-finding bioinformatics applications. RESULTS: screenwerk is an open-source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. screenwerk is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. screenwerk is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control and analyze, report and visualize the results of drug sensitivity screens. AVAILABILITY AND IMPLEMENTATION: The R-package and technical documentation is available at https://github.com/Enserink-lab/screenwerk; the R source code is publicly available at https://github.com/Enserink-lab/screenwerk under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy. Selected modules are available through Galaxy, an open-source platform for FAIR data analysis at https://oncotools.elixir.no. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Ayuda-Duran P."
+                    },
+                    {
+                        "name": "Enserink J.M."
+                    },
+                    {
+                        "name": "Hanes R."
+                    },
+                    {
+                        "name": "Hovig E."
+                    },
+                    {
+                        "name": "Nakken S."
+                    },
+                    {
+                        "name": "Ronneberg L."
+                    },
+                    {
+                        "name": "Zucknick M."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "screenwerk: a modular tool for the design and analysis of drug combination screens"
+            },
+            "pmcid": "PMC9825784",
+            "pmid": "36573326"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/scriptella/scriptella.biotools.json b/data/scriptella/scriptella.biotools.json
new file mode 100644
index 0000000000000..ec84ba366deb5
--- /dev/null
+++ b/data/scriptella/scriptella.biotools.json
@@ -0,0 +1,70 @@
+{
+    "additionDate": "2023-01-26T09:36:09.265276Z",
+    "biotoolsCURIE": "biotools:scriptella",
+    "biotoolsID": "scriptella",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "Fyodor Kupolov",
+            "url": "https://www.linkedin.com/in/kupolov?original_referer=https%3A%2F%2Fscriptella.org%2F"
+        }
+    ],
+    "description": "Scriptella is an open source ETL (Extract-Transform-Load) and script execution tool written in Java.\n\nOur primary focus is simplicity. You don't have to study yet another complex XML-based language - use SQL (or other scripting language suitable for the data source) to perform required transformations.",
+    "documentation": [
+        {
+            "type": [
+                "API documentation"
+            ],
+            "url": "https://scriptella.org/docs/api/index.html"
+        },
+        {
+            "type": [
+                "Installation instructions"
+            ],
+            "url": "https://scriptella.org/tutorial.html"
+        },
+        {
+            "type": [
+                "Release notes"
+            ],
+            "url": "https://scriptella.org/reference/index.html"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://scriptella.org/download.html"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://scriptella.org/index.html",
+    "lastUpdate": "2023-02-01T12:14:48.753733Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Technical monitoring"
+            ],
+            "url": "https://scriptella.org/reference/index.html"
+        }
+    ],
+    "name": "Scriptella",
+    "owner": "iacs-biocomputacion",
+    "version": [
+        "1.2"
+    ]
+}
diff --git a/data/scrnax/scrnax.biotools.json b/data/scrnax/scrnax.biotools.json
index b5376cb060830..ce9b736cd548b 100644
--- a/data/scrnax/scrnax.biotools.json
+++ b/data/scrnax/scrnax.biotools.json
@@ -1,15 +1,30 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2022-03-18T17:09:46.764239Z",
     "biotoolsCURIE": "biotools:scrnax",
     "biotoolsID": "scrnax",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "ScRNAX: cross-species transfer of high quality 3’UTR annotation for single cell RNA-Seq",
     "editPermission": {
         "type": "private"
     },
     "homepage": "https://github.com/bi-compbio/scrnax/",
-    "lastUpdate": "2022-12-09T22:11:56.323735Z",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-13T01:19:41.649532Z",
+    "license": "MIT",
     "name": "scRNAx",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "holgerklein",
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "Cell biology",
diff --git a/data/scshapes/scshapes.biotools.json b/data/scshapes/scshapes.biotools.json
index a8114a8974535..780b112d7a2e2 100644
--- a/data/scshapes/scshapes.biotools.json
+++ b/data/scshapes/scshapes.biotools.json
@@ -1,7 +1,10 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2022-10-07T06:57:09.401426Z",
     "biotoolsCURIE": "biotools:scshapes",
     "biotoolsID": "scshapes",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "A novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). Each gene is modelled independently under each treatment condition using the error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion and likelihood ratio test statistic.",
     "editPermission": {
         "type": "private"
@@ -17,9 +20,21 @@
         }
     ],
     "homepage": "https://github.com/Malindrie/scShapes",
-    "lastUpdate": "2022-12-09T22:33:59.089443Z",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-13T01:16:55.081986Z",
+    "license": "GPL-3.0",
     "name": "scShapes",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "Malindrie",
+    "toolType": [
+        "Library"
+    ],
     "topic": [
         {
             "term": "Gene expression",
diff --git a/data/scthi/scthi.biotools.json b/data/scthi/scthi.biotools.json
index 5e346f22a00af..07ef436cf95c4 100644
--- a/data/scthi/scthi.biotools.json
+++ b/data/scthi/scthi.biotools.json
@@ -1,7 +1,10 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2020-08-07T12:40:18Z",
     "biotoolsCURIE": "biotools:scthi",
     "biotoolsID": "scthi",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "scTHI is an R/Bioconductor package to identify active pairs of ligand-receptors from single cells in order to study,among others, tumor-host interactions. scTHI contains a set of signatures to classify cells from the tumor microenvironment.",
     "documentation": [
         {
@@ -10,15 +13,41 @@
                 "General"
             ],
             "url": "https://bioconductor.org/packages/release/bioc/vignettes/scTHI/inst/doc/vignette.html"
+        },
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://bioconductor.org/packages/release/bioc/manuals/scTHI/man/scTHI.pdf"
         }
     ],
     "editPermission": {
         "type": "private"
     },
     "homepage": "https://bioconductor.org/packages/release/bioc/html/scTHI.html",
-    "lastUpdate": "2022-12-09T22:39:13.010917Z",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-13T01:14:19.381642Z",
+    "license": "GPL-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/miccec/scTHI"
+        }
+    ],
     "name": "scTHI",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "michele.ceccarelli",
+    "toolType": [
+        "Library"
+    ],
     "topic": [
         {
             "term": "Cell biology",
diff --git a/data/scwecta/scwecta.biotools.json b/data/scwecta/scwecta.biotools.json
new file mode 100644
index 0000000000000..b717d2bf8e40d
--- /dev/null
+++ b/data/scwecta/scwecta.biotools.json
@@ -0,0 +1,97 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T19:34:01.772755Z",
+    "biotoolsCURIE": "biotools:scwecta",
+    "biotoolsID": "scwecta",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Guohua Wang"
+        },
+        {
+            "name": "Qiaoming Liu"
+        },
+        {
+            "name": "Shan Huang"
+        },
+        {
+            "name": "Tongtong Ren",
+            "orcidid": "https://orcid.org/0000-0002-2914-5233"
+        }
+    ],
+    "description": "A weighted ensemble classification framework for cell type assignment based on single cell transcriptome.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ttren-sc/scWECTA",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T19:34:01.775477Z",
+    "license": "Not licensed",
+    "name": "scWECTA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106409",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdRapid advances in single-cell transcriptome analysis provide deeper insights into the study of tissue heterogeneity at the cellular level. Unsupervised clustering can identify potential cell populations in single-cell RNA-sequencing (scRNA-seq) data, but fail to further determine the identity of each cell. Existing automatic annotation methods using scRNA-seq data based on machine learning mainly use single feature set and single classifier. In view of this, we propose a Weighted Ensemble classification framework for Cell Type Annotation, named scWECTA, which improves the accuracy of cell type identification. scWECTA uses five informative gene sets and integrates five classifiers based on soft weighted ensemble framework. And the ensemble weights are inferred through the constrained non-negative least squares. Validated on multiple pairs of scRNA-seq datasets, scWECTA is able to accurately annotate scRNA-seq data across platforms and across tissues, especially for imbalanced data containing rare cell types. Moreover, scWECTA outperforms other comparable methods in balancing the prediction accuracy of common cell types and the unassigned rate of non-common cell types at the same time. The source code of scWECTA is freely available at https://github.com/ttren-sc/scWECTA.",
+                "authors": [
+                    {
+                        "name": "Huang S."
+                    },
+                    {
+                        "name": "Liu Q."
+                    },
+                    {
+                        "name": "Ren T."
+                    },
+                    {
+                        "name": "Wang G."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "scWECTA: A weighted ensemble classification framework for cell type assignment based on single cell transcriptome"
+            },
+            "pmid": "36512878"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/sdprx/sdprx.biotools.json b/data/sdprx/sdprx.biotools.json
new file mode 100644
index 0000000000000..e80cb6059a063
--- /dev/null
+++ b/data/sdprx/sdprx.biotools.json
@@ -0,0 +1,90 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T18:08:06.498561Z",
+    "biotoolsCURIE": "biotools:sdprx",
+    "biotoolsID": "sdprx",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Geyu Zhou"
+        },
+        {
+            "name": "Hongyu Zhao"
+        },
+        {
+            "name": "Tianqi Chen"
+        }
+    ],
+    "description": "A statistical method for cross-population prediction of complex traits.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genetic mapping",
+                    "uri": "http://edamontology.org/operation_0282"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/eldronzhou/SDPRX",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T18:08:06.500997Z",
+    "license": "MIT",
+    "name": "SDPRX",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.AJHG.2022.11.007",
+            "metadata": {
+                "abstract": "© 2022 American Society of Human GeneticsPolygenic risk score (PRS) has demonstrated its great utility in biomedical research through identifying high-risk individuals for different diseases from their genotypes. However, the broader application of PRS to the general population is hindered by the limited transferability of PRS developed in Europeans to non-European populations. To improve PRS prediction accuracy in non-European populations, we develop a statistical method called SDPRX that can effectively integrate genome wide association study summary statistics from different populations. SDPRX automatically adjusts for linkage disequilibrium differences between populations and characterizes the joint distribution of the effect sizes of a variant in two populations to be both null, population specific, or shared with correlation. Through simulations and applications to real traits, we show that SDPRX improves the prediction performance over existing methods in non-European populations.",
+                "authors": [
+                    {
+                        "name": "Chen T."
+                    },
+                    {
+                        "name": "Zhao H."
+                    },
+                    {
+                        "name": "Zhou G."
+                    }
+                ],
+                "date": "2023-01-05T00:00:00Z",
+                "journal": "American Journal of Human Genetics",
+                "title": "SDPRX: A statistical method for cross-population prediction of complex traits"
+            },
+            "pmid": "36460009"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        }
+    ]
+}
diff --git a/data/sdypools/sdypools.biotools.json b/data/sdypools/sdypools.biotools.json
deleted file mode 100644
index 9a84b4eabd23e..0000000000000
--- a/data/sdypools/sdypools.biotools.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-    "additionDate": "2021-08-29T04:44:20Z",
-    "biotoolsCURIE": "biotools:sdypools",
-    "biotoolsID": "sdypools",
-    "description": "Sdy Pools adalah situs yang menampilkan hasil togel sdy 4D tercepat dan paling akurat. Keluaran angka 4D Sydney diambil langsung dari website resminya, jadi angkanya tidak berbeda dengan website resminya. Wla TOP SDY ini dibuat untuk mempermudah para pemain togel Sydney Indonesia dengan menampilkan Live Draw Sydney dari berbagai sumber terpercaya, cepat dan tepat waktu.",
-    "editPermission": {
-        "type": "private"
-    },
-    "homepage": "https://rebrand.ly/daftar-papua4d",
-    "lastUpdate": "2022-08-04T04:50:59.687185Z",
-    "name": "Sdypools - Sdy Pools - Data Sdy - Keluaran Sdy - Togel Sdy",
-    "owner": "admin"
-}
diff --git a/data/secret6/secret6.biotools.json b/data/secret6/secret6.biotools.json
new file mode 100644
index 0000000000000..b1c6b9409858a
--- /dev/null
+++ b/data/secret6/secret6.biotools.json
@@ -0,0 +1,117 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T02:19:06.848679Z",
+    "biotoolsCURIE": "biotools:secret6",
+    "biotoolsID": "secret6",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "hyou@sjtu.edu.cn",
+            "name": "Hong-Yu Ou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jiahao Guan"
+        },
+        {
+            "name": "Jianfeng Zhang"
+        },
+        {
+            "name": "Zhaoyan Chen",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A comprehensive resource of bacterial Type VI Secretion Systems.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://bioinfo-mml.sjtu.edu.cn/SecReT6/",
+    "lastUpdate": "2023-01-26T02:19:06.851582Z",
+    "name": "SecReT6",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1007/S11427-022-2172-X",
+            "metadata": {
+                "abstract": "© 2022, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature.Type VI Secretion System (T6SS) plays significant roles in microbial activities via injecting effectors into adjacent cells or environments. T6SS increasingly gained attention due to its important influence on pathogenesis, microbial competition, etc. T6SS-associated research is explosively expanding on numerous grounds that call for an efficient resource. The SecReT6 version 3 provides comprehensive information on T6SS and the interactions between T6SS and T6SS-related proteins such as T6SS regulators and T6SS effectors. To assist T6SS researches like microbial competition and regulatory mechanisms, SecReT6 v3 developed online tools for detection and analysis of T6SS and T6SS-related proteins and estimation of T6SS-dependent killing risk. We have identified a novel T6SS regulator and T6SS-dependent killing capacity in Acinetobacter baumannii clinical isolates with the aid of SecReT6 v3. 17,212 T6SSs and plentiful T6SS-related proteins in 26,573 bacterial complete genomes were also detected, analyzed and incorporated into the database. The database is freely available at https://bioinfo-mml.sjtu.edu.cn/SecReT6/.",
+                "authors": [
+                    {
+                        "name": "Chen Z."
+                    },
+                    {
+                        "name": "Deng Z."
+                    },
+                    {
+                        "name": "Djordjevic M."
+                    },
+                    {
+                        "name": "Guan J."
+                    },
+                    {
+                        "name": "Li G."
+                    },
+                    {
+                        "name": "Ou H.-Y."
+                    },
+                    {
+                        "name": "Tai C."
+                    },
+                    {
+                        "name": "Wang H."
+                    },
+                    {
+                        "name": "Wang M."
+                    },
+                    {
+                        "name": "Zhang J."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Science China Life Sciences",
+                "title": "SecReT6 update: a comprehensive resource of bacterial Type VI Secretion Systems"
+            },
+            "pmid": "36346548"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Proteins",
+            "uri": "http://edamontology.org/topic_0078"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/secuer/secuer.biotools.json b/data/secuer/secuer.biotools.json
new file mode 100644
index 0000000000000..d6c06c604a82d
--- /dev/null
+++ b/data/secuer/secuer.biotools.json
@@ -0,0 +1,115 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T18:02:29.486776Z",
+    "biotoolsCURIE": "biotools:secuer",
+    "biotoolsID": "secuer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "linliu@sjtu.edu.cn",
+            "name": "Lin Liu",
+            "orcidid": "https://orcid.org/0000-0002-9883-7962",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "xqzheng@shsmu.edu.cn",
+            "name": "Xiaoqi Zheng",
+            "orcidid": "https://orcid.org/0000-0002-1832-4404",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "hjwu@pku.edu.cn",
+            "name": "Hua-Jun Wu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Nana Wei"
+        },
+        {
+            "name": "Yating Nie"
+        }
+    ],
+    "description": "Ultrafast, scalable and accurate clustering of single-cell RNA-seq data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/nanawei11/Secuer",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T18:02:29.489448Z",
+    "license": "MIT",
+    "name": "Secuer",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PCBI.1010753",
+            "metadata": {
+                "abstract": "Copyright: © 2022 Wei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License,Identifying cell clusters is a critical step for single-cell transcriptomics study. Despite the numerous clustering tools developed recently, the rapid growth of scRNA-seq volumes prompts for a more (computationally) efficient clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite graph representation algorithm, Secuer enjoys reduced runtime and memory usage over one order of magnitude for datasets with more than 1 million cells. Meanwhile, Secuer also achieves better or comparable accuracy than competing methods in small and moderate benchmark datasets. Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy. Overall, Secuer is a versatile, accurate, and scalable clustering framework suitable for small to ultra-large single-cell clustering tasks.",
+                "authors": [
+                    {
+                        "name": "Liu L."
+                    },
+                    {
+                        "name": "Nie Y."
+                    },
+                    {
+                        "name": "Wei N."
+                    },
+                    {
+                        "name": "Wu H.-J."
+                    },
+                    {
+                        "name": "Zheng X."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data"
+            },
+            "pmcid": "PMC9754601",
+            "pmid": "36469543"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/sedb_2.0/sedb_2.0.biotools.json b/data/sedb_2.0/sedb_2.0.biotools.json
new file mode 100644
index 0000000000000..14f4b5f4d477f
--- /dev/null
+++ b/data/sedb_2.0/sedb_2.0.biotools.json
@@ -0,0 +1,147 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T02:13:59.893429Z",
+    "biotoolsCURIE": "biotools:sedb_2.0",
+    "biotoolsID": "sedb_2.0",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "lcqbio@163.com",
+            "name": "Chunquan Li",
+            "orcidid": "https://orcid.org/0000-0002-4700-5496",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Chao Song"
+        },
+        {
+            "name": "Qiuyu Wang"
+        },
+        {
+            "name": "Yuezhu Wang"
+        }
+    ],
+    "description": "A comprehensive super-enhancer database of human and mouse.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Peak calling",
+                    "uri": "http://edamontology.org/operation_3222"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.licpathway.net/sedb",
+    "lastUpdate": "2023-01-26T02:13:59.896121Z",
+    "name": "SEdb 2.0",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC968",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Super-enhancers (SEs) are cell-specific DNA cis-regulatory elements that can supervise the transcriptional regulation processes of downstream genes. SEdb 2.0 (http://www.licpathway.net/sedb) aims to provide a comprehensive SE resource and annotate their potential roles in gene transcriptions. Compared with SEdb 1.0, we have made the following improvements: (i) Newly added the mouse SEs and expanded the scale of human SEs. SEdb 2.0 contained 1 167 518 SEs from 1739 human H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq) samples and 550 226 SEs from 931 mouse H3K27ac ChIP-seq samples, which was five times that of SEdb 1.0. (ii) Newly added transcription factor binding sites (TFBSs) in SEs identified by TF motifs and TF ChIP-seq data. (iii) Added comprehensive (epi)genetic annotations of SEs, including chromatin accessibility regions, methylation sites, chromatin interaction regions and topologically associating domains (TADs). (iv) Newly embedded and updated search and analysis tools, including 'Search SE by TF-based', 'Differential-Overlapping-SE analysis' and 'SE-based TF-Gene analysis'. (v) Newly provided quality control (QC) metrics for ChIP-seq processing. In summary, SEdb 2.0 is a comprehensive update of SEdb 1.0, which curates more SEs and annotation information than SEdb 1.0. SEdb 2.0 provides a friendly platform for researchers to more comprehensively clarify the important role of SEs in the biological process.",
+                "authors": [
+                    {
+                        "name": "Ai B."
+                    },
+                    {
+                        "name": "Bai X."
+                    },
+                    {
+                        "name": "Feng C."
+                    },
+                    {
+                        "name": "Li C."
+                    },
+                    {
+                        "name": "Liu X."
+                    },
+                    {
+                        "name": "Qian F."
+                    },
+                    {
+                        "name": "Song C."
+                    },
+                    {
+                        "name": "Wang F."
+                    },
+                    {
+                        "name": "Wang Q."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Zhang G."
+                    },
+                    {
+                        "name": "Zhang J."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zhao J."
+                    },
+                    {
+                        "name": "Zhao X."
+                    },
+                    {
+                        "name": "Zhou L."
+                    },
+                    {
+                        "name": "Zhu J."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "SEdb 2.0: a comprehensive super-enhancer database of human and mouse"
+            },
+            "pmcid": "PMC9825585",
+            "pmid": "36318264"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "Epigenetics",
+            "uri": "http://edamontology.org/topic_3295"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Methylated DNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3674"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/seesawpred/seesawpred.biotools.json b/data/seesawpred/seesawpred.biotools.json
new file mode 100644
index 0000000000000..32c154fb81f41
--- /dev/null
+++ b/data/seesawpred/seesawpred.biotools.json
@@ -0,0 +1,149 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-11T11:17:03.217970Z",
+    "biotoolsCURIE": "biotools:seesawpred",
+    "biotoolsID": "seesawpred",
+    "collectionID": [
+        "LCSB-CBG"
+    ],
+    "credit": [
+        {
+            "email": "antonio.delsol@uni.lu",
+            "name": "Antonio del Sol",
+            "note": "Group leader, Computational Biology group, Luxembourg Centre for Systems Biomedicine \nFull professor / Chief scientist 1 in Bioinformatics at University of Luxembourg",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ],
+            "url": "https://wwwen.uni.lu/lcsb/people/antonio_del_sol_mesa"
+        }
+    ],
+    "description": "A Web Application for Predicting Cell-fate Determinants in Cell Differentiation",
+    "documentation": [
+        {
+            "note": "Help section on the webpage contains the documentation and T&C..",
+            "type": [
+                "User manual"
+            ],
+            "url": "http://seesaw.lcsb.uni.lu"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://git-r3lab.uni.lu/andras.hartmann/seesaw"
+        }
+    ],
+    "editPermission": {
+        "type": "group"
+    },
+    "elixirNode": [
+        "Luxembourg"
+    ],
+    "elixirPlatform": [
+        "Tools"
+    ],
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Expression data",
+                        "uri": "http://edamontology.org/data_2603"
+                    },
+                    "format": [
+                        {
+                            "term": "CSV",
+                            "uri": "http://edamontology.org/format_3752"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Gene regulatory network prediction",
+                    "uri": "http://edamontology.org/operation_2437"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Gene symbol",
+                        "uri": "http://edamontology.org/data_1026"
+                    },
+                    "format": [
+                        {
+                            "term": "scores format",
+                            "uri": "http://edamontology.org/format_1999"
+                        }
+                    ]
+                }
+            ]
+        }
+    ],
+    "homepage": "http://seesaw.lcsb.uni.lu",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-11T11:17:03.220473Z",
+    "license": "AGPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Service"
+            ],
+            "url": "http://seesaw.lcsb.uni.lu"
+        }
+    ],
+    "name": "SeesawPred",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "KartikeyaS",
+    "publication": [
+        {
+            "doi": "10.1038/s41598-018-31688-9",
+            "metadata": {
+                "abstract": "© 2018, The Author(s).Cellular differentiation is a complex process where a less specialized cell evolves into a more specialized cell. Despite the increasing research effort, identification of cell-fate determinants (transcription factors (TFs) determining cell fates during differentiation) still remains a challenge, especially when closely related cell types from a common progenitor are considered. Here, we develop SeesawPred, a web application that, based on a gene regulatory network (GRN) model of cell differentiation, can computationally predict cell-fate determinants from transcriptomics data. Unlike previous approaches, it allows the user to upload gene expression data and does not rely on pre-compiled reference data sets, enabling its application to novel differentiation systems. SeesawPred correctly predicted known cell-fate determinants on various cell differentiation examples in both mouse and human, and also performed better compared to state-of-the-art methods. The application is freely available for academic, non-profit use at http://seesaw.lcsb.uni.lu.",
+                "authors": [
+                    {
+                        "name": "Hartmann A."
+                    },
+                    {
+                        "name": "Okawa S."
+                    },
+                    {
+                        "name": "Zaffaroni G."
+                    },
+                    {
+                        "name": "del Sol A."
+                    }
+                ],
+                "citationCount": 5,
+                "date": "2018-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "SeesawPred: A Web Application for Predicting Cell-fate Determinants in Cell Differentiation"
+            },
+            "pmcid": "PMC6127256",
+            "pmid": "30190516",
+            "type": [
+                "Primary"
+            ],
+            "version": "0.5.1"
+        }
+    ],
+    "toolType": [
+        "Bioinformatics portal"
+    ],
+    "topic": [
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ],
+    "version": [
+        "0.5.1"
+    ]
+}
diff --git a/data/segcond/segcond.biotools.json b/data/segcond/segcond.biotools.json
new file mode 100644
index 0000000000000..21db8fd6c31c4
--- /dev/null
+++ b/data/segcond/segcond.biotools.json
@@ -0,0 +1,113 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T02:09:53.623238Z",
+    "biotoolsCURIE": "biotools:segcond",
+    "biotoolsID": "segcond",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "cnikolaou@fleming.gr",
+            "name": "Christoforos Nikolaou",
+            "orcidid": "https://orcid.org/0000-0003-3001-6786",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "thomas.graf@crg.eu",
+            "name": "Thomas Graf",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Antonios Klonizakis",
+            "orcidid": "https://orcid.org/0000-0001-9850-6971"
+        }
+    ],
+    "description": "SEGCOND predicts putative transcriptional condensate-associated genomic regions by integrating multi-omics data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Peak calling",
+                    "uri": "http://edamontology.org/operation_3222"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/AntonisK95/SEGCOND",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-26T02:09:53.626113Z",
+    "license": "Not licensed",
+    "name": "SEGCOND",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC742",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The compartmentalization of biochemical reactions, involved in the activation of gene expression in the eukaryotic nucleus, leads to the formation of membraneless bodies through liquid-liquid phase separation. These formations, called transcriptional condensates, appear to play important roles in gene regulation as they are assembled through the association of multiple enhancer regions in 3D genomic space. To date, we are still lacking efficient computational methodologies to identify the regions responsible for the formation of such condensates, based on genomic and conformational data. RESULTS: In this work, we present SEGCOND, a computational framework aiming to highlight genomic regions involved in the formation of transcriptional condensates. SEGCOND is flexible in combining multiple genomic datasets related to enhancer activity and chromatin accessibility, to perform a genome segmentation. It then uses this segmentation for the detection of highly transcriptionally active regions of the genome. At a final step, and through the integration of Hi-C data, it identifies regions of putative transcriptional condensates (PTCs) as genomic domains where multiple enhancer elements coalesce in 3D space. SEGCOND identifies a subset of enhancer segments with increased transcriptional activity. PTCs are also found to significantly overlap highly interconnected enhancer elements and super enhancers obtained through two independent approaches. Application of SEGCOND on data from a well-defined system of B-cell to macrophage transdifferentiation leads to the identification of previously unreported genes with a likely role in the process. AVAILABILITY AND IMPLEMENTATION: Source code and details for the implementation of SEGCOND is available at https://github.com/AntonisK95/SEGCOND. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Graf T."
+                    },
+                    {
+                        "name": "Klonizakis A."
+                    },
+                    {
+                        "name": "Nikolaou C."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "SEGCOND predicts putative transcriptional condensate-associated genomic regions by integrating multi-omics data"
+            },
+            "pmcid": "PMC9805567",
+            "pmid": "36394233"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "ChIP-seq",
+            "uri": "http://edamontology.org/topic_3169"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/sema_web/sema_web.biotools.json b/data/sema_web/sema_web.biotools.json
new file mode 100644
index 0000000000000..2eee63da57838
--- /dev/null
+++ b/data/sema_web/sema_web.biotools.json
@@ -0,0 +1,141 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T10:51:18.848452Z",
+    "biotoolsCURIE": "biotools:sema_web",
+    "biotoolsID": "sema_web",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ivanisenko@airi.net",
+            "name": "Nikita V. Ivanisenko",
+            "orcidid": "http://orcid.org/0000-0002-0333-8117",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Marina N. Asatryan",
+            "orcidid": "http://orcid.org/0000-0001-6273-8615"
+        },
+        {
+            "name": "Olga L. Kardymon",
+            "orcidid": "http://orcid.org/0000-0002-4827-8891"
+        },
+        {
+            "name": "Tatiana I. Shashkova",
+            "orcidid": "http://orcid.org/0000-0002-8754-8727"
+        }
+    ],
+    "description": "Antigen B-cell conformational epitope prediction using deep transfer learning.'",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                },
+                {
+                    "term": "Residue contact prediction",
+                    "uri": "http://edamontology.org/operation_0272"
+                },
+                {
+                    "term": "Side chain modelling",
+                    "uri": "http://edamontology.org/operation_0480"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://sema.airi.net",
+    "lastUpdate": "2023-02-26T10:51:18.851149Z",
+    "name": "SEMA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/fimmu.2022.960985",
+            "metadata": {
+                "abstract": "Copyright © 2022 Shashkova, Umerenkov, Salnikov, Strashnov, Konstantinova, Lebed, Shcherbinin, Asatryan, Kardymon and Ivanisenko.One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1v, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the SARS-CoV-2 RBD domain. SEMA is available at https://github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.",
+                "authors": [
+                    {
+                        "name": "Asatryan M.N."
+                    },
+                    {
+                        "name": "Ivanisenko N.V."
+                    },
+                    {
+                        "name": "Kardymon O.L."
+                    },
+                    {
+                        "name": "Konstantinova A.V."
+                    },
+                    {
+                        "name": "Lebed I."
+                    },
+                    {
+                        "name": "Salnikov M."
+                    },
+                    {
+                        "name": "Shashkova T.I."
+                    },
+                    {
+                        "name": "Shcherbinin D.N."
+                    },
+                    {
+                        "name": "Strashnov P.V."
+                    },
+                    {
+                        "name": "Umerenkov D."
+                    }
+                ],
+                "date": "2022-09-15T00:00:00Z",
+                "journal": "Frontiers in Immunology",
+                "title": "SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning"
+            },
+            "pmcid": "PMC9523212",
+            "pmid": "36189325"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Allergy, clinical immunology and immunotherapeutics",
+            "uri": "http://edamontology.org/topic_3400"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein folding, stability and design",
+            "uri": "http://edamontology.org/topic_0130"
+        },
+        {
+            "term": "Vaccinology",
+            "uri": "http://edamontology.org/topic_3966"
+        }
+    ]
+}
diff --git a/data/sensdeep/sensdeep.biotools.json b/data/sensdeep/sensdeep.biotools.json
new file mode 100644
index 0000000000000..def4494a0148f
--- /dev/null
+++ b/data/sensdeep/sensdeep.biotools.json
@@ -0,0 +1,93 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-26T02:05:31.475404Z",
+    "biotoolsCURIE": "biotools:sensdeep",
+    "biotoolsID": "sensdeep",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "enginaybey@gmail.com",
+            "name": "Engin Aybey",
+            "orcidid": "http://orcid.org/0000-0002-1780-262X",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "ozgur.gumus@ege.edu.tr",
+            "name": "Özgür Gümüş",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "An Ensemble Deep Learning Method for Protein-Protein Interaction Sites Prediction.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein interaction prediction",
+                    "uri": "http://edamontology.org/operation_2492"
+                },
+                {
+                    "term": "Protein secondary structure prediction",
+                    "uri": "http://edamontology.org/operation_0267"
+                },
+                {
+                    "term": "Protein-protein binding site prediction",
+                    "uri": "http://edamontology.org/operation_2464"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/enginaybey/SENSDeep",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-26T02:05:31.479552Z",
+    "license": "Not licensed",
+    "name": "SENSDeep",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1007/S12539-022-00543-X",
+            "metadata": {
+                "abstract": "© 2022, International Association of Scientists in the Interdisciplinary Areas.Purpose: The determination of which amino acid in a protein interacts with other proteins is important in understanding the functional mechanism of that protein. Although there are experimental methods to detect protein–protein interaction sites (PPISs), these are costly, time-consuming, and require expertise. Therefore, many computational methods have been proposed to accelerate this type of research, but they are generally insufficient to predict PPISs accurately. There is a need for development in this field. Methods: In this study, we introduce a new PPISs prediction method. This method is a sequence-based Stacking ENSemble Deep (SENSDeep) learning method that has an ensemble learning model including the models of RNN, CNN, GRU sequence to sequence (GRUs2s), GRU sequence to sequence with an attention layer (GRUs2satt) and a multilayer perceptron. Two embedded features, secondary structure, and protein sequence information are added to the training data set in addition to twelve existing features to improve the prediction performance of the method. Results: SENSDeep trained on the training data set without two extra features obtains a better performance on some of the independent testing data sets than that of the other methods in the literature, especially on scoring metrics of sensitivity, F1, MCC, and AUPRC, having increments up to 63.5%, 19.3%, 18.5%, 11.4%, respectively. It is shown that the added extra features improve the performance of the method by having almost the same performance with less data as the method trained on the data set without these added features. On the other hand, different sizes of the sliding window are tried on the data sets and an optimal sliding window size for SENSDeep is found. Moreover, SENSDeep has also been compared to structure-based methods. Some of these methods have been found to perform better. Using SENSDeep obtained by training with both training data sets, PPISs prediction examples of various proteins that are not in these training data sets are also presented. Furthermore, execution times for SENSDeep and its submodels are shown. Availability and implementation: https://github.com/enginaybey/SENSDeep.",
+                "authors": [
+                    {
+                        "name": "Aybey E."
+                    },
+                    {
+                        "name": "Gumus O."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Interdisciplinary Sciences – Computational Life Sciences",
+                "title": "SENSDeep: An Ensemble Deep Learning Method for Protein–Protein Interaction Sites Prediction"
+            },
+            "pmid": "36346583"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein binding sites",
+            "uri": "http://edamontology.org/topic_3534"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        }
+    ]
+}
diff --git a/data/seqcp/seqcp.biotools.json b/data/seqcp/seqcp.biotools.json
new file mode 100644
index 0000000000000..ba6feedcc7b80
--- /dev/null
+++ b/data/seqcp/seqcp.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T17:52:10.878614Z",
+    "biotoolsCURIE": "biotools:seqcp",
+    "biotoolsID": "seqcp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "WadeLo@nycu.edu.tw",
+            "name": "Wei-Cheng Lo",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "pclyu@mx.nthu.edu.tw",
+            "name": "Ping-Chiang Lyu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Chi-Chun Chen"
+        },
+        {
+            "name": "Hsuan-Cheng Huang"
+        },
+        {
+            "name": "Yu-Wei Huang"
+        }
+    ],
+    "description": "A sequence-based algorithm for searching circularly permuted proteins.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Protein modelling",
+                    "uri": "http://edamontology.org/operation_0477"
+                },
+                {
+                    "term": "Protein structure validation",
+                    "uri": "http://edamontology.org/operation_0321"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://pcnas.life.nthu.edu.tw/SeqCP/",
+    "lastUpdate": "2023-02-13T17:52:10.881368Z",
+    "name": "SeqCP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.11.024",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsCircular permutation (CP) is a protein sequence rearrangement in which the amino- and carboxyl-termini of a protein can be created in different positions along the imaginary circularized sequence. Circularly permutated proteins usually exhibit conserved three-dimensional structures and functions. By comparing the structures of circular permutants (CPMs), protein research and bioengineering applications can be approached in ways that are difficult to achieve by traditional mutagenesis. Most current CP detection algorithms depend on structural information. Because there is a vast number of proteins with unknown structures, many CP pairs may remain unidentified. An efficient sequence-based CP detector will help identify more CP pairs and advance many protein studies. For instance, some hypothetical proteins may have CPMs with known functions and structures that are informative for functional annotation, but existing structure-based CP search methods cannot be applied when those hypothetical proteins lack structural information. Despite the considerable potential for applications, sequence-based CP search methods have not been well developed. We present a sequence-based method, SeqCP, which analyzes normal and duplicated sequence alignments to identify CPMs and determine candidate CP sites for proteins. SeqCP was trained by data obtained from the Circular Permutation Database and tested with nonredundant datasets from the Protein Data Bank. It shows high reliability in CP identification and achieves an AUC of 0.9. SeqCP has been implemented into a web server available at: http://pcnas.life.nthu.edu.tw/SeqCP/.",
+                "authors": [
+                    {
+                        "name": "Chen C.-C."
+                    },
+                    {
+                        "name": "Huang H.-C."
+                    },
+                    {
+                        "name": "Huang Y.-W."
+                    },
+                    {
+                        "name": "Lo W.-C."
+                    },
+                    {
+                        "name": "Lyu P.-C."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "SeqCP: A sequence-based algorithm for searching circularly permuted proteins"
+            },
+            "pmcid": "PMC9763678",
+            "pmid": "36582435"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Bioengineering",
+            "uri": "http://edamontology.org/topic_3398"
+        },
+        {
+            "term": "Protein modifications",
+            "uri": "http://edamontology.org/topic_0601"
+        },
+        {
+            "term": "Protein structure analysis",
+            "uri": "http://edamontology.org/topic_2814"
+        }
+    ]
+}
diff --git a/data/sesam/sesam.biotools.json b/data/sesam/sesam.biotools.json
new file mode 100644
index 0000000000000..5ebc18de3ea24
--- /dev/null
+++ b/data/sesam/sesam.biotools.json
@@ -0,0 +1,138 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T23:25:30.835941Z",
+    "biotoolsCURIE": "biotools:sesam",
+    "biotoolsID": "sesam",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "matthieu.falque@inrae.fr",
+            "name": "Matthieu Falque",
+            "orcidid": "https://orcid.org/0000-0002-6444-858X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Adrien Vidal",
+            "orcidid": "http://orcid.org/0000-0001-5139-6445"
+        },
+        {
+            "name": "Franck Gauthier",
+            "orcidid": "http://orcid.org/0000-0003-0574-065X"
+        },
+        {
+            "name": "Olivier C. Martin",
+            "orcidid": "http://orcid.org/0000-0002-5295-5963"
+        }
+    ],
+    "description": "The SeSAM R package allows fully automatic construction of two successive genetic maps.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genetic mapping",
+                    "uri": "http://edamontology.org/operation_0282"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Heat map generation",
+                    "uri": "http://edamontology.org/operation_0531"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                },
+                {
+                    "term": "Physical mapping",
+                    "uri": "http://edamontology.org/operation_2944"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://forgemia.inra.fr/gqe-acep/sesam",
+    "language": [
+        "C++",
+        "R"
+    ],
+    "lastUpdate": "2023-01-25T23:25:30.839002Z",
+    "license": "GPL-3.0",
+    "name": "SeSAM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05045-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Genotyping and sequencing technologies produce increasingly large numbers of genetic markers with potentially high rates of missing or erroneous data. Therefore, the construction of linkage maps is more and more complex. Moreover, the size of segregating populations remains constrained by cost issues and is less and less commensurate with the numbers of SNPs available. Thus, guaranteeing a statistically robust marker order requires that maps include only a carefully selected subset of SNPs. Results: In this context, the SeSAM software allows automatic genetic map construction using seriation and placement approaches, to produce (1) a high-robustness framework map which includes as many markers as possible while keeping the order robustness beyond a given statistical threshold, and (2) a high-density total map including the framework plus almost all polymorphic markers. During this process, care is taken to limit the impact of genotyping errors and of missing data on mapping quality. SeSAM can be used with a wide range of biparental populations including from outcrossing species for which phases are inferred on-the-fly by maximum-likelihood during map elongation. The package also includes functions to simulate data sets, convert data formats, detect putative genotyping errors, visualize data and map quality (including graphical genotypes), and merge several maps into a consensus. SeSAM is also suitable for interactive map construction, by providing lower-level functions for 2-point and multipoint EM analyses. The software is implemented in a R package including functions in C++. Conclusions: SeSAM is a fully automatic linkage mapping software designed to (1) produce a framework map as robust as desired by optimizing the selection of a subset of markers, and (2) produce a high-density map including almost all polymorphic markers. The software can be used with a wide range of biparental mapping populations including cases from outcrossing. SeSAM is freely available under a GNU GPL v3 license and works on Linux, Windows, and macOS platforms. It can be downloaded together with its user-manual and quick-start tutorial from ForgeMIA (SeSAM project) at https://forgemia.inra.fr/gqe-acep/sesam/-/releases.",
+                "authors": [
+                    {
+                        "name": "Chevrolier N."
+                    },
+                    {
+                        "name": "Falque M."
+                    },
+                    {
+                        "name": "Gauthier F."
+                    },
+                    {
+                        "name": "Guiglielmoni N."
+                    },
+                    {
+                        "name": "Jasson S."
+                    },
+                    {
+                        "name": "Leroux D."
+                    },
+                    {
+                        "name": "Martin O.C."
+                    },
+                    {
+                        "name": "Rodrigez W."
+                    },
+                    {
+                        "name": "Tourrette E."
+                    },
+                    {
+                        "name": "Vidal A."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "SeSAM: software for automatic construction of order-robust linkage maps"
+            },
+            "pmcid": "PMC9675223",
+            "pmid": "36402957"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/seth/seth.biotools.json b/data/seth/seth.biotools.json
new file mode 100644
index 0000000000000..6e3c5b8e57cdb
--- /dev/null
+++ b/data/seth/seth.biotools.json
@@ -0,0 +1,101 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T10:35:27.812003Z",
+    "biotoolsCURIE": "biotools:seth",
+    "biotoolsID": "seth",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mheinzinger@rostlab.org",
+            "name": "Michael Heinzinger,",
+            "orcidid": "http://orcid.org/0000-0002-9601-3580",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Dagmar Ilzhoefer"
+        },
+        {
+            "name": "Burkhard Rost",
+            "orcidid": "http://orcid.org/0000-0003-0179-8424",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "SETH predicts nuances of residue disorder from protein embeddings.",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://zenodo.org/record/6673817"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Protein disorder prediction",
+                    "uri": "http://edamontology.org/operation_3904"
+                },
+                {
+                    "term": "Protein secondary structure prediction (coils)",
+                    "uri": "http://edamontology.org/operation_0470"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/DagmarIlz/SETH",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T10:35:27.814681Z",
+    "license": "GPL-3.0",
+    "name": "SETH",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/fbinf.2022.1019597",
+            "pmcid": "PMC9580958",
+            "pmid": "36304335"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        },
+        {
+            "term": "Protein disordered structure",
+            "uri": "http://edamontology.org/topic_3538"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
+}
diff --git a/data/seth_1/seth_1.biotools.json b/data/seth_1/seth_1.biotools.json
index 03f463d5a2726..0290c272a2c27 100644
--- a/data/seth_1/seth_1.biotools.json
+++ b/data/seth_1/seth_1.biotools.json
@@ -3,11 +3,20 @@
     "additionDate": "2022-08-31T12:21:37.655267Z",
     "biotoolsCURIE": "biotools:seth_1",
     "biotoolsID": "seth_1",
+    "confidence_flag": "tool",
     "cost": "Free of charge",
     "credit": [
         {
             "email": "mheinzinger@rostlab.org",
-            "name": "Michael Heinzinger"
+            "name": "Michael Heinzinger",
+            "orcidid": "http://orcid.org/0000-0002-9601-3580"
+        },
+        {
+            "name": "Dagmar Ilzhoefer"
+        },
+        {
+            "name": "Burkhard Rost",
+            "orcidid": "http://orcid.org/0000-0003-0179-8424"
         }
     ],
     "description": "SETH is a novel method that predicts residue disorder from embeddings generated by the protein Language Model ProtT5, which explicitly only uses single sequences as input.",
@@ -40,7 +49,7 @@
     "language": [
         "Python"
     ],
-    "lastUpdate": "2022-12-09T22:41:16.630201Z",
+    "lastUpdate": "2023-01-13T01:07:49.788164Z",
     "license": "GPL-3.0",
     "link": [
         {
@@ -58,6 +67,8 @@
     "publication": [
         {
             "doi": "10.1101/2022.06.23.497276",
+            "pmcid": "PMC9580958",
+            "pmid": "36304335",
             "type": [
                 "Primary"
             ]
diff --git a/data/seurat/seurat.biotools.json b/data/seurat/seurat.biotools.json
index b789a870ab2bf..dc2ce7723a8c2 100644
--- a/data/seurat/seurat.biotools.json
+++ b/data/seurat/seurat.biotools.json
@@ -6,27 +6,39 @@
     "collectionID": [
         "PerMedCoE"
     ],
+    "confidence_flag": "tool",
     "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Andrew Butler",
+            "orcidid": "https://orcid.org/0000-0003-3608-0463"
+        },
+        {
+            "name": "Charlotte Darby",
+            "orcidid": "https://orcid.org/0000-0003-2195-5300"
+        },
+        {
+            "name": "Saket Choudhary",
+            "orcidid": "https://orcid.org/0000-0001-5202-7633"
+        },
+        {
+            "name": "Shiwei Zheng",
+            "orcidid": "https://orcid.org/0000-0001-6682-6743"
+        }
+    ],
     "description": "Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements, and to integrate diverse types of single-cell data.",
     "documentation": [
         {
             "type": [
-                "General"
+                "Installation instructions"
             ],
-            "url": "https://satijalab.org/seurat/"
+            "url": "https://satijalab.org/seurat/articles/install.html"
         },
         {
             "type": [
-                "Installation instructions"
+                "User manual"
             ],
-            "url": "https://satijalab.org/seurat/articles/install.html"
-        }
-    ],
-    "download": [
-        {
-            "type": "Source code",
-            "url": "https://github.com/satijalab/seurat/",
-            "version": "4.0"
+            "url": "https://cloud.r-project.org/web/packages/Seurat/Seurat.pdf"
         }
     ],
     "editPermission": {
@@ -36,8 +48,28 @@
     "language": [
         "R"
     ],
-    "lastUpdate": "2022-12-09T22:42:32.070365Z",
+    "lastUpdate": "2023-01-13T01:01:50.285126Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://cloud.r-project.org/web/packages/Seurat/index.html"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/satijalab/seurat"
+        }
+    ],
     "name": "Seurat",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "tntiniak",
     "publication": [
         {
@@ -121,13 +153,18 @@
                         "name": "Zheng S."
                     }
                 ],
-                "citationCount": 1006,
+                "citationCount": 1135,
                 "date": "2021-06-24T00:00:00Z",
                 "journal": "Cell",
                 "title": "Integrated analysis of multimodal single-cell data"
-            }
+            },
+            "pmcid": "PMC8238499",
+            "pmid": "34062119"
         }
     ],
+    "toolType": [
+        "Library"
+    ],
     "topic": [
         {
             "term": "RNA-Seq",
diff --git a/data/sgppools/sgppools.biotools.json b/data/sgppools/sgppools.biotools.json
deleted file mode 100644
index 1d243b3a50e13..0000000000000
--- a/data/sgppools/sgppools.biotools.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-    "additionDate": "2021-08-17T15:29:00Z",
-    "biotoolsCURIE": "biotools:sgppools",
-    "biotoolsID": "sgppools",
-    "description": "Sgppools adalah agen judi  togel online singapore 4D terbaik Indonesia dengan data keluaran prize 4d sgp pools online resmi melaui wla singapura. Link daftar bandar toto singapore pools serta agen judi togel sgp pool keluaran hari ini tercepat. Agen sgp pools menyediakan data pengeluaran singapore paling lengkap dan mudah di dapatkan. Situs toto sgp pools dengan diskon bettingan singapura 4d terbesar di Indonesia. Pengeluaran sgp hari ini tercepat, data pengeluaran sgp pools online hari ini, prediksi jitu sgp pools online, data keluaran togel sgp pools 4d, situs togel online sgp pools terpercaya.",
-    "editPermission": {
-        "type": "private"
-    },
-    "homepage": "https://ipabindia.org/sgp-pools/",
-    "lastUpdate": "2021-08-17T15:43:31Z",
-    "link": [
-        {
-            "note": "Agen Togel Sgp Pools Online",
-            "type": [
-                "Other"
-            ],
-            "url": "https://yk2daily.net/togel/sgp-pools.html"
-        }
-    ],
-    "name": "Sgppools - Sgp Pools 4D - Data Sgp Prize - Togel Sgp Online",
-    "owner": "sgppools"
-}
diff --git a/data/shimming_toolbox/shimming_toolbox.biotools.json b/data/shimming_toolbox/shimming_toolbox.biotools.json
new file mode 100644
index 0000000000000..e87b295dfafcf
--- /dev/null
+++ b/data/shimming_toolbox/shimming_toolbox.biotools.json
@@ -0,0 +1,109 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T23:18:45.865485Z",
+    "biotoolsCURIE": "biotools:shimming_toolbox",
+    "biotoolsID": "shimming_toolbox",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jcohen@polymtl.ca",
+            "name": "Julien Cohen-Adad",
+            "orcidid": "https://orcid.org/0000-0003-3662-9532"
+        },
+        {
+            "name": "Gaspard Cereza"
+        },
+        {
+            "name": "Alexandre D'Astous",
+            "orcidid": "https://orcid.org/0000-0003-0381-7334"
+        },
+        {
+            "name": "Eva Alonso-Ortiz",
+            "orcidid": "https://orcid.org/0000-0001-6590-7234"
+        }
+    ],
+    "description": "An open-source software toolbox for B0 and B1 shimming in MRI.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://shimming-toolbox.org",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T23:18:45.868336Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/shimming-toolbox/shimming-toolbox"
+        }
+    ],
+    "name": "Shimming Toolbox",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/MRM.29528",
+            "metadata": {
+                "abstract": "© 2022 International Society for Magnetic Resonance in Medicine.Purpose: Introduce Shimming Toolbox (https://shimming-toolbox.org), an open-source software package for prototyping new methods and performing static, dynamic, and real-time B0 shimming as well as B1 shimming experiments. Methods: Shimming Toolbox features various field mapping techniques, manual and automatic masking for the brain and spinal cord, B0 and B1 shimming capabilities accessible through a user-friendly graphical user interface. Validation of Shimming Toolbox was demonstrated in three scenarios: (i) B0 dynamic shimming in the brain at 7T using custom AC/DC coils, (ii) B0 real-time shimming in the spinal cord at 3T, and (iii) B1 static shimming in the spinal cord at 7T. Results: The B0 dynamic shimming of the brain at 7T took about 10 min to perform. It showed a 47% reduction in the standard deviation of the B0 field, associated with noticeable improvements in geometric distortions in EPI images. Real-time dynamic xyz-shimming in the spinal cord took about 5 min and showed a 30% reduction in the standard deviation of the signal distribution. B1 static shimming experiments in the spinal cord took about 10 min to perform and showed a 40% reduction in the coefficient of variation of the B1 field. Conclusion: Shimming Toolbox provides an open-source platform where researchers can collaborate, prototype and conveniently test B0 and B1 shimming experiments. Future versions will include additional field map preprocessing techniques, optimization algorithms, and compatibility across multiple MRI manufacturers.",
+                "authors": [
+                    {
+                        "name": "Alonso-Ortiz E."
+                    },
+                    {
+                        "name": "Cereza G."
+                    },
+                    {
+                        "name": "Cohen-Adad J."
+                    },
+                    {
+                        "name": "D'Astous A."
+                    },
+                    {
+                        "name": "Gilbert K.M."
+                    },
+                    {
+                        "name": "Papp D."
+                    },
+                    {
+                        "name": "Stockmann J.P."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Magnetic Resonance in Medicine",
+                "title": "Shimming toolbox: An open-source software toolbox for B0 and B1 shimming in MRI"
+            },
+            "pmid": "36441743"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        }
+    ]
+}
diff --git a/data/shinydatashield/shinydatashield.biotools.json b/data/shinydatashield/shinydatashield.biotools.json
new file mode 100644
index 0000000000000..dc33c75334bda
--- /dev/null
+++ b/data/shinydatashield/shinydatashield.biotools.json
@@ -0,0 +1,109 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T14:08:40.888473Z",
+    "biotoolsCURIE": "biotools:shinydatashield",
+    "biotoolsID": "shinydatashield",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "juanr.gonzalez@isglobal.org",
+            "name": "Juan R González",
+            "orcidid": "https://orcid.org/0000-0003-3267-2146",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Xavier Escribà-Montagut"
+        },
+        {
+            "name": "Yannick Marcon"
+        },
+        {
+            "name": "Paul Burton",
+            "orcidid": "https://orcid.org/0000-0001-5799-9634"
+        }
+    ],
+    "description": "ShinyDataSHIELD—an R Shiny application to perform federated non-disclosive data analysis in multicohort studies.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://www.datashield.org/help"
+        }
+    ],
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://hub.docker.com/r/brgelab/shiny-data-shield"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Scatter plot plotting",
+                    "uri": "http://edamontology.org/operation_2940"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://datashield-demo.obiba.org/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-03-17T14:08:40.894057Z",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://isglobal-brge.github.io/ShinyDataSHIELD_bookdown/"
+        }
+    ],
+    "name": "ShinyDataSHIELD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/IJE/DYAC201",
+            "pmcid": "PMC9908040"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        }
+    ]
+}
diff --git a/data/sierpinski-triangle-recursion/sierpinski-triangle-recursion.biotools.json b/data/sierpinski-triangle-recursion/sierpinski-triangle-recursion.biotools.json
deleted file mode 100644
index c208b53117f80..0000000000000
--- a/data/sierpinski-triangle-recursion/sierpinski-triangle-recursion.biotools.json
+++ /dev/null
@@ -1,49 +0,0 @@
-{
-    "accessibility": "Open access (with restrictions)",
-    "additionDate": "2021-05-05T11:10:08Z",
-    "biotoolsCURIE": "biotools:sierpinski-triangle-recursion",
-    "biotoolsID": "sierpinski-triangle-recursion",
-    "collectionID": [
-        "File Exchange",
-        "MATLAB"
-    ],
-    "cost": "Free of charge (with restrictions)",
-    "credit": [
-        {
-            "name": "Trong Hoang Vo",
-            "typeEntity": "Person",
-            "typeRole": [
-                "Primary contact"
-            ],
-            "url": "https://www.mathworks.com/matlabcentral/profile/7944589-trong-hoang-vo"
-        }
-    ],
-    "description": "This function draws Sierpinski triangle by using recursion",
-    "download": [
-        {
-            "type": "Screenshot",
-            "url": "https://www.mathworks.com//matlabcentral/images/default_screenshot.jpg"
-        }
-    ],
-    "editPermission": {
-        "type": "private"
-    },
-    "homepage": "https://www.mathworks.com/matlabcentral/fileexchange/56551-recursion-for-sierpinski-triangle",
-    "language": [
-        "MATLAB"
-    ],
-    "lastUpdate": "2021-05-23T10:23:32Z",
-    "name": "Recursion for Sierpinski Triangle",
-    "operatingSystem": [
-        "Linux",
-        "Mac",
-        "Windows"
-    ],
-    "owner": "zsmag19",
-    "toolType": [
-        "Library"
-    ],
-    "version": [
-        "1.0"
-    ]
-}
diff --git a/data/sighotspotter/sighotspotter.biotools.json b/data/sighotspotter/sighotspotter.biotools.json
index c577bebc36cf9..38846a8111b46 100644
--- a/data/sighotspotter/sighotspotter.biotools.json
+++ b/data/sighotspotter/sighotspotter.biotools.json
@@ -2,6 +2,9 @@
     "additionDate": "2020-01-14T09:00:54Z",
     "biotoolsCURIE": "biotools:SigHotSpotter",
     "biotoolsID": "SigHotSpotter",
+    "collectionID": [
+        "LCSB-CBG"
+    ],
     "confidence_flag": "tool",
     "description": "scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies.",
     "editPermission": {
@@ -26,7 +29,7 @@
         }
     ],
     "homepage": "https://SigHotSpotter.lcsb.uni.lu",
-    "lastUpdate": "2021-01-16T13:39:45Z",
+    "lastUpdate": "2023-01-13T14:22:07.793452Z",
     "link": [
         {
             "type": [
@@ -53,7 +56,7 @@
                         "name": "del Sol A."
                     }
                 ],
-                "citationCount": 5,
+                "citationCount": 8,
                 "date": "2020-01-01T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "SigHotSpotter: Scrna-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies"
diff --git a/data/simbiology-tmdd-model/simbiology-tmdd-model.biotools.json b/data/simbiology-tmdd-model/simbiology-tmdd-model.biotools.json
index fe85226b78d44..65d36c7c008aa 100644
--- a/data/simbiology-tmdd-model/simbiology-tmdd-model.biotools.json
+++ b/data/simbiology-tmdd-model/simbiology-tmdd-model.biotools.json
@@ -7,6 +7,7 @@
         "File Exchange",
         "MATLAB"
     ],
+    "confidence_flag": "tool",
     "cost": "Free of charge (with restrictions)",
     "credit": [
         {
@@ -32,7 +33,8 @@
     "language": [
         "MATLAB"
     ],
-    "lastUpdate": "2021-05-13T17:23:58Z",
+    "lastUpdate": "2023-01-13T00:48:40.745396Z",
+    "license": "Other",
     "name": "SimBiology model for Target-Mediated Drug Disposition (TMDD)",
     "operatingSystem": [
         "Linux",
@@ -53,11 +55,12 @@
                         "name": "Mager D.E."
                     }
                 ],
-                "citationCount": 392,
+                "citationCount": 440,
                 "date": "2001-12-01T00:00:00Z",
                 "journal": "Journal of Pharmacokinetics and Pharmacodynamics",
                 "title": "General pharmacokinetic model for drugs exhibiting target-mediated drug disposition"
-            }
+            },
+            "pmid": "11999290"
         }
     ],
     "toolType": [
diff --git a/data/simbu/simbu.biotools.json b/data/simbu/simbu.biotools.json
new file mode 100644
index 0000000000000..2fb9c894bb13b
--- /dev/null
+++ b/data/simbu/simbu.biotools.json
@@ -0,0 +1,103 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-23T09:59:40.630909Z",
+    "biotoolsCURIE": "biotools:simbu",
+    "biotoolsID": "simbu",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "markus.list@tum.de",
+            "name": "Markus List",
+            "orcidid": "http://orcid.org/0000-0002-0941-4168",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alexander Dietrich",
+            "orcidid": "http://orcid.org/0000-0002-8661-0453"
+        },
+        {
+            "name": "Francesca Finotello",
+            "orcidid": "http://orcid.org/0000-0003-0712-4658"
+        },
+        {
+            "name": "Gregor Sturm",
+            "orcidid": "http://orcid.org/0000-0001-9584-7842"
+        }
+    ],
+    "description": "The goal of SimBu is to simulate pseudo-bulk RNAseq datasets with variable cell-type fractions baed on public or private single-cell RNAseq datasets.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "http://omnideconv.org/SimBu/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Deisotoping",
+                    "uri": "http://edamontology.org/operation_3629"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/omnideconv/SimBu",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-23T09:59:40.633629Z",
+    "license": "GPL-3.0",
+    "name": "SimBu",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac499",
+            "pmid": "36124800"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Cytometry",
+            "uri": "http://edamontology.org/topic_3934"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/simple-blast/simple-blast.biotools.json b/data/simple-blast/simple-blast.biotools.json
index 9be2afc458c25..149f5c8884a41 100644
--- a/data/simple-blast/simple-blast.biotools.json
+++ b/data/simple-blast/simple-blast.biotools.json
@@ -7,6 +7,7 @@
         "File Exchange",
         "MATLAB"
     ],
+    "confidence_flag": "tool",
     "cost": "Free of charge (with restrictions)",
     "credit": [
         {
@@ -32,7 +33,8 @@
     "language": [
         "MATLAB"
     ],
-    "lastUpdate": "2021-05-24T08:17:03Z",
+    "lastUpdate": "2023-01-13T00:44:48.077913Z",
+    "license": "Other",
     "name": "A simple BLAST algorithm",
     "operatingSystem": [
         "Linux",
diff --git a/data/simpledsfviewer1/simpledsfviewer1.biotools.json b/data/simpledsfviewer1/simpledsfviewer1.biotools.json
index d6f9a24cb1323..1129b95daaaa4 100644
--- a/data/simpledsfviewer1/simpledsfviewer1.biotools.json
+++ b/data/simpledsfviewer1/simpledsfviewer1.biotools.json
@@ -1,15 +1,86 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2022-03-19T02:23:01.202501Z",
     "biotoolsCURIE": "biotools:simpledsfviewer1",
     "biotoolsID": "simpledsfviewer1",
-    "description": "for characterizing protein thermal stability in different temperatures\n\nhttps://github.com/hscsun/SimpleDSFviewer-5.0.git\n\n\nhttps://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fpro.3703&file=pro3703-sup-0001-Supinfo.docx",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "David G. Fernig"
+        },
+        {
+            "name": "Edwin A. Yates"
+        },
+        {
+            "name": "Yong Li"
+        },
+        {
+            "name": "Changye Sun",
+            "orcidid": "https://orcid.org/0000-0001-8602-9629"
+        }
+    ],
+    "description": "For characterizing protein thermal stability in different temperature",
     "editPermission": {
         "type": "private"
     },
     "homepage": "https://doi.org/10.1002/pro.3703",
-    "lastUpdate": "2022-12-09T22:45:25.625518Z",
+    "language": [
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-01-13T15:52:12.945453Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fpro.3703&file=pro3703-sup-0001-Supinfo.docx"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/hscsun/SimpleDSFviewer-5.0"
+        }
+    ],
     "name": "SimpleDSFviewer",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
     "owner": "MD",
+    "publication": [
+        {
+            "doi": "10.1002/pro.3703",
+            "metadata": {
+                "abstract": "© 2019 The Protein SocietyDifferential scanning fluorimetry (DSF) is a widely used thermal shift assay for measuring protein stability and protein–ligand interactions that are simple, cheap, and amenable to high throughput. However, data analysis remains a challenge, requiring improved methods. Here, the program SimpleDSFviewer, a user-friendly interface, is described to help the researchers who apply DSF technique in their studies. SimpleDSFviewer integrates melting curve (MC) normalization, smoothing, and melting temperature (Tm) analysis and directly previews analyzed data, providing an efficient analysis tool for DSF. SimpleDSFviewer is developed in Matlab, and it is freely available for all users to use in Matlab workspace or with Matlab Runtime. It is easy to use and an efficient tool for researchers to preview and analyze their data in a very short time.",
+                "authors": [
+                    {
+                        "name": "Fernig D.G."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Sun C."
+                    },
+                    {
+                        "name": "Yates E.A."
+                    }
+                ],
+                "citationCount": 10,
+                "date": "2020-01-01T00:00:00Z",
+                "journal": "Protein Science",
+                "title": "SimpleDSFviewer: A tool to analyze and view differential scanning fluorimetry data for characterizing protein thermal stability and interactions"
+            },
+            "pmcid": "PMC6933846",
+            "pmid": "31394001"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
     "topic": [
         {
             "term": "Protein folding, stability and design",
diff --git a/data/sinfonia/sinfonia.biotools.json b/data/sinfonia/sinfonia.biotools.json
new file mode 100644
index 0000000000000..562cafd6f05ce
--- /dev/null
+++ b/data/sinfonia/sinfonia.biotools.json
@@ -0,0 +1,122 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T11:55:00.494447Z",
+    "biotoolsCURIE": "biotools:sinfonia",
+    "biotoolsID": "sinfonia",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chenshengquan@nankai.edu.cn",
+            "name": "Shengquan Chen",
+            "orcidid": "https://orcid.org/0000-0002-3503-9306",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Rui Jiang"
+        },
+        {
+            "name": "Siyu Li"
+        },
+        {
+            "name": "Yuhang Jia"
+        },
+        {
+            "name": "Zhen Li"
+        }
+    ],
+    "description": "Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://sinfonia-svg.readthedocs.io/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "RNA-Seq quantification",
+                    "uri": "http://edamontology.org/operation_3800"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/BioX-NKU/SINFONIA",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T11:55:00.499230Z",
+    "license": "MIT",
+    "name": "SINFONIA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/CELLS12040604",
+            "metadata": {
+                "abstract": "Recent advances in spatial transcriptomics have revolutionized the understanding of tissue organization. The identification of spatially variable genes (SVGs) is an essential step for downstream spatial domain characterization. Although several methods have been proposed for identifying SVGs, inadequate ability to decipher spatial domains, poor efficiency, and insufficient interoperability with existing standard analysis workflows still impede the applications of these methods. Here we propose SINFONIA, a scalable method for identifying spatially variable genes via ensemble strategies. Implemented in Python, SINFONIA can be seamlessly integrated into existing analysis workflows. Using 15 spatial transcriptomic datasets generated with different protocols and with different sizes, dimensions and qualities, we show the advantage of SINFONIA over three baseline methods and two variants via systematic evaluation of spatial clustering, domain resolution, latent representation, spatial visualization, and computational efficiency with 21 quantitative metrics. Additionally, SINFONIA is robust relative to the choice of the number of SVGs. We anticipate SINFONIA will facilitate the analysis of spatial transcriptomics.",
+                "authors": [
+                    {
+                        "name": "Chen S."
+                    },
+                    {
+                        "name": "Jia Y."
+                    },
+                    {
+                        "name": "Jiang R."
+                    },
+                    {
+                        "name": "Li S."
+                    },
+                    {
+                        "name": "Li Z."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "Cells",
+                "title": "SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains"
+            },
+            "pmcid": "PMC9954745",
+            "pmid": "36831270"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/siper/siper.biotools.json b/data/siper/siper.biotools.json
new file mode 100644
index 0000000000000..d7af956c176c6
--- /dev/null
+++ b/data/siper/siper.biotools.json
@@ -0,0 +1,161 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-13T14:40:14.164348Z",
+    "biotoolsCURIE": "biotools:siper",
+    "biotoolsID": "siper",
+    "collectionID": [
+        "LCSB-CBG"
+    ],
+    "credit": [
+        {
+            "email": "antonio.delsol@uni.lu",
+            "name": "Antonio del Sol",
+            "note": "Group leader, Computational Biology group,  Luxembourg Centre for Systems Biomedicine  \t\nFull professor / Chief scientist 1 in Bioinformatics at University of Luxembourg",
+            "typeEntity": "Person",
+            "typeRole": [
+                "Primary contact"
+            ],
+            "url": "https://wwwen.uni.lu/lcsb/people/antonio_del_sol_mesa"
+        }
+    ],
+    "description": "A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion",
+    "documentation": [
+        {
+            "type": [
+                "Quick start guide"
+            ],
+            "url": "https://siper.uni.lu/pages/information"
+        }
+    ],
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://git-r3lab.uni.lu/menglin.zheng/SiPer"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "elixirNode": [
+        "Luxembourg"
+    ],
+    "elixirPlatform": [
+        "Tools"
+    ],
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene expression matrix",
+                        "uri": "http://edamontology.org/data_3112"
+                    },
+                    "format": [
+                        {
+                            "term": "TSV",
+                            "uri": "http://edamontology.org/format_3475"
+                        }
+                    ]
+                },
+                {
+                    "data": {
+                        "term": "Transcription factor name",
+                        "uri": "http://edamontology.org/data_2755"
+                    },
+                    "format": [
+                        {
+                            "term": "TSV",
+                            "uri": "http://edamontology.org/format_3475"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Modelling and simulation",
+                    "uri": "http://edamontology.org/operation_2426"
+                },
+                {
+                    "term": "Prediction and recognition",
+                    "uri": "http://edamontology.org/operation_2423"
+                }
+            ],
+            "output": [
+                {
+                    "data": {
+                        "term": "Compound name",
+                        "uri": "http://edamontology.org/data_0990"
+                    },
+                    "format": [
+                        {
+                            "term": "Annotated text format",
+                            "uri": "http://edamontology.org/format_3780"
+                        }
+                    ]
+                },
+                {
+                    "data": {
+                        "term": "Protein name",
+                        "uri": "http://edamontology.org/data_1009"
+                    },
+                    "format": [
+                        {
+                            "term": "Annotated text format",
+                            "uri": "http://edamontology.org/format_3780"
+                        }
+                    ]
+                }
+            ]
+        }
+    ],
+    "homepage": "https://siper.uni.lu/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-13T14:40:14.167404Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://git-r3lab.uni.lu/menglin.zheng/SiPer"
+        }
+    ],
+    "name": "SiPer",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "KartikeyaS",
+    "publication": [
+        {
+            "doi": "10.1016/j.stemcr.2022.10.013",
+            "pmid": "36400030",
+            "type": [
+                "Primary"
+            ]
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Computational biology",
+            "uri": "http://edamontology.org/topic_3307"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/siq/siq.biotools.json b/data/siq/siq.biotools.json
new file mode 100644
index 0000000000000..e056ad02efd4f
--- /dev/null
+++ b/data/siq/siq.biotools.json
@@ -0,0 +1,89 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-20T00:41:15.996734Z",
+    "biotoolsCURIE": "biotools:siq",
+    "biotoolsID": "siq",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "M.Tijsterman@lumc.nl",
+            "name": "Marcel Tijsterman",
+            "orcidid": "http://orcid.org/0000-0001-8465-9002",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "R.van_Schendel@lumc.nl",
+            "name": "Robin van Schendel",
+            "orcidid": "http://orcid.org/0000-0001-7068-0679",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Joost Schimmel",
+            "orcidid": "http://orcid.org/0000-0002-2620-4349"
+        }
+    ],
+    "description": "Easy quantitative measurement of mutation profiles in sequencing data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "PCR primer design",
+                    "uri": "http://edamontology.org/operation_0308"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://siq.researchlumc.nl/SIQPlotteR/",
+    "lastUpdate": "2023-01-20T00:41:15.999475Z",
+    "name": "SIQ",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/nargab/lqac063",
+            "pmcid": "PMC9442499",
+            "pmid": "36071722"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Nucleic acid sites, features and motifs",
+            "uri": "http://edamontology.org/topic_3511"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/sitepath/sitepath.biotools.json b/data/sitepath/sitepath.biotools.json
new file mode 100644
index 0000000000000..33a10e9608211
--- /dev/null
+++ b/data/sitepath/sitepath.biotools.json
@@ -0,0 +1,155 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T23:02:54.727743Z",
+    "biotoolsCURIE": "biotools:sitepath",
+    "biotoolsID": "sitepath",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "wap@ism.cams.cn",
+            "name": "Aiping Wu",
+            "orcidid": "https://orcid.org/0000-0002-5869-651X",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhy@ism.cams.cn",
+            "name": "Hang-Yu Zhou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Chengyang Ji"
+        },
+        {
+            "name": "Na Han"
+        }
+    ],
+    "description": "A visual tool to identify polymorphism clades and help find fixed and parallel mutations.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://wuaipinglab.github.io/sitePath/"
+        },
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://bioconductor.org/packages/release/bioc/manuals/sitePath/man/sitePath.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phylogenetic inference",
+                    "uri": "http://edamontology.org/operation_0323"
+                },
+                {
+                    "term": "Phylogenetic tree editing",
+                    "uri": "http://edamontology.org/operation_0326"
+                },
+                {
+                    "term": "Phylogenetic tree visualisation",
+                    "uri": "http://edamontology.org/operation_0567"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/wuaipinglab/sitePath",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-25T23:02:54.731826Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://bioconductor.org/packages/release/bioc/html/sitePath.html"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/wuaipinglab/sitePath_assessment"
+        }
+    ],
+    "name": "sitePath",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05064-4",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Identifying polymorphism clades on phylogenetic trees could help detect punctual mutations that are associated with viral functions. With visualization tools coloring the tree, it is easy to visually find clades where most sequences have the same polymorphism state. However, with the fast accumulation of viral sequences, a computational tool to automate this process is urgently needed. Results: Here, by implementing a branch-and-bound-like search method, we developed an R package named sitePath to identify polymorphism clades automatically. Based on the identified polymorphism clades, fixed and parallel mutations could be inferred. Furthermore, sitePath also integrated visualization tools to generate figures of the calculated results. In an example with the influenza A virus H3N2 dataset, the detected fixed mutations coincide with antigenic shift mutations. The highly specificity and sensitivity of sitePath in finding fixed mutations were achieved for a range of parameters and different phylogenetic tree inference software. Conclusions: The result suggests that sitePath can identify polymorphism clades per site. The clustering of sequences on a phylogenetic tree can be used to infer fixed and parallel mutations. High-quality figures of the calculated results could also be generated by sitePath.",
+                "authors": [
+                    {
+                        "name": "Cheng Y."
+                    },
+                    {
+                        "name": "Han N."
+                    },
+                    {
+                        "name": "Ji C."
+                    },
+                    {
+                        "name": "Shang J."
+                    },
+                    {
+                        "name": "Weng S."
+                    },
+                    {
+                        "name": "Wu A."
+                    },
+                    {
+                        "name": "Yang R."
+                    },
+                    {
+                        "name": "Zhou H.-Y."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "sitePath: a visual tool to identify polymorphism clades and help find fixed and parallel mutations"
+            },
+            "pmcid": "PMC9701067",
+            "pmid": "36434502"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/sivae/sivae.biotools.json b/data/sivae/sivae.biotools.json
new file mode 100644
index 0000000000000..b7e504fddc3c4
--- /dev/null
+++ b/data/sivae/sivae.biotools.json
@@ -0,0 +1,114 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T11:45:07.366770Z",
+    "biotoolsCURIE": "biotools:sivae",
+    "biotoolsID": "sivae",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "gquon@ucdavis.edu",
+            "name": "Gerald Quon",
+            "orcidid": "https://orcid.org/0000-0002-1716-0153",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ruoxin Li"
+        },
+        {
+            "name": "Yongin Choi"
+        }
+    ],
+    "description": "Interpretable deep generative models for single-cell transcriptomes.",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://pypi.org/project/siVAE/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Network visualisation",
+                    "uri": "http://edamontology.org/operation_3925"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/quon-titative-biology/siVAE",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T11:45:07.371680Z",
+    "license": "MIT",
+    "name": "siVAE",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13059-023-02850-Y",
+            "metadata": {
+                "abstract": "Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.",
+                "authors": [
+                    {
+                        "name": "Choi Y."
+                    },
+                    {
+                        "name": "Li R."
+                    },
+                    {
+                        "name": "Quon G."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Genome Biology",
+                "title": "siVAE: interpretable deep generative models for single-cell transcriptomes"
+            },
+            "pmcid": "PMC9940350",
+            "pmid": "36803416"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Epigenomics",
+            "uri": "http://edamontology.org/topic_3173"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/skempi/skempi.biotools.json b/data/skempi/skempi.biotools.json
index a9bbf4c706c99..4a506468640c2 100644
--- a/data/skempi/skempi.biotools.json
+++ b/data/skempi/skempi.biotools.json
@@ -3,6 +3,8 @@
     "additionDate": "2021-08-27T15:39:27Z",
     "biotoolsCURIE": "biotools:skempi",
     "biotoolsID": "skempi",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "credit": [
         {
             "email": "b.jimenezgarcia@uu.nl",
@@ -71,7 +73,8 @@
         "type": "private"
     },
     "homepage": "https://life.bsc.es/pid/skempi2",
-    "lastUpdate": "2022-12-09T22:47:21.402108Z",
+    "lastUpdate": "2023-01-11T01:35:37.359315Z",
+    "license": "CC-BY-4.0",
     "maturity": "Mature",
     "name": "SKEMPI",
     "operatingSystem": [
@@ -102,11 +105,13 @@
                         "name": "Moal I.H."
                     }
                 ],
-                "citationCount": 94,
+                "citationCount": 96,
                 "date": "2019-02-01T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "SKEMPI 2.0: An updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation"
-            }
+            },
+            "pmcid": "PMC6361233",
+            "pmid": "30020414"
         }
     ],
     "toolType": [
diff --git a/data/smallareamapp/smallareamapp.biotools.json b/data/smallareamapp/smallareamapp.biotools.json
new file mode 100644
index 0000000000000..842e5ccd585bd
--- /dev/null
+++ b/data/smallareamapp/smallareamapp.biotools.json
@@ -0,0 +1,113 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T22:53:28.429731Z",
+    "biotoolsCURIE": "biotools:smallareamapp",
+    "biotoolsID": "smallareamapp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jonathan.simkin@bccancer.bc.ca",
+            "name": "Jonathan Simkin",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Gina Ogilvie"
+        },
+        {
+            "name": "Ryan R Woods"
+        },
+        {
+            "name": "Trevor J B Dummer"
+        }
+    ],
+    "description": "Small area disease mapping of cancer incidence in British Columbia using Bayesian spatial models and the smallareamapp R Package.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Protein structure validation",
+                    "uri": "http://edamontology.org/operation_0321"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/jdsimkin04/smallareamapp",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-25T22:53:28.457635Z",
+    "license": "MIT",
+    "name": "smallareamapp",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FONC.2022.833265",
+            "metadata": {
+                "abstract": "Copyright © 2022 Simkin, Dummer, Erickson, Otterstatter, Woods and Ogilvie.Introduction: There is an increasing interest in small area analyses in cancer surveillance; however, technical capacity is limited and accessible analytical approaches remain to be determined. This study demonstrates an accessible approach for small area cancer risk estimation using Bayesian hierarchical models and data visualization through the smallareamapp R package. Materials and methods: Incident lung (N = 26,448), female breast (N = 28,466), cervical (N = 1,478), and colorectal (N = 25,457) cancers diagnosed among British Columbia (BC) residents between 2011 and 2018 were obtained from the BC Cancer Registry. Indirect age-standardization was used to derive age-adjusted expected counts and standardized incidence ratios (SIRs) relative to provincial rates. Moran’s I was used to assess the strength and direction of spatial autocorrelation. A modified Besag, York and Mollie model (BYM2) was used for model incidence counts to calculate posterior median relative risks (RR) by Community Health Service Areas (CHSA; N = 218), adjusting for spatial dependencies. Integrated Nested Laplace Approximation (INLA) was used for Bayesian model implementation. Areas with exceedance probabilities (above a threshold RR = 1.1) greater or equal to 80% were considered to have an elevated risk. The posterior median and 95% credible intervals (CrI) for the spatially structured effect were reported. Predictive posterior checks were conducted through predictive integral transformation values and observed versus fitted values. Results: The proportion of variance in the RR explained by a spatial effect ranged from 4.4% (male colorectal) to 19.2% (female breast). Lung cancer showed the greatest number of CHSAs with elevated risk (Nwomen = 50/218, Nmen = 44/218), representing 2357 total excess cases. The largest lung cancer RRs were 1.67 (95% CrI = 1.06–2.50; exceedance probability = 96%; cases = 13) among women and 2.49 (95% CrI = 2.14–2.88; exceedance probability = 100%; cases = 174) among men. Areas with small population sizes and extreme SIRs were generally smoothed towards the null (RR = 1.0). Discussion: We present a ready-to-use approach for small area cancer risk estimation and disease mapping using BYM2 and exceedance probabilities. We developed the smallareamapp R package, which provides a user-friendly interface through an R-Shiny application, for epidemiologists and surveillance experts to examine geographic variation in risk. These methods and tools can be used to estimate risk, generate hypotheses, and examine ecologic associations while adjusting for spatial dependency.",
+                "authors": [
+                    {
+                        "name": "Dummer T.J.B."
+                    },
+                    {
+                        "name": "Erickson A.C."
+                    },
+                    {
+                        "name": "Ogilvie G."
+                    },
+                    {
+                        "name": "Otterstatter M.C."
+                    },
+                    {
+                        "name": "Simkin J."
+                    },
+                    {
+                        "name": "Woods R.R."
+                    }
+                ],
+                "date": "2022-10-19T00:00:00Z",
+                "journal": "Frontiers in Oncology",
+                "title": "Small area disease mapping of cancer incidence in British Columbia using Bayesian spatial models and the smallareamapp R Package"
+            },
+            "pmcid": "PMC9627310",
+            "pmid": "36338766"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Ecology",
+            "uri": "http://edamontology.org/topic_0610"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        }
+    ]
+}
diff --git a/data/smap_design/smap_design.biotools.json b/data/smap_design/smap_design.biotools.json
new file mode 100644
index 0000000000000..cf38feea3d6e3
--- /dev/null
+++ b/data/smap_design/smap_design.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T11:31:59.702267Z",
+    "biotoolsCURIE": "biotools:smap_design",
+    "biotoolsID": "smap_design",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "thomas.jacobs@psb.ugent.be",
+            "name": "Thomas B Jacobs",
+            "orcidid": "https://orcid.org/0000-0002-5408-492X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Evelien Waegneer"
+        },
+        {
+            "name": "Ward Develtere"
+        },
+        {
+            "name": "Tom Ruttink",
+            "orcidid": "https://orcid.org/0000-0002-1012-9399"
+        }
+    ],
+    "description": "A multiplex PCR amplicon and gRNA design tool to screen for natural and CRISPR-induced genetic variation.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://ngs-smap.readthedocs.io/en/latest/design/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "PCR primer design",
+                    "uri": "http://edamontology.org/operation_0308"
+                },
+                {
+                    "term": "Read mapping",
+                    "uri": "http://edamontology.org/operation_3198"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://gitlab.com/ilvo/smap-design",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T11:31:59.708380Z",
+    "license": "AGPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://gitlab.com/ilvo/smap"
+        }
+    ],
+    "name": "SMAP design",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAD036",
+            "pmid": "36718951"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Probes and primers",
+            "uri": "http://edamontology.org/topic_0632"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/smashpp/smashpp.biotools.json b/data/smashpp/smashpp.biotools.json
index 0858afa3eee5e..178d8505ee924 100644
--- a/data/smashpp/smashpp.biotools.json
+++ b/data/smashpp/smashpp.biotools.json
@@ -1,7 +1,10 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2020-04-17T15:39:16Z",
     "biotoolsCURIE": "biotools:smashpp",
     "biotoolsID": "smashpp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "Find and visualize rearrangements in DNA sequences",
     "editPermission": {
         "type": "private"
@@ -31,9 +34,21 @@
         }
     ],
     "homepage": "https://github.com/smortezah/smashpp",
-    "lastUpdate": "2022-12-09T22:50:47.912724Z",
+    "language": [
+        "C++"
+    ],
+    "lastUpdate": "2023-01-11T01:30:41.960585Z",
+    "license": "GPL-3.0",
     "name": "Smash++",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "admin",
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "DNA",
diff --git a/data/smfm/smfm.biotools.json b/data/smfm/smfm.biotools.json
new file mode 100644
index 0000000000000..a02ad1ea03fd7
--- /dev/null
+++ b/data/smfm/smfm.biotools.json
@@ -0,0 +1,119 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T17:42:49.958911Z",
+    "biotoolsCURIE": "biotools:smfm",
+    "biotoolsID": "smfm",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "lixt314@jlu.edu.cn",
+            "name": "Xiangtao Li",
+            "orcidid": "https://orcid.org/0000-0002-8716-9823",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ka-chun Wong"
+        },
+        {
+            "name": "Yansong Wang"
+        },
+        {
+            "name": "Zilong Hou"
+        },
+        {
+            "name": "Yuning Yang",
+            "orcidid": "https://orcid.org/0000-0003-4556-139X"
+        }
+    ],
+    "description": "Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Sequence motif analysis",
+                    "uri": "http://edamontology.org/operation_2404"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/no-banana/SMFM-master",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T17:42:49.961731Z",
+    "license": "Not licensed",
+    "name": "SMFM",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PCBI.1010779",
+            "metadata": {
+                "abstract": "Copyright: © 2022 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Enhancers are short non-coding DNA sequences outside of the target promoter regions that can be bound by specific proteins to increase a gene’s transcriptional activity, which has a crucial role in the spatiotemporal and quantitative regulation of gene expression. However, enhancers do not have a specific sequence motifs or structures, and their scattered distribution in the genome makes the identification of enhancers from human cell lines particularly challenging. Here we present a novel, stacked multivariate fusion framework called SMFM, which enables a comprehensive identification and analysis of enhancers from regulatory DNA sequences as well as their interpretation. Specifically, to characterize the hierarchical relationships of enhancer sequences, multi-source biological information and dynamic semantic information are fused to represent regulatory DNA enhancer sequences. Then, we implement a deep learning–based sequence network to learn the feature representation of the enhancer sequences comprehensively and to extract the implicit relationships in the dynamic semantic information. Ultimately, an ensemble machine learning classifier is trained based on the refined multi-source features and dynamic implicit relations obtained from the deep learning-based sequence network. Benchmarking experiments demonstrated that SMFM significantly outperforms other existing methods using several evaluation metrics. In addition, an independent test set was used to validate the generalization performance of SMFM by comparing it to other state-of-the-art enhancer identification methods. Moreover, we performed motif analysis based on the contribution scores of different bases of enhancer sequences to the final identification results. Besides, we conducted interpretability analysis of the identified enhancer sequences based on attention weights of EnhancerBERT, a fine-tuned BERT model that provides new insights into exploring the gene semantic information likely to underlie the discovered enhancers in an interpretable manner. Finally, in a human placenta study with 4,562 active distal gene regulatory enhancers, SMFM successfully exposed tissue-related placental development and the differential mechanism, demonstrating the generalizability and stability of our proposed framework.",
+                "authors": [
+                    {
+                        "name": "Hou Z."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Wong K.-C."
+                    },
+                    {
+                        "name": "Yang Y."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework"
+            },
+            "pmcid": "PMC9836277",
+            "pmid": "36520922"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Gene regulation",
+            "uri": "http://edamontology.org/topic_0204"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/smma-hnrl/smma-hnrl.biotools.json b/data/smma-hnrl/smma-hnrl.biotools.json
new file mode 100644
index 0000000000000..d549176fc7a7c
--- /dev/null
+++ b/data/smma-hnrl/smma-hnrl.biotools.json
@@ -0,0 +1,113 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T17:38:17.220592Z",
+    "biotoolsCURIE": "biotools:smma-hnrl",
+    "biotoolsID": "smma-hnrl",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "lijianwei@hebut.edu.cn",
+            "name": "Jianwei Li",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Baoqin Wu"
+        },
+        {
+            "name": "Hongxin Lin"
+        },
+        {
+            "name": "Yinfei Wang"
+        },
+        {
+            "name": "Zhiguang Li"
+        }
+    ],
+    "description": "Prediction of potential small molecule−miRNA associations based on heterogeneous network representation learning.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "miRNA target prediction",
+                    "uri": "http://edamontology.org/operation_0463"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/SMMA-HNRL/SMMA-HNRL",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T17:38:17.223367Z",
+    "license": "Not licensed",
+    "name": "SMMA-HNRL",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FGENE.2022.1079053",
+            "metadata": {
+                "abstract": "Copyright © 2022 Li, Lin, Wang, Li and Wu.MicroRNAs (miRNAs) are closely associated with the occurrences and developments of many complex human diseases. Increasing studies have shown that miRNAs emerge as new therapeutic targets of small molecule (SM) drugs. Since traditional experiment methods are expensive and time consuming, it is particularly crucial to find efficient computational approaches to predict potential small molecule-miRNA (SM-miRNA) associations. Considering that integrating multi-source heterogeneous information related with SM-miRNA association prediction would provide a comprehensive insight into the features of both SMs and miRNAs, we proposed a novel model of Small Molecule-MiRNA Association prediction based on Heterogeneous Network Representation Learning (SMMA-HNRL) for more precisely predicting the potential SM-miRNA associations. In SMMA-HNRL, a novel heterogeneous information network was constructed with SM nodes, miRNA nodes and disease nodes. To access and utilize of the topological information of the heterogeneous information network, feature vectors of SM and miRNA nodes were obtained by two different heterogeneous network representation learning algorithms (HeGAN and HIN2Vec) respectively and merged with connect operation. Finally, LightGBM was chosen as the classifier of SMMA-HNRL for predicting potential SM-miRNA associations. The 10-fold cross validations were conducted to evaluate the prediction performance of SMMA-HNRL, it achieved an area under of ROC curve of 0.9875, which was superior to other three state-of-the-art models. With two independent validation datasets, the test experiment results revealed the robustness of our model. Moreover, three case studies were performed. As a result, 35, 37, and 22 miRNAs among the top 50 predicting miRNAs associated with 5-FU, cisplatin, and imatinib were validated by experimental literature works respectively, which confirmed the effectiveness of SMMA-HNRL. The source code and experimental data of SMMA-HNRL are available at https://github.com/SMMA-HNRL/SMMA-HNRL.",
+                "authors": [
+                    {
+                        "name": "Li J."
+                    },
+                    {
+                        "name": "Li Z."
+                    },
+                    {
+                        "name": "Lin H."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Wu B."
+                    }
+                ],
+                "date": "2022-12-02T00:00:00Z",
+                "journal": "Frontiers in Genetics",
+                "title": "Prediction of potential small molecule−miRNA associations based on heterogeneous network representation learning"
+            },
+            "pmcid": "PMC9755196",
+            "pmid": "36531225"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/snekmer/snekmer.biotools.json b/data/snekmer/snekmer.biotools.json
new file mode 100644
index 0000000000000..fcf765232ccad
--- /dev/null
+++ b/data/snekmer/snekmer.biotools.json
@@ -0,0 +1,105 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T11:20:40.400215Z",
+    "biotoolsCURIE": "biotools:snekmer",
+    "biotoolsID": "snekmer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jason.mcdermott@pnnl.gov",
+            "name": "Jason E McDermott",
+            "orcidid": "https://orcid.org/0000-0003-2961-2572",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Christine H Chang"
+        },
+        {
+            "name": "William C Nelson"
+        },
+        {
+            "name": "Robert G Egbert",
+            "orcidid": "https://orcid.org/0000-0002-9470-7124"
+        }
+    ],
+    "description": "A scalable pipeline for protein sequence fingerprinting based on amino acid recoding.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://snekmer.readthedocs.io"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Protein fragment weight comparison",
+                    "uri": "http://edamontology.org/operation_2929"
+                },
+                {
+                    "term": "Sequence clustering",
+                    "uri": "http://edamontology.org/operation_0291"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/PNNL-CompBio/Snekmer",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T11:20:40.405674Z",
+    "license": "BSD-3-Clause",
+    "name": "Snekmer",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOADV/VBAD005",
+            "pmcid": "PMC9913046",
+            "pmid": "36789294"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene and protein families",
+            "uri": "http://edamontology.org/topic_0623"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/snpkit/snpkit.biotools.json b/data/snpkit/snpkit.biotools.json
index c1f195b107f1f..6be6302835dac 100644
--- a/data/snpkit/snpkit.biotools.json
+++ b/data/snpkit/snpkit.biotools.json
@@ -1,7 +1,10 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2022-02-22T16:09:21.482950Z",
     "biotoolsCURIE": "biotools:snpkit",
     "biotoolsID": "snpkit",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "A Modular/Scalable workflow for Microbial Variant Calling, Recombination detection and Phylogenetic tree reconstruction.",
     "editPermission": {
         "type": "private"
@@ -67,9 +70,29 @@
         }
     ],
     "homepage": "https://alipirani88.github.io/snpkit/",
-    "lastUpdate": "2022-12-09T22:56:42.079419Z",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-11T01:28:44.951149Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/alipirani88/snpkit"
+        }
+    ],
     "name": "snpkit",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "alipirani88",
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "Phylogenetics",
diff --git a/data/snpstarrseq/snpstarrseq.biotools.json b/data/snpstarrseq/snpstarrseq.biotools.json
new file mode 100644
index 0000000000000..1407ab766f6a5
--- /dev/null
+++ b/data/snpstarrseq/snpstarrseq.biotools.json
@@ -0,0 +1,95 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T17:33:56.941824Z",
+    "biotoolsCURIE": "biotools:snpstarrseq",
+    "biotoolsID": "snpstarrseq",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "nlack@prostatecentre.com",
+            "name": "Nathan A Lack",
+            "orcidid": "https://orcid.org/0000-0001-7399-5844",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Bogdan Pasaniuc",
+            "orcidid": "https://orcid.org/0000-0002-0227-2056"
+        },
+        {
+            "name": "Tunc Morova",
+            "orcidid": "https://orcid.org/0000-0002-4062-6185"
+        },
+        {
+            "name": "Yi Ding",
+            "orcidid": "https://orcid.org/0000-0003-3595-2493"
+        }
+    ],
+    "description": "Optimized high-throughput screening of non-coding variants identified from genome-wide association studies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "SNP annotation",
+                    "uri": "http://edamontology.org/operation_3661"
+                },
+                {
+                    "term": "SNP detection",
+                    "uri": "http://edamontology.org/operation_0484"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/mortunco/snp-starrseq",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-02-13T17:33:56.944644Z",
+    "license": "MIT",
+    "name": "snpSTARRseq",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1198",
+            "pmid": "36546757"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/sodb/sodb.biotools.json b/data/sodb/sodb.biotools.json
new file mode 100644
index 0000000000000..16945b91bd2ef
--- /dev/null
+++ b/data/sodb/sodb.biotools.json
@@ -0,0 +1,156 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T11:04:00.959513Z",
+    "biotoolsCURIE": "biotools:sodb",
+    "biotoolsID": "sodb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Jianhua Yao"
+        },
+        {
+            "name": "Wentao Pan"
+        },
+        {
+            "name": "Michael Q. Zhang",
+            "orcidid": "http://orcid.org/0000-0002-7022-6115"
+        },
+        {
+            "name": "Zhiyuan Yuan",
+            "orcidid": "http://orcid.org/0000-0002-9367-4236"
+        }
+    ],
+    "description": "SODB facilitates comprehensive exploration of spatial omics data.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://pysodb.readthedocs.io/en/latest/index.html"
+        }
+    ],
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://github.com/yuanzhiyuan/SODB_analysis/tree/master/Demonstration"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/TencentAILabHealthcare/pysodb",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T11:04:00.963267Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/yuanzhiyuan/SODB_analysis/"
+        }
+    ],
+    "name": "SODB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41592-023-01772-8",
+            "metadata": {
+                "date": "2023-03-01T00:00:00Z",
+                "journal": "Nature Methods",
+                "title": "Spatial Omics DataBase (SODB): increasing accessibility to spatial omics data"
+            },
+            "pmid": "36797411"
+        },
+        {
+            "doi": "10.1038/S41592-023-01773-7",
+            "metadata": {
+                "abstract": "Spatial omics technologies generate wealthy but highly complex datasets. Here we present Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources and a suite of interactive data analytical modules. SODB currently maintains >2,400 experiments from >25 spatial omics technologies, which are freely accessible as a unified data format compatible with various computational packages. SODB also provides multiple interactive data analytical modules, especially a unique module, Spatial Omics View (SOView). We conduct comprehensive statistical analyses and illustrate the utility of both basic and advanced analytical modules using multiple spatial omics datasets. We demonstrate SOView utility with brain spatial transcriptomics data and recover known anatomical structures. We further delineate functional tissue domains with associated marker genes that were obscured when analyzed using previous methods. We finally show how SODB may efficiently facilitate computational method development. The SODB website is https://gene.ai.tencent.com/SpatialOmics/. The command-line package is available at https://pysodb.readthedocs.io/en/latest/.",
+                "authors": [
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Pan W."
+                    },
+                    {
+                        "name": "Xu Z."
+                    },
+                    {
+                        "name": "Yao J."
+                    },
+                    {
+                        "name": "Yuan Z."
+                    },
+                    {
+                        "name": "Zhang M.Q."
+                    },
+                    {
+                        "name": "Zhao F."
+                    },
+                    {
+                        "name": "Zhao X."
+                    },
+                    {
+                        "name": "Zhao Y."
+                    }
+                ],
+                "date": "2023-03-01T00:00:00Z",
+                "journal": "Nature Methods",
+                "title": "SODB facilitates comprehensive exploration of spatial omics data"
+            },
+            "pmid": "36797409"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/soluprotmutsupdb/soluprotmutsupdb.biotools.json b/data/soluprotmutsupdb/soluprotmutsupdb.biotools.json
new file mode 100644
index 0000000000000..169358f347f61
--- /dev/null
+++ b/data/soluprotmutsupdb/soluprotmutsupdb.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T22:49:31.072923Z",
+    "biotoolsCURIE": "biotools:soluprotmutsupdb",
+    "biotoolsID": "soluprotmutsupdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "222755@mail.muni.cz",
+            "name": "David Bednar",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "mazurenko@mail.muni.cz",
+            "name": "Stanislav Mazurenko",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jan Velecký"
+        },
+        {
+            "name": "Marie Hamsikova"
+        }
+    ],
+    "description": "A manually curated database of protein solubility changes upon mutations.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Protein solubility prediction",
+                    "uri": "http://edamontology.org/operation_0409"
+                },
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://loschmidt.chemi.muni.cz/soluprotmutdb",
+    "lastUpdate": "2023-01-25T22:49:31.075847Z",
+    "name": "SoluProtMutsupDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.11.009",
+            "metadata": {
+                "abstract": "© 2022 The Author(s)Protein solubility is an attractive engineering target primarily due to its relation to yields in protein production and manufacturing. Moreover, better knowledge of the mutational effects on protein solubility could connect several serious human diseases with protein aggregation. However, we have limited understanding of the protein structural determinants of solubility, and the available data have mostly been scattered in the literature. Here, we present SoluProtMutDB – the first database containing data on protein solubility changes upon mutations. Our database accommodates 33 000 measurements of 17 000 protein variants in 103 different proteins. The database can serve as an essential source of information for the researchers designing improved protein variants or those developing machine learning tools to predict the effects of mutations on solubility. The database comprises all the previously published solubility datasets and thousands of new data points from recent publications, including deep mutational scanning experiments. Moreover, it features many available experimental conditions known to affect protein solubility. The datasets have been manually curated with substantial corrections, improving suitability for machine learning applications. The database is available at loschmidt.chemi.muni.cz/soluprotmutdb.",
+                "authors": [
+                    {
+                        "name": "Bednar D."
+                    },
+                    {
+                        "name": "Damborsky J."
+                    },
+                    {
+                        "name": "Hamsikova M."
+                    },
+                    {
+                        "name": "Mazurenko S."
+                    },
+                    {
+                        "name": "Musil M."
+                    },
+                    {
+                        "name": "Stourac J."
+                    },
+                    {
+                        "name": "Velecky J."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "SoluProtMutDB: A manually curated database of protein solubility changes upon mutations"
+            },
+            "pmcid": "PMC9678803",
+            "pmid": "36420168"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Data submission, annotation and curation",
+            "uri": "http://edamontology.org/topic_0219"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Proteins",
+            "uri": "http://edamontology.org/topic_0078"
+        }
+    ]
+}
diff --git a/data/sophie/sophie.biotools.json b/data/sophie/sophie.biotools.json
new file mode 100644
index 0000000000000..c482e66a792cd
--- /dev/null
+++ b/data/sophie/sophie.biotools.json
@@ -0,0 +1,127 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-23T10:05:34.670743Z",
+    "biotoolsCURIE": "biotools:sophie",
+    "biotoolsID": "sophie",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "pskums@gsu.edu",
+            "name": "Pavel Skums",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Fatemeh Mohebbi"
+        },
+        {
+            "name": "Vyacheslav Tsyvina"
+        },
+        {
+            "name": "Yury Khudyakov"
+        }
+    ],
+    "description": "Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework.",
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://zenodo.org/record/6792964"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Ancestral reconstruction",
+                    "uri": "http://edamontology.org/operation_3745"
+                },
+                {
+                    "term": "Phylogenetic inference (maximum likelihood and Bayesian methods)",
+                    "uri": "http://edamontology.org/operation_0547"
+                },
+                {
+                    "term": "Phylogenetic inference (parsimony methods)",
+                    "uri": "http://edamontology.org/operation_0545"
+                },
+                {
+                    "term": "Phylogenetic reconstruction",
+                    "uri": "http://edamontology.org/operation_3478"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/compbel/SOPHIE/",
+    "language": [
+        "MATLAB",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-23T10:05:34.673303Z",
+    "license": "MIT",
+    "name": "SOPHIE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.cels.2022.07.005",
+            "metadata": {
+                "abstract": "© 2022 Elsevier Inc.Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed for phylogenetic inference of transmission histories, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, although common source outbreaks violate this assumption. We propose a maximum likelihood framework, SOPHIE, based on the integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modeled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity, and accurately infers transmissions without case-specific epidemiological data.",
+                "authors": [
+                    {
+                        "name": "Baykal P.I."
+                    },
+                    {
+                        "name": "Khudyakov Y."
+                    },
+                    {
+                        "name": "Mohebbi F."
+                    },
+                    {
+                        "name": "Nemira A."
+                    },
+                    {
+                        "name": "Ramachandran S."
+                    },
+                    {
+                        "name": "Skums P."
+                    },
+                    {
+                        "name": "Tsyvina V."
+                    }
+                ],
+                "date": "2022-10-19T00:00:00Z",
+                "journal": "Cell Systems",
+                "title": "SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework"
+            },
+            "pmcid": "PMC9590096",
+            "pmid": "36265470"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        },
+        {
+            "term": "Virology",
+            "uri": "http://edamontology.org/topic_0781"
+        }
+    ]
+}
diff --git a/data/sourcefinder/sourcefinder.biotools.json b/data/sourcefinder/sourcefinder.biotools.json
new file mode 100644
index 0000000000000..3305d0df003b9
--- /dev/null
+++ b/data/sourcefinder/sourcefinder.biotools.json
@@ -0,0 +1,129 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T22:39:40.498567Z",
+    "biotoolsCURIE": "biotools:sourcefinder",
+    "biotoolsID": "sourcefinder",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "daytan@food.dtu.dk",
+            "name": "Derya Aytan-Aktug",
+            "orcidid": "https://orcid.org/0000-0002-7086-8791",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Vladislav Grigorjev"
+        },
+        {
+            "name": "Frank M. Aarestrup",
+            "orcidid": "https://orcid.org/0000-0002-7116-2723"
+        },
+        {
+            "name": "Ole Lund",
+            "orcidid": "https://orcid.org/0000-0003-1108-0491"
+        }
+    ],
+    "description": "A Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Antimicrobial resistance prediction",
+                    "uri": "http://edamontology.org/operation_3482"
+                },
+                {
+                    "term": "Genome assembly",
+                    "uri": "http://edamontology.org/operation_0525"
+                },
+                {
+                    "term": "Scaffolding",
+                    "uri": "http://edamontology.org/operation_3216"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cge.food.dtu.dk/services/SourceFinder/",
+    "lastUpdate": "2023-01-25T22:39:40.501155Z",
+    "name": "SourceFinder",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1128/SPECTRUM.02641-22",
+            "metadata": {
+                "abstract": "© 2022 American Society for Microbiology. All rights reserved.High-throughput genome sequencing technologies enable the investigation of complex genetic interactions, including the horizontal gene transfer of plasmids and bacteriophages. However, identifying these elements from assembled reads remains challenging due to genome sequence plasticity and the difficulty in assembling complete sequences. In this study, we developed a classifier, using random forest, to identify whether sequences originated from bacterial chromosomes, plasmids, or bacteriophages. The classifier was trained on a diverse collection of 23,211 chromosomal, plasmid, and bacteriophage sequences from hundreds of bacterial species. In order to adapt the classifier to incomplete sequences, each complete sequence was subsampled into 5,000 nucleotide fragments and further subdivided into k-mers. This three-class classifier succeeded in identifying chromosomes, plasmids, and bacteriophages using k-mer distributions of complete and partial genome sequences, including simulated metagenomic scaffolds with minimum performance of 0.939 area under the receiver operating characteristic curve (AUC). This classifier, implemented as SourceFinder, has been made available as an online web service to help the community with predicting the chromosomal, plasmid, and bacteriophage sources of assembled bacterial sequence data (https://cge.food.dtu.dk/ services/SourceFinder/).",
+                "authors": [
+                    {
+                        "name": "Aarestrup F.M."
+                    },
+                    {
+                        "name": "Aytan-Aktug D."
+                    },
+                    {
+                        "name": "Clausen P.T.L.C."
+                    },
+                    {
+                        "name": "Davis J.J."
+                    },
+                    {
+                        "name": "Grigorjev V."
+                    },
+                    {
+                        "name": "Lund O."
+                    },
+                    {
+                        "name": "Munk P."
+                    },
+                    {
+                        "name": "Nguyen M."
+                    },
+                    {
+                        "name": "Szarvas J."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "Microbiology Spectrum",
+                "title": "SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies"
+            },
+            "pmcid": "PMC9769690",
+            "pmid": "36377945"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Mobile genetic elements",
+            "uri": "http://edamontology.org/topic_0798"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/spacelid/spacelid.biotools.json b/data/spacelid/spacelid.biotools.json
new file mode 100644
index 0000000000000..bbe94cf45a69e
--- /dev/null
+++ b/data/spacelid/spacelid.biotools.json
@@ -0,0 +1,135 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T22:15:55.942421Z",
+    "biotoolsCURIE": "biotools:spacelid",
+    "biotoolsID": "spacelid",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "chenyuzong@sz.tsinghua.edu.cn",
+            "name": "Yu Zong Chen",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "chuxiyi@nbu.edu.cn",
+            "name": "Xin-Yi Chu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Junyong Wang"
+        },
+        {
+            "name": "Yufen Zhao"
+        }
+    ],
+    "description": "Database of space life investigations and bioinformatics of microbiology in extreme environments.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bidd.group/spacelid/",
+    "lastUpdate": "2023-01-25T22:15:55.945189Z",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "http://bidd.group/spacelid/browse.php"
+        }
+    ],
+    "name": "SpaceLID",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FMICB.2022.1017773",
+            "metadata": {
+                "abstract": "Copyright © 2022 Wang, Wang, Zeng, Wang, Yu, Wei, Cai, Chu, Chen and Zhao.Biological experiments performed in space crafts like space stations, space shuttles, and recoverable satellites has enabled extensive spaceflight life investigations (SLIs). In particular, SLIs have revealed distinguished space effects on microbial growth, survival, metabolite production, biofilm formation, virulence development and drug resistant mutations. These provide unique perspectives to ground-based microbiology and new opportunities for industrial pharmaceutical and metabolite productions. SLIs are with specialized experimental setups, analysis methods and research outcomes, which can be accessed by established databases National Aeronautics and Space Administration (NASA) Life Science Data Archive, Erasmus Experiment Archive, and NASA GeneLab. The increasing research across diverse fields may be better facilitated by databases of convenient search facilities and categorized presentation of comprehensive contents. We therefore developed the Space Life Investigation Database (SpaceLID) http://bidd.group/spacelid/, which collected SLIs from published academic papers. Currently, this database provides detailed menu search facilities and categorized contents about the studied phenomena, materials, experimental procedures, analysis methods, and research outcomes of 448 SLIs of 90 species (microbial, plant, animal, human), 81 foods and 106 pharmaceuticals, including 232 SLIs not covered by the established databases. The potential applications of SpaceLID are illustrated by the examples of published experimental design and bioinformatic analysis of spaceflight microbial phenomena.",
+                "authors": [
+                    {
+                        "name": "Cai M."
+                    },
+                    {
+                        "name": "Chen Y.Z."
+                    },
+                    {
+                        "name": "Chu X.-Y."
+                    },
+                    {
+                        "name": "Wang J."
+                    },
+                    {
+                        "name": "Wang S."
+                    },
+                    {
+                        "name": "Wang T."
+                    },
+                    {
+                        "name": "Wei Y."
+                    },
+                    {
+                        "name": "Yu Z."
+                    },
+                    {
+                        "name": "Zeng X."
+                    },
+                    {
+                        "name": "Zhao Y."
+                    }
+                ],
+                "date": "2022-11-03T00:00:00Z",
+                "journal": "Frontiers in Microbiology",
+                "title": "Database of space life investigations and bioinformatics of microbiology in extreme environments"
+            },
+            "pmcid": "PMC9668873",
+            "pmid": "36406421"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Microbiology",
+            "uri": "http://edamontology.org/topic_3301"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/spark/spark.biotools.json b/data/spark/spark.biotools.json
index ced60f3ea3dbd..b0d4821154573 100644
--- a/data/spark/spark.biotools.json
+++ b/data/spark/spark.biotools.json
@@ -2,6 +2,9 @@
     "additionDate": "2020-01-09T18:32:41Z",
     "biotoolsCURIE": "biotools:Spark",
     "biotoolsID": "Spark",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
     "confidence_flag": "tool",
     "description": "Apache Spark is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. Melanoma expression analysis with Big Data technologies.",
     "editPermission": {
@@ -26,7 +29,7 @@
         }
     ],
     "homepage": "http://spark.apache.org/",
-    "lastUpdate": "2021-01-16T14:09:48Z",
+    "lastUpdate": "2023-01-26T13:37:07.250455Z",
     "name": "Spark",
     "owner": "Pub2Tools",
     "publication": [
diff --git a/data/sparkec/sparkec.biotools.json b/data/sparkec/sparkec.biotools.json
new file mode 100644
index 0000000000000..c38fc82fce6e2
--- /dev/null
+++ b/data/sparkec/sparkec.biotools.json
@@ -0,0 +1,98 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T22:08:40.634085Z",
+    "biotoolsCURIE": "biotools:sparkec",
+    "biotoolsID": "sparkec",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "roberto.rey.exposito@udc.es",
+            "name": "Roberto R. Expósito",
+            "orcidid": "https://orcid.org/0000-0002-2077-1473",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Juan Touriño"
+        },
+        {
+            "name": "Marco Martínez-Sánchez"
+        }
+    ],
+    "description": "Speeding up alignment-based DNA error correction tools.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/UDC-GAC/SparkEC",
+    "language": [
+        "Java"
+    ],
+    "lastUpdate": "2023-01-25T22:08:40.637878Z",
+    "license": "GPL-3.0",
+    "name": "SparkEC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05013-1",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: In recent years, huge improvements have been made in the context of sequencing genomic data under what is called Next Generation Sequencing (NGS). However, the DNA reads generated by current NGS platforms are not free of errors, which can affect the quality of downstream analysis. Although error correction can be performed as a preprocessing step to overcome this issue, it usually requires long computational times to analyze those large datasets generated nowadays through NGS. Therefore, new software capable of scaling out on a cluster of nodes with high performance is of great importance. Results: In this paper, we present SparkEC, a parallel tool capable of fixing those errors produced during the sequencing process. For this purpose, the algorithms proposed by the CloudEC tool, which is already proved to perform accurate corrections, have been analyzed and optimized to improve their performance by relying on the Apache Spark framework together with the introduction of other enhancements such as the usage of memory-efficient data structures and the avoidance of any input preprocessing. The experimental results have shown significant improvements in the computational times of SparkEC when compared to CloudEC for all the representative datasets and scenarios under evaluation, providing an average and maximum speedups of 4.9× and 11.9× , respectively, over its counterpart. Conclusion: As error correction can take excessive computational time, SparkEC provides a scalable solution for correcting large datasets. Due to its distributed implementation, SparkEC speed can increase with respect to the number of nodes in a cluster. Furthermore, the software is freely available under GPLv3 license and is compatible with different operating systems (Linux, Windows and macOS).",
+                "authors": [
+                    {
+                        "name": "Exposito R.R."
+                    },
+                    {
+                        "name": "Martinez-Sanchez M."
+                    },
+                    {
+                        "name": "Tourino J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "SparkEC: speeding up alignment-based DNA error correction tools"
+            },
+            "pmcid": "PMC9639292",
+            "pmid": "36344928"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Software engineering",
+            "uri": "http://edamontology.org/topic_3372"
+        }
+    ]
+}
diff --git a/data/splace/splace.biotools.json b/data/splace/splace.biotools.json
new file mode 100644
index 0000000000000..e34a15dc3b241
--- /dev/null
+++ b/data/splace/splace.biotools.json
@@ -0,0 +1,89 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T17:21:19.732541Z",
+    "biotoolsCURIE": "biotools:splace",
+    "biotoolsID": "splace",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "renato.renison@gmail.com",
+            "name": "Renato R. M. Oliveira",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Guilherme Oliveira"
+        },
+        {
+            "name": "Santelmo Vasconcelos"
+        }
+    ],
+    "description": "A tool to automatically SPLit, Align, and ConcatenatE genes for phylogenomic inference of several organisms.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phylogenetic inference",
+                    "uri": "http://edamontology.org/operation_0323"
+                },
+                {
+                    "term": "Phylogenetic tree editing",
+                    "uri": "http://edamontology.org/operation_0326"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/reinator/splace",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-13T17:21:19.736590Z",
+    "license": "GPL-3.0",
+    "name": "SPLACE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FBINF.2022.1074802",
+            "pmcid": "PMC9772462",
+            "pmid": "36568700"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Phylogenomics",
+            "uri": "http://edamontology.org/topic_0194"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/spliceai-visual/spliceai-visual.biotools.json b/data/spliceai-visual/spliceai-visual.biotools.json
new file mode 100644
index 0000000000000..a1fd9e08caf82
--- /dev/null
+++ b/data/spliceai-visual/spliceai-visual.biotools.json
@@ -0,0 +1,193 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T10:57:36.809155Z",
+    "biotoolsCURIE": "biotools:spliceai-visual",
+    "biotoolsID": "spliceai-visual",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jean-madeleine.desainteagathe@aphp.fr",
+            "name": "Jean-Madeleine de Sainte Agathe",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Benjamin Cogné"
+        },
+        {
+            "name": "David Baux"
+        },
+        {
+            "name": "Mathilde Filser"
+        }
+    ],
+    "description": "A free online tool to improve SpliceAI splicing variant interpretation.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Alternative splicing prediction",
+                    "uri": "http://edamontology.org/operation_0264"
+                },
+                {
+                    "term": "Splice site prediction",
+                    "uri": "http://edamontology.org/operation_0433"
+                },
+                {
+                    "term": "Variant classification",
+                    "uri": "http://edamontology.org/operation_3225"
+                },
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                },
+                {
+                    "term": "Variant prioritisation",
+                    "uri": "http://edamontology.org/operation_3226"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://mobidetails.iurc.montp.inserm.fr/MD",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T10:57:36.814033Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://tinyurl.com/spliceai-visual"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/Illumina/SpliceAI"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/mobidic/spliceai"
+        }
+    ],
+    "name": "SpliceAI-visual",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S40246-023-00451-1",
+            "metadata": {
+                "abstract": "Abstract: SpliceAI is an open-source deep learning splicing prediction algorithm that has demonstrated in the past few years its high ability to predict splicing defects caused by DNA variations. However, its outputs present several drawbacks: (1) although the numerical values are very convenient for batch filtering, their precise interpretation can be difficult, (2) the outputs are delta scores which can sometimes mask a severe consequence, and (3) complex delins are most often not handled. We present here SpliceAI-visual, a free online tool based on the SpliceAI algorithm, and show how it complements the traditional SpliceAI analysis. First, SpliceAI-visual manipulates raw scores and not delta scores, as the latter can be misleading in certain circumstances. Second, the outcome of SpliceAI-visual is user-friendly thanks to the graphical presentation. Third, SpliceAI-visual is currently one of the only SpliceAI-derived implementations able to annotate complex variants (e.g., complex delins). We report here the benefits of using SpliceAI-visual and demonstrate its relevance in the assessment/modulation of the PVS1 classification criteria. We also show how SpliceAI-visual can elucidate several complex splicing defects taken from the literature but also from unpublished cases. SpliceAI-visual is available as a Google Colab notebook and has also been fully integrated in a free online variant interpretation tool, MobiDetails (https://mobidetails.iurc.montp.inserm.fr/MD). Graphical abstract: [Figure not available: see fulltext.]",
+                "authors": [
+                    {
+                        "name": "Baux D."
+                    },
+                    {
+                        "name": "Bergougnoux A."
+                    },
+                    {
+                        "name": "Besnard T."
+                    },
+                    {
+                        "name": "Buratti J."
+                    },
+                    {
+                        "name": "Clot F."
+                    },
+                    {
+                        "name": "Cogne B."
+                    },
+                    {
+                        "name": "Cossee M."
+                    },
+                    {
+                        "name": "Filser M."
+                    },
+                    {
+                        "name": "Frobert L."
+                    },
+                    {
+                        "name": "Gueguen P."
+                    },
+                    {
+                        "name": "Isidor B."
+                    },
+                    {
+                        "name": "Kalatzis V."
+                    },
+                    {
+                        "name": "Le Guern E."
+                    },
+                    {
+                        "name": "Lejeune E."
+                    },
+                    {
+                        "name": "Masingue M."
+                    },
+                    {
+                        "name": "Pasquier F."
+                    },
+                    {
+                        "name": "Perrin A."
+                    },
+                    {
+                        "name": "Rendu J."
+                    },
+                    {
+                        "name": "Roux A.-F."
+                    },
+                    {
+                        "name": "Van Goethem C."
+                    },
+                    {
+                        "name": "Verebi C."
+                    },
+                    {
+                        "name": "de Sainte Agathe J.-M."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Human Genomics",
+                "title": "SpliceAI-visual: a free online tool to improve SpliceAI splicing variant interpretation"
+            },
+            "pmcid": "PMC9912651",
+            "pmid": "36765386"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/split-pin/split-pin.biotools.json b/data/split-pin/split-pin.biotools.json
new file mode 100644
index 0000000000000..9e73739b20a96
--- /dev/null
+++ b/data/split-pin/split-pin.biotools.json
@@ -0,0 +1,123 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T10:40:04.297695Z",
+    "biotoolsCURIE": "biotools:split-pin",
+    "biotoolsID": "split-pin",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "luca.lanzano@unict.it",
+            "name": "Luca Lanzanò",
+            "orcidid": "https://orcid.org/0000-0001-6539-394X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Angelita Costantino"
+        },
+        {
+            "name": "Elisabetta Di Franco"
+        },
+        {
+            "name": "Alberto Diaspro",
+            "orcidid": "http://orcid.org/0000-0002-4916-5928"
+        }
+    ],
+    "description": "SPLIT-PIN software enabling confocal and super-resolution imaging with a virtually closed pinhole.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/llanzano/SPLITPIN",
+    "language": [
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-03-17T10:40:04.302377Z",
+    "license": "Not licensed",
+    "name": "SPLIT-PIN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41598-023-29951-9",
+            "metadata": {
+                "abstract": "In point-scanning microscopy, optical sectioning is achieved using a small aperture placed in front of the detector, i.e. the detection pinhole, which rejects the out-of-focus background. The maximum level of optical sectioning is theoretically obtained for the minimum size of the pinhole aperture, but this is normally prevented by the dramatic reduction of the detected signal when the pinhole is closed, leading to a compromise between axial resolution and signal-to-noise ratio. We have recently demonstrated that, instead of closing the pinhole, one can reach a similar level of optical sectioning by tuning the pinhole size in a confocal microscope and by analyzing the resulting image series. The method, consisting in the application of the separation of photons by lifetime tuning (SPLIT) algorithm to series of images acquired with tunable pinhole size, is called SPLIT-pinhole (SPLIT-PIN). Here, we share and describe a SPLIT-PIN software for the processing of series of images acquired at tunable pinhole size, which generates images with reduced out-of-focus background. The software can be used on series of at least two images acquired on available commercial microscopes equipped with a tunable pinhole, including confocal and stimulated emission depletion (STED) microscopes. We demonstrate applicability on different types of imaging modalities: (1) confocal imaging of DNA in a non-adherent cell line; (2) removal of out-of-focus background in super-resolved STED microscopy; (3) imaging of live intestinal organoids stained with a membrane dye.",
+                "authors": [
+                    {
+                        "name": "Bianchini P."
+                    },
+                    {
+                        "name": "Cerutti E."
+                    },
+                    {
+                        "name": "Costantino A."
+                    },
+                    {
+                        "name": "D'Amico M."
+                    },
+                    {
+                        "name": "Di Franco E."
+                    },
+                    {
+                        "name": "Diaspro A."
+                    },
+                    {
+                        "name": "Gulisano M."
+                    },
+                    {
+                        "name": "Lanzano L."
+                    },
+                    {
+                        "name": "Privitera A.P."
+                    },
+                    {
+                        "name": "Vicidomini G."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Scientific Reports",
+                "title": "SPLIT-PIN software enabling confocal and super-resolution imaging with a virtually closed pinhole"
+            },
+            "pmcid": "PMC9931717",
+            "pmid": "36792719"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Biophysics",
+            "uri": "http://edamontology.org/topic_3306"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        }
+    ]
+}
diff --git a/data/splnmtf/splnmtf.biotools.json b/data/splnmtf/splnmtf.biotools.json
new file mode 100644
index 0000000000000..bf1c17814aa2c
--- /dev/null
+++ b/data/splnmtf/splnmtf.biotools.json
@@ -0,0 +1,104 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T21:58:32.268997Z",
+    "biotoolsCURIE": "biotools:splnmtf",
+    "biotoolsID": "splnmtf",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Qi Dang"
+        },
+        {
+            "name": "Xiaoying Liu"
+        },
+        {
+            "name": "Shengli Xie",
+            "orcidid": "https://orcid.org/0000-0003-2041-5214"
+        },
+        {
+            "name": "Yong Liang",
+            "orcidid": "https://orcid.org/0000-0002-4791-4307"
+        }
+    ],
+    "description": "Improved Computational Drug-Repositioning by Self-Paced Non-Negative Matrix Tri-Factorization.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/qi0906/SPLNMTF",
+    "language": [
+        "MATLAB",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T21:58:32.272531Z",
+    "license": "Not licensed",
+    "name": "SPLNMTF",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1109/TCBB.2022.3225300",
+            "metadata": {
+                "abstract": "IEEEDrug repositioning (DR) is a strategy to find new targets for existing drugs, which plays an important role in reducing the costs, time, and risk of traditional drug development. Recently, the matrix factorization approach has been widely used in the field of DR prediction. Nevertheless, there are still two challenges: 1) Learning ability deficiencies, the model cannot accurately predict more potential associations. 2) Easy to fall into a bad local optimal solution, the model tends to get a suboptimal result. In this study, we propose a self-paced non-negative matrix tri-factorization (SPLNMTF) model, which integrates three types of different biological data from patients, genes, and drugs into a heterogeneous network through non-negative matrix tri-factorization, thereby learning more information to improve the learning ability of the model. In the meantime, the SPLNMTF model sequentially includes samples into training from easy (high-quality) to complex (low-quality) in the soft weighting way, which effectively alleviates falling into a bad local optimal solution to improve the prediction performance of the model. The experimental results on two real datasets of ovarian cancer and acute myeloid leukemia (AML) show that SPLNMTF outperforms the other eight state-of-the-art models and gets better prediction performance in drug repositioning. The data and source code are available at: <uri>https://github.com/qi0906/SPLNMTF</uri>.",
+                "authors": [
+                    {
+                        "name": "Dang Q."
+                    },
+                    {
+                        "name": "Liang Y."
+                    },
+                    {
+                        "name": "Ling C."
+                    },
+                    {
+                        "name": "Liu X."
+                    },
+                    {
+                        "name": "Miao R."
+                    },
+                    {
+                        "name": "Ouyang D."
+                    },
+                    {
+                        "name": "Xie S."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
+                "title": "Improved Computational Drug-Repositioning by Self-Paced Non-Negative Matrix Tri-Factorization"
+            },
+            "pmid": "36445996"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Drug development",
+            "uri": "http://edamontology.org/topic_3373"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/spoke/spoke.biotools.json b/data/spoke/spoke.biotools.json
new file mode 100644
index 0000000000000..ffcb7138f43ae
--- /dev/null
+++ b/data/spoke/spoke.biotools.json
@@ -0,0 +1,172 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T10:34:13.962015Z",
+    "biotoolsCURIE": "biotools:spoke",
+    "biotoolsID": "spoke",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "sergio.baranzini@ucsf.edu",
+            "name": "Sergio E Baranzini",
+            "orcidid": "https://orcid.org/0000-0003-0067-194X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Charlotte A Nelson"
+        },
+        {
+            "name": "John H Morris"
+        },
+        {
+            "name": "Karthik Soman"
+        }
+    ],
+    "description": "The scalable precision medicine open knowledge engine (SPOKE).",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://spoke.rbvi.ucsf.edu",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T10:34:13.967311Z",
+    "name": "SPOKE",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD080",
+            "metadata": {
+                "abstract": "MOTIVATION: Knowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information. RESULTS: In this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a 'parent table' of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts. AVAILABILITY AND IMPLEMENTATION: The SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Akbas R.E."
+                    },
+                    {
+                        "name": "Baranzini S.E."
+                    },
+                    {
+                        "name": "Bharat K."
+                    },
+                    {
+                        "name": "Cerono G."
+                    },
+                    {
+                        "name": "Chakraborty A."
+                    },
+                    {
+                        "name": "Costes S.V."
+                    },
+                    {
+                        "name": "Hardi J."
+                    },
+                    {
+                        "name": "Harroud A."
+                    },
+                    {
+                        "name": "Huang C.C."
+                    },
+                    {
+                        "name": "Huang S."
+                    },
+                    {
+                        "name": "Israni S."
+                    },
+                    {
+                        "name": "Keiser M."
+                    },
+                    {
+                        "name": "Mardirossian T."
+                    },
+                    {
+                        "name": "Meng E.C."
+                    },
+                    {
+                        "name": "Morris J.H."
+                    },
+                    {
+                        "name": "Musen M."
+                    },
+                    {
+                        "name": "Nelson C.A."
+                    },
+                    {
+                        "name": "Pico A.R."
+                    },
+                    {
+                        "name": "Rizk-Jackson A."
+                    },
+                    {
+                        "name": "Rose P.W."
+                    },
+                    {
+                        "name": "Sanders L."
+                    },
+                    {
+                        "name": "Schenk G."
+                    },
+                    {
+                        "name": "Shi Y."
+                    },
+                    {
+                        "name": "Smith B."
+                    },
+                    {
+                        "name": "Soman K."
+                    },
+                    {
+                        "name": "Tang A."
+                    },
+                    {
+                        "name": "Zhou X."
+                    }
+                ],
+                "date": "2023-02-03T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information"
+            },
+            "pmcid": "PMC9940622",
+            "pmid": "36759942"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Data mining",
+            "uri": "http://edamontology.org/topic_3473"
+        },
+        {
+            "term": "Endocrinology and metabolism",
+            "uri": "http://edamontology.org/topic_3407"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        }
+    ]
+}
diff --git a/data/spot-disorder-single/spot-disorder-single.biotools.json b/data/spot-disorder-single/spot-disorder-single.biotools.json
index 46c3ff1cda1e1..4e4142aaaf314 100644
--- a/data/spot-disorder-single/spot-disorder-single.biotools.json
+++ b/data/spot-disorder-single/spot-disorder-single.biotools.json
@@ -3,6 +3,7 @@
     "additionDate": "2022-08-31T09:23:45.288170Z",
     "biotoolsCURIE": "biotools:spot-disorder-single",
     "biotoolsID": "spot-disorder-single",
+    "confidence_flag": "tool",
     "cost": "Free of charge",
     "credit": [
         {
@@ -12,6 +13,10 @@
         {
             "email": "yaoqi.zhou@griffith.edu.au",
             "name": "Yaoqi Zhou"
+        },
+        {
+            "name": "Jack Hanson",
+            "orcidid": "http://orcid.org/0000-0001-6956-6748"
         }
     ],
     "description": "SPOT-Disorder-Single is a single-sequence method that is more accurate than SPOT-Disorder (a profile-based method) for proteins with few homologous sequences and comparable for proteins in predicting long-disordered regions.",
@@ -30,6 +35,20 @@
     },
     "function": [
         {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
             "operation": [
                 {
                     "term": "Homology-based gene prediction",
@@ -39,10 +58,11 @@
         }
     ],
     "homepage": "http://sparks-lab.org/server/spot-disorder-single/",
-    "lastUpdate": "2022-12-09T23:00:58.265155Z",
+    "lastUpdate": "2023-01-13T15:56:18.663132Z",
     "name": "SPOT-Disorder-Single",
     "operatingSystem": [
-        "Linux"
+        "Linux",
+        "Windows"
     ],
     "owner": "daniela.mereuta",
     "publication": [
@@ -61,7 +81,7 @@
                         "name": "Zhou Y."
                     }
                 ],
-                "citationCount": 38,
+                "citationCount": 39,
                 "date": "2018-11-26T00:00:00Z",
                 "journal": "Journal of Chemical Information and Modeling",
                 "title": "Accurate Single-Sequence Prediction of Protein Intrinsic Disorder by an Ensemble of Deep Recurrent and Convolutional Architectures"
diff --git a/data/spot-disorder/spot-disorder.biotools.json b/data/spot-disorder/spot-disorder.biotools.json
index f274230b6c327..c85b1b4b9b4cd 100644
--- a/data/spot-disorder/spot-disorder.biotools.json
+++ b/data/spot-disorder/spot-disorder.biotools.json
@@ -3,6 +3,7 @@
     "additionDate": "2022-08-31T09:39:10.246272Z",
     "biotoolsCURIE": "biotools:spot-disorder",
     "biotoolsID": "spot-disorder",
+    "confidence_flag": "tool",
     "cost": "Free of charge",
     "credit": [
         {
@@ -40,6 +41,18 @@
                         "term": "Protein features",
                         "uri": "http://edamontology.org/data_1277"
                     }
+                },
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
                 }
             ],
             "operation": [
@@ -51,10 +64,11 @@
         }
     ],
     "homepage": "http://sparks-lab.org/server/spot-disorder/",
-    "lastUpdate": "2022-12-09T22:59:10.010460Z",
+    "lastUpdate": "2023-01-13T15:55:40.416695Z",
     "name": "SPOT-Disorder",
     "operatingSystem": [
-        "Linux"
+        "Linux",
+        "Windows"
     ],
     "owner": "daniela.mereuta",
     "publication": [
@@ -76,7 +90,7 @@
                         "name": "Zhou Y."
                     }
                 ],
-                "citationCount": 179,
+                "citationCount": 181,
                 "date": "2017-01-01T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks"
diff --git a/data/spread_4/spread_4.biotools.json b/data/spread_4/spread_4.biotools.json
new file mode 100644
index 0000000000000..7a823c418612b
--- /dev/null
+++ b/data/spread_4/spread_4.biotools.json
@@ -0,0 +1,89 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T21:50:17.781436Z",
+    "biotoolsCURIE": "biotools:spread_4",
+    "biotoolsID": "spread_4",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "philippe.lemey@kuleuven.be",
+            "name": "Philippe Lemey",
+            "orcidid": "https://orcid.org/0000-0003-2826-5353",
+            "typeEntity": "Person",
+            "url": "https://orcid.org/0000-0003-2826-5353"
+        },
+        {
+            "name": "Filip Bielejec"
+        },
+        {
+            "name": "Guy Baele"
+        },
+        {
+            "name": "Kanika D Nahata"
+        }
+    ],
+    "description": "Online visualisation of pathogen phylogeographic reconstructions.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Ancestral reconstruction",
+                    "uri": "http://edamontology.org/operation_3745"
+                },
+                {
+                    "term": "Phylogenetic reconstruction",
+                    "uri": "http://edamontology.org/operation_3478"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://spreadviz.org",
+    "lastUpdate": "2023-01-25T21:50:17.784360Z",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://beast.community/spread4"
+        }
+    ],
+    "name": "SPREAD 4",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/VE/VEAC088",
+            "pmcid": "PMC9615431",
+            "pmid": "36325034"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/spss/spss.biotools.json b/data/spss/spss.biotools.json
new file mode 100644
index 0000000000000..40f0fc7d5f816
--- /dev/null
+++ b/data/spss/spss.biotools.json
@@ -0,0 +1,49 @@
+{
+    "additionDate": "2023-01-31T07:09:17.307014Z",
+    "biotoolsCURIE": "biotools:spss",
+    "biotoolsID": "spss",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "url": "https://www.ibm.com/cloud/support?lnk=flathl"
+        }
+    ],
+    "description": "The IBM SPSS software platform offers advanced statistical analysis, a vast library of machine learning algorithms, text analysis, open-source extensibility, integration with big data and seamless deployment into applications.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://www.ibm.com/docs/en?lnk=flatitem"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Statistical calculation",
+                    "uri": "http://edamontology.org/operation_2238"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.ibm.com/spss",
+    "lastUpdate": "2023-02-01T13:05:54.655619Z",
+    "license": "Proprietary",
+    "link": [
+        {
+            "note": "Why IBM® SPSS® software?",
+            "type": [
+                "Other"
+            ],
+            "url": "https://www.ibm.com/spss"
+        }
+    ],
+    "name": "SPSS",
+    "owner": "iacs-biocomputacion"
+}
diff --git a/data/srgs/srgs.biotools.json b/data/srgs/srgs.biotools.json
new file mode 100644
index 0000000000000..4bc9aa733ffb1
--- /dev/null
+++ b/data/srgs/srgs.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T17:16:40.012580Z",
+    "biotoolsCURIE": "biotools:srgs",
+    "biotoolsID": "srgs",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jtguan@xmu.edu.cn",
+            "name": "Jinting Guan",
+            "orcidid": "https://orcid.org/0000-0001-9433-5677",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Guoli Ji"
+        },
+        {
+            "name": "Yan Zhuang"
+        },
+        {
+            "name": "Yang Wang"
+        },
+        {
+            "name": "Yongjie Wang"
+        }
+    ],
+    "description": "Sparse partial least squares-based recursive gene selection for gene regulatory network inference.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene regulatory network analysis",
+                    "uri": "http://edamontology.org/operation_1781"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Weighted correlation network analysis",
+                    "uri": "http://edamontology.org/operation_3766"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/JGuan-lab/SRGS",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-13T17:16:40.015283Z",
+    "license": "Not licensed",
+    "name": "SRGS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12864-022-09020-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: The identification of gene regulatory networks (GRNs) facilitates the understanding of the underlying molecular mechanism of various biological processes and complex diseases. With the availability of single-cell RNA sequencing data, it is essential to infer GRNs from single-cell expression. Although some GRN methods originally developed for bulk expression data can be applicable to single-cell data and several single-cell specific GRN algorithms were developed, recent benchmarking studies have emphasized the need of developing more accurate and robust GRN modeling methods that are compatible for single-cell expression data. Results: We present SRGS, SPLS (sparse partial least squares)-based recursive gene selection, to infer GRNs from bulk or single-cell expression data. SRGS recursively selects and scores the genes which may have regulations on the considered target gene based on SPLS. When dealing with gene expression data with dropouts, we randomly scramble samples, set some values in the expression matrix to zeroes, and generate multiple copies of data through multiple iterations to make SRGS more robust. We test SRGS on different kinds of expression data, including simulated bulk data, simulated single-cell data without and with dropouts, and experimental single-cell data, and also compared with the existing GRN methods, including the ones originally developed for bulk data, the ones developed specifically for single-cell data, and even the ones recommended by recent benchmarking studies. Conclusions: It has been shown that SRGS is competitive with the existing GRN methods and effective in the gene regulatory network inference from bulk or single-cell gene expression data. SRGS is available at: https://github.com/JGuan-lab/SRGS.",
+                "authors": [
+                    {
+                        "name": "Guan J."
+                    },
+                    {
+                        "name": "Ji G."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Zhuang Y."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Genomics",
+                "title": "SRGS: sparse partial least squares-based recursive gene selection for gene regulatory network inference"
+            },
+            "pmcid": "PMC9710113",
+            "pmid": "36451086"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/srns/srns.biotools.json b/data/srns/srns.biotools.json
new file mode 100644
index 0000000000000..869fa00cc87de
--- /dev/null
+++ b/data/srns/srns.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T10:27:47.672684Z",
+    "biotoolsCURIE": "biotools:srns",
+    "biotoolsID": "srns",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jianghj@zju.edu.cn",
+            "name": "Hangjin Jiang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "maojh88@zju.edu.cn",
+            "name": "Jianhua Mao",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Huihui Liu"
+        },
+        {
+            "name": "Qing Ye"
+        },
+        {
+            "name": "Yuzhou Li"
+        }
+    ],
+    "description": "Machine learning models for predicting steroid-resistant of nephrotic syndrome.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature selection",
+                    "uri": "http://edamontology.org/operation_3936"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://datalinkx.shinyapps.io/srns",
+    "lastUpdate": "2023-03-17T10:27:47.676970Z",
+    "name": "SRNS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FIMMU.2023.1090241",
+            "metadata": {
+                "abstract": "Background: In the absence of effective measures to predict steroid responsiveness, patients with nonhereditary steroid-resistant nephrotic syndrome (SRNS) have a significantly increased risk of progression to end-stage renal disease. In view of the poor outcomes of SRNS, it is urgent to identify the steroid responsiveness of idiopathic nephrotic syndrome (INS) early. Methods: To build a prediction model for SRNS, we collected 91 subjects; 57 of them had steroid-sensitive nephrotic syndrome, and the others had SRNS. For each subject, 87 clinical variables were measured. In general, only a small part of these variables is informative to SRNS. Thus, we proposed a new variable selection framework including a penalized regression approach (named MLR+TLP) to select variables having a linear effect on the SRNS and a nonparametric screening method (MAC) to select variables having a nonlinear marginal (joint) effect on the SRNS. Thereafter, considering the correlation between selected clinical variables, we used a stepwise method to build our final model for predicting SRNS. In addition, a statistical testing procedure is proposed to test the overfitting of the proposed model. Results: Twenty-six clinical variables were selected to be informative to SRNS, and an SVM model was built to predict SRNS with a leave-one-out cross-validation (LOO-CV) accuracy of 95.2% (overfitting p value<0.005). To make the model more useful, we incorporate prior medical information into the model and consider the correlation between selected variables. Then, a reduced SVM model including only eight clinical variables (erythrocyte sedimentation rate, urine occult blood, percentage of neutrophils, immunoglobulin A, cholesterol, vinculin autoantibody, aspartate aminotransferase, and prolonged prothrombin time) was built to have a LOO-CV accuracy of 92.8% (overfitting p value<0.005). The validation cohort showed that the reduced model obtained an accuracy of 94.0% (overfitting p value<0.005), with a sensitivity of 90.0% and a specificity of 96.7%. Notably, vinculin autoantibody is the only podocyte autoantibody included in this model. It is linearly related to steroid responsiveness. Finally, our model is freely available as a user-friendly web tool at https://datalinkx.shinyapps.io/srns/. Conclusion: The SRNS prediction model constructed in this study comprehensively and objectively evaluates the internal conditions and disease status of INS patients and will provide scientific guidance for selecting treatment methods for children with nonhereditary SRNS.",
+                "authors": [
+                    {
+                        "name": "Jiang H."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Liu H."
+                    },
+                    {
+                        "name": "Mao J."
+                    },
+                    {
+                        "name": "Ye Q."
+                    }
+                ],
+                "date": "2023-01-26T00:00:00Z",
+                "journal": "Frontiers in Immunology",
+                "title": "Machine learning models for predicting steroid-resistant of nephrotic syndrome"
+            },
+            "pmcid": "PMC9911108",
+            "pmid": "36776850"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cytometry",
+            "uri": "http://edamontology.org/topic_3934"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/ssd-kd/ssd-kd.biotools.json b/data/ssd-kd/ssd-kd.biotools.json
new file mode 100644
index 0000000000000..445448aae6138
--- /dev/null
+++ b/data/ssd-kd/ssd-kd.biotools.json
@@ -0,0 +1,99 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T17:09:23.129542Z",
+    "biotoolsCURIE": "biotools:ssd-kd",
+    "biotoolsID": "ssd-kd",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Chunyan Miao"
+        },
+        {
+            "name": "Yongwei Wang",
+            "orcidid": "https://orcid.org/0000-0001-9712-8964"
+        },
+        {
+            "name": "Yuheng Wang",
+            "orcidid": "https://orcid.org/0000-0001-5252-7005"
+        },
+        {
+            "name": "Z. Jane Wang",
+            "orcidid": "https://orcid.org/0000-0002-3791-0249"
+        }
+    ],
+    "description": "A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/enkiwang/Portable-Skin-Lesion-Diagnosis",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T17:12:01.404930Z",
+    "license": "MIT",
+    "name": "SSD-KD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.MEDIA.2022.102693",
+            "metadata": {
+                "abstract": "© 2022 Elsevier B.V.Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burden worldwide. Over the last few years, computer-aided diagnosis has been rapidly developed and make great progress in healthcare and medical practices due to the advances in artificial intelligence, particularly with the adoption of convolutional neural networks. However, most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. In this case, the knowledge distillation (KD) method has been proven as an efficient tool to help improve the adaptability of lightweight models under limited resources, meanwhile keeping a high-level representation capability. To bridge the gap, this study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin disease classification. Our method models an intra-instance relational feature representation and integrates it with existing KD research. A dual relational knowledge distillation architecture is self-supervised trained while the weighted softened outputs are also exploited to enable the student model to capture richer knowledge from the teacher model. To demonstrate the effectiveness of our method, we conduct experiments on ISIC 2019, a large-scale open-accessed benchmark of skin diseases dermoscopic images. Experiments show that our distilled MobileNetV2 can achieve an accuracy as high as 85% for the classification tasks of 8 different skin diseases with minimal parameters and computing requirements. Ablation studies confirm the effectiveness of our intra- and inter-instance relational knowledge integration strategy. Compared with state-of-the-art knowledge distillation techniques, the proposed method demonstrates improved performance. To the best of our knowledge, this is the first deep knowledge distillation application for multi-disease classification on the large-scale dermoscopy database. Our codes and models are available at https://github.com/enkiwang/Portable-Skin-Lesion-Diagnosis.",
+                "authors": [
+                    {
+                        "name": "Cai J."
+                    },
+                    {
+                        "name": "Lee T.K."
+                    },
+                    {
+                        "name": "Miao C."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Wang Z.J."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "Medical Image Analysis",
+                "title": "SSD-KD: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images"
+            },
+            "pmid": "36462373"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Dermatology",
+            "uri": "http://edamontology.org/topic_3404"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        }
+    ]
+}
diff --git a/data/sspa_py/sspa_py.biotools.json b/data/sspa_py/sspa_py.biotools.json
new file mode 100644
index 0000000000000..2513b3252dd81
--- /dev/null
+++ b/data/sspa_py/sspa_py.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-20T00:54:45.074253Z",
+    "biotoolsCURIE": "biotools:sspa_py",
+    "biotoolsID": "sspa_py",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Cecilia Wieder",
+            "orcidid": "http://orcid.org/0000-0003-1548-4346"
+        },
+        {
+            "name": "Rachel PJ Lai",
+            "orcidid": "http://orcid.org/0000-0003-3418-850X"
+        },
+        {
+            "name": "Timothy Ebbels",
+            "orcidid": "http://orcid.org/0000-0002-3372-8423"
+        }
+    ],
+    "description": "sspa provides a Python interface for metabolomics pathway analysis. In addition to conventional methods over-representation analysis (ORA) and gene/metabolite set enrichment analysis (GSEA), it also provides a wide range of single-sample pathway analysis (ssPA) methods.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://cwieder.github.io/py-ssPA/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Network visualisation",
+                    "uri": "http://edamontology.org/operation_3925"
+                },
+                {
+                    "term": "Pathway analysis",
+                    "uri": "http://edamontology.org/operation_3928"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pypi.org/project/sspa/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-20T00:54:45.076784Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://zenodo.org/record/7515344"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/cwieder/py-ssPA"
+        }
+    ],
+    "name": "ssPA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/s12859-022-05005-1",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Single sample pathway analysis (ssPA) transforms molecular level omics data to the pathway level, enabling the discovery of patient-specific pathway signatures. Compared to conventional pathway analysis, ssPA overcomes the limitations by enabling multi-group comparisons, alongside facilitating numerous downstream analyses such as pathway-based machine learning. While in transcriptomics ssPA is a widely used technique, there is little literature evaluating its suitability for metabolomics. Here we provide a benchmark of established ssPA methods (ssGSEA, GSVA, SVD (PLAGE), and z-score) alongside the evaluation of two novel methods we propose: ssClustPA and kPCA, using semi-synthetic metabolomics data. We then demonstrate how ssPA can facilitate pathway-based interpretation of metabolomics data by performing a case-study on inflammatory bowel disease mass spectrometry data, using clustering to determine subtype-specific pathway signatures. Results: While GSEA-based and z-score methods outperformed the others in terms of recall, clustering/dimensionality reduction-based methods provided higher precision at moderate-to-high effect sizes. A case study applying ssPA to inflammatory bowel disease data demonstrates how these methods yield a much richer depth of interpretation than conventional approaches, for example by clustering pathway scores to visualise a pathway-based patient subtype-specific correlation network. We also developed the sspa python package (freely available at https://pypi.org/project/sspa/), providing implementations of all the methods benchmarked in this study. Conclusion: This work underscores the value ssPA methods can add to metabolomic studies and provides a useful reference for those wishing to apply ssPA methods to metabolomics data.",
+                "authors": [
+                    {
+                        "name": "Ebbels T.M.D."
+                    },
+                    {
+                        "name": "Lai R.P.J."
+                    },
+                    {
+                        "name": "Wieder C."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "Single sample pathway analysis in metabolomics: performance evaluation and application"
+            },
+            "pmcid": "PMC9664704",
+            "pmid": "36376837"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/stackcirrnapred/stackcirrnapred.biotools.json b/data/stackcirrnapred/stackcirrnapred.biotools.json
new file mode 100644
index 0000000000000..dbd3c6cd8f429
--- /dev/null
+++ b/data/stackcirrnapred/stackcirrnapred.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T17:03:37.118089Z",
+    "biotoolsCURIE": "biotools:stackcirrnapred",
+    "biotoolsID": "stackcirrnapred",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ghwang@hit.edu.cn",
+            "name": "Guohua Wang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jie Li"
+        },
+        {
+            "name": "Xin Wang"
+        },
+        {
+            "name": "Yadong Liu"
+        }
+    ],
+    "description": "Computational classification of long circRNA from other lncRNA based on stacking strategy..",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Feature selection",
+                    "uri": "http://edamontology.org/operation_3936"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/xwang1427/StackCirRNAPred",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T17:03:37.121306Z",
+    "license": "Not licensed",
+    "name": "StackCirRNAPred",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05118-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: CircRNAs are essential for the regulation of post-transcriptional gene expression, including as miRNA sponges, and play an important role in disease development. Some computational tools have been proposed recently to predict circRNA, since only one classifier is used, there is still much that can be done to improve the performance. Results: StackCirRNAPred was proposed, the computational classification of long circRNA from other lncRNA based on stacking strategy. In order to cope with the potential problem that a single feature might not be able to distinguish circRNA well from other lncRNA, we first extracted features from different sources, including nucleic acid composition, sequence spatial features and physicochemical properties, Alu and tandem repeats. We innovatively apply the stacking strategy to integrate the more advantageous classifiers of RF, LightGBM, XGBoost. This allows the model to incorporate these features more flexibly. StackCirRNAPred was found to be significantly better than other tools, with precision, accuracy, F1, recall and MCC of 0.843, 0.833, 0.831, 0.819 and 0.666 respectively. We tested it directly on the mouse dataset. StackCirRNAPred was still significantly better than other methods, with precision, accuracy, F1, recall and MCC of 0.837, 0.839, 0.839, 0.841, 0.677. Conclusions: We proposed StackCirRNAPred based on stacking strategy to distinguish long circRNAs from other lncRNAs. With the test results demonstrating the validity and robustness of StackCirRNAPred, we hope StackCirRNAPred will complement existing circRNA prediction methods and is helpful in down-stream research.",
+                "authors": [
+                    {
+                        "name": "Li J."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Wang G."
+                    },
+                    {
+                        "name": "Wang X."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "StackCirRNAPred: computational classification of long circRNA from other lncRNA based on stacking strategy"
+            },
+            "pmcid": "PMC9793644",
+            "pmid": "36575368"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/stapaminer/stapaminer.biotools.json b/data/stapaminer/stapaminer.biotools.json
new file mode 100644
index 0000000000000..5c33ac166dea8
--- /dev/null
+++ b/data/stapaminer/stapaminer.biotools.json
@@ -0,0 +1,94 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T14:03:22.255389Z",
+    "biotoolsCURIE": "biotools:stapaminer",
+    "biotoolsID": "stapaminer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xhwu@suda.edu.cn",
+            "name": "Xiaohui Wu",
+            "orcidid": "http://orcid.org/0000-0003-0356-7785",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Guoli Ji",
+            "orcidid": "http://orcid.org/0000-0002-7811-2710"
+        },
+        {
+            "name": "Qi Tang",
+            "orcidid": "http://orcid.org/0000-0002-6869-8527"
+        },
+        {
+            "name": "Shuting Xia",
+            "orcidid": "http://orcid.org/0000-0003-2577-4339"
+        }
+    ],
+    "description": "Mining Spatial Patterns of Alternative Polyadenylation for Spatially Resolved Transcriptomic Studies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/BMILAB/stAPAminer",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-27T14:03:22.257917Z",
+    "license": "MIT",
+    "name": "stAPAminer",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.gpb.2023.01.003",
+            "pmid": "36669641"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "RNA splicing",
+            "uri": "http://edamontology.org/topic_3320"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/starmap/starmap.biotools.json b/data/starmap/starmap.biotools.json
new file mode 100644
index 0000000000000..9415913ef62b1
--- /dev/null
+++ b/data/starmap/starmap.biotools.json
@@ -0,0 +1,122 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T21:45:13.727287Z",
+    "biotoolsCURIE": "biotools:starmap",
+    "biotoolsID": "starmap",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Frank DiMaio"
+        },
+        {
+            "name": "Vadim Kotov"
+        },
+        {
+            "name": "Thomas C. Marlovits",
+            "orcidid": "http://orcid.org/0000-0001-8106-4038"
+        },
+        {
+            "name": "Wolfgang Lugmayr",
+            "orcidid": "http://orcid.org/0000-0001-8501-8637"
+        }
+    ],
+    "description": "A user-friendly workflow for Rosetta-driven molecular structure refinement.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
+                },
+                {
+                    "term": "Molecular model refinement",
+                    "uri": "http://edamontology.org/operation_0322"
+                },
+                {
+                    "term": "Protein structure validation",
+                    "uri": "http://edamontology.org/operation_0321"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/wlugmayr/chimerax-starmap",
+    "language": [
+        "Perl",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T21:45:13.729805Z",
+    "license": "BSD-2-Clause",
+    "name": "StarMap",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41596-022-00757-9",
+            "metadata": {
+                "abstract": "© 2022, Springer Nature Limited.Cryogenic electron microscopy (cryo-EM) data represent density maps of macromolecular systems at atomic or near-atomic resolution. However, building and refining 3D atomic models by using data from cryo-EM maps is not straightforward and requires significant hands-on experience and manual intervention. We recently developed StarMap, an easy-to-use interface between the popular structural display program ChimeraX and Rosetta, a powerful molecular modeling engine. StarMap offers a general approach for refining structural models of biological macromolecules into cryo-EM density maps by combining Monte Carlo sampling with local density-guided optimization, Rosetta-based all-atom refinement and real-space B-factor calculations in a straightforward workflow. StarMap includes options for structural symmetry, local refinements and independent model validation. The overall quality of the refinement and the structure resolution is then assessed via analytical outputs, such as magnification calibration (pixel size calibration) and Fourier shell correlations. Z-scores reported by StarMap provide an easily interpretable indicator of the goodness of fit for each residue and can be plotted to evaluate structural models and improve local residue refinements, as well as to identify flexible regions and potentially functional sites in large macromolecular complexes. The protocol requires general computer skills, without the need for coding expertise, because most parts of the workflow can be operated by clicking tabs within the ChimeraX graphical user interface. Time requirements for the model refinement depend on the size and quality of the input data; however, this step can typically be completed within 1 d. The analytical parts of the workflow are completed within minutes.",
+                "authors": [
+                    {
+                        "name": "DiMaio F."
+                    },
+                    {
+                        "name": "Goessweiner-Mohr N."
+                    },
+                    {
+                        "name": "Kotov V."
+                    },
+                    {
+                        "name": "Lugmayr W."
+                    },
+                    {
+                        "name": "Marlovits T.C."
+                    },
+                    {
+                        "name": "Wald J."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Nature Protocols",
+                "title": "StarMap: a user-friendly workflow for Rosetta-driven molecular structure refinement"
+            },
+            "pmid": "36323866"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Electron microscopy",
+            "uri": "http://edamontology.org/topic_0611"
+        },
+        {
+            "term": "Molecular modelling",
+            "uri": "http://edamontology.org/topic_2275"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        },
+        {
+            "term": "X-ray diffraction",
+            "uri": "http://edamontology.org/topic_2828"
+        }
+    ]
+}
diff --git a/data/starpepdb/starpepdb.biotools.json b/data/starpepdb/starpepdb.biotools.json
new file mode 100644
index 0000000000000..8ee99e42c69b7
--- /dev/null
+++ b/data/starpepdb/starpepdb.biotools.json
@@ -0,0 +1,127 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T16:57:26.426169Z",
+    "biotoolsCURIE": "biotools:starpepdb",
+    "biotoolsID": "starpepdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ymarrero@usfq.edu.ec",
+            "name": "Yovani Marrero-Ponce",
+            "orcidid": "https://orcid.org/0000-0003-2721-1142",
+            "typeEntity": "Person",
+            "url": "http://www.uv.es/yoma/"
+        },
+        {
+            "name": "Ana Cristina Aguilar"
+        },
+        {
+            "name": "Longendri Aguilera-Mendoza",
+            "orcidid": "https://orcid.org/0000-0002-2421-0382"
+        },
+        {
+            "name": "Sebastián Ayala-Ruano",
+            "orcidid": "https://orcid.org/0000-0001-9756-6745"
+        }
+    ],
+    "description": "Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Peptide identification",
+                    "uri": "http://edamontology.org/operation_3631"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://mobiosd-hub.com/starpep",
+    "language": [
+        "Java"
+    ],
+    "lastUpdate": "2023-02-13T16:57:26.431595Z",
+    "name": "StarPepDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACSOMEGA.2C03398",
+            "metadata": {
+                "abstract": "© 2022 American Chemical Society. All rights reserved.Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 1065unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.",
+                "authors": [
+                    {
+                        "name": "Aguero-Chapin G."
+                    },
+                    {
+                        "name": "Aguilar A.C."
+                    },
+                    {
+                        "name": "Aguilera-Mendoza L."
+                    },
+                    {
+                        "name": "Antunes A."
+                    },
+                    {
+                        "name": "Ayala-Ruano S."
+                    },
+                    {
+                        "name": "Marrero-Ponce Y."
+                    },
+                    {
+                        "name": "Perez N."
+                    }
+                ],
+                "date": "2022-12-20T00:00:00Z",
+                "journal": "ACS Omega",
+                "title": "Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides"
+            },
+            "pmcid": "PMC9773354",
+            "pmid": "36570318"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Chemistry",
+            "uri": "http://edamontology.org/topic_3314"
+        },
+        {
+            "term": "Immunogenetics",
+            "uri": "http://edamontology.org/topic_3930"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/start-asap/start-asap.biotools.json b/data/start-asap/start-asap.biotools.json
index c40672fb1d335..b7b763bb9a0c1 100644
--- a/data/start-asap/start-asap.biotools.json
+++ b/data/start-asap/start-asap.biotools.json
@@ -1,14 +1,29 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2020-05-01T10:10:02Z",
     "biotoolsCURIE": "biotools:start-asap",
     "biotoolsID": "start-asap",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "Prepare the input directory for 'ASA3P', creating automatically a _config.xls_ file from the reads provided.\nRequires one or more reference files (.gbk recommended) and a directory with FASTQ files (.fq or .fastq, gzipped).\nMetadata can be supplied via command line or with a JSON file.",
     "editPermission": {
         "type": "private"
     },
     "homepage": "http://github.com/quadram-institute-bioscience/start-asap/",
-    "lastUpdate": "2020-05-01T10:10:11Z",
+    "language": [
+        "Perl"
+    ],
+    "lastUpdate": "2023-01-11T01:18:24.461157Z",
+    "license": "MIT",
     "name": "start-asap",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "telatin",
+    "toolType": [
+        "Command-line tool"
+    ],
     "validated": 1
 }
diff --git a/data/stata/stata.biotools.json b/data/stata/stata.biotools.json
new file mode 100644
index 0000000000000..034aadd32d1f3
--- /dev/null
+++ b/data/stata/stata.biotools.json
@@ -0,0 +1,69 @@
+{
+    "additionDate": "2023-01-27T07:25:27.037484Z",
+    "biotoolsCURIE": "biotools:stata",
+    "biotoolsID": "stata",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "email": "service@stata.com",
+            "name": "StataCorp LLC",
+            "url": "https://www.stata.com/"
+        }
+    ],
+    "description": "Fast. Accurate. Easy to use. Stata is a complete, integrated software package that provides all your data science needs—data manipulation, visualization, statistics, and automated reporting",
+    "documentation": [
+        {
+            "type": [
+                "Installation instructions"
+            ],
+            "url": "https://www.stata.com/install-guide/"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://www.stata.com/order/dl/"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        },
+        {
+            "operation": [
+                {
+                    "term": "Statistical calculation",
+                    "uri": "http://edamontology.org/operation_2238"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.stata.com/",
+    "lastUpdate": "2023-02-01T13:20:37.281322Z",
+    "license": "Proprietary",
+    "link": [
+        {
+            "note": "Technical Services\n\nhttps://www.stata.com/support/tech-support/contact/",
+            "type": [
+                "Other",
+                "Other"
+            ],
+            "url": "https://www.stata.com/"
+        }
+    ],
+    "name": "Stata",
+    "owner": "iacs-biocomputacion",
+    "version": [
+        "17"
+    ]
+}
diff --git a/data/steprna/steprna.biotools.json b/data/steprna/steprna.biotools.json
new file mode 100644
index 0000000000000..b6bdc322fd21e
--- /dev/null
+++ b/data/steprna/steprna.biotools.json
@@ -0,0 +1,94 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T16:52:11.996768Z",
+    "biotoolsCURIE": "biotools:steprna",
+    "biotoolsID": "steprna",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "v.l.hunt@bath.ac.uk",
+            "name": "Vicky L. Hunt",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ben Murcott"
+        },
+        {
+            "name": "Dominika Lastik"
+        },
+        {
+            "name": "Rebecca J Pawluk"
+        }
+    ],
+    "description": "Identification of Dicer cleavage signatures and passenger strand lengths in small RNA sequences.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                },
+                {
+                    "term": "siRNA duplex prediction",
+                    "uri": "http://edamontology.org/operation_2008"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Vicky-Hunt-Lab/stepRNA",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-13T16:52:12.009429Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://pypi.org/project/stepRNA/1.0.5/"
+        }
+    ],
+    "name": "stepRNA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FBINF.2022.994871",
+            "pmcid": "PMC9720893",
+            "pmid": "36478706"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ],
+    "version": [
+        "1.0.5"
+    ]
+}
diff --git a/data/steps_4.0/steps_4.0.biotools.json b/data/steps_4.0/steps_4.0.biotools.json
new file mode 100644
index 0000000000000..d0ffb2f49fe2d
--- /dev/null
+++ b/data/steps_4.0/steps_4.0.biotools.json
@@ -0,0 +1,132 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T21:39:25.786413Z",
+    "biotoolsCURIE": "biotools:steps_4.0",
+    "biotoolsID": "steps_4.0",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "erik@oist.jp",
+            "name": "Erik De Schutter",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Felix Schürmann"
+        },
+        {
+            "name": "Tristan Carel"
+        },
+        {
+            "name": "Weiliang Chen"
+        }
+    ],
+    "description": "Fast and memory-efficient molecular simulations of neurons at the nanoscale.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Pathway analysis",
+                    "uri": "http://edamontology.org/operation_3928"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://steps.sourceforge.net/",
+    "lastUpdate": "2023-01-25T21:39:25.789374Z",
+    "name": "STEPS 4.0",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FNINF.2022.883742",
+            "metadata": {
+                "abstract": "Copyright © 2022 Chen, Carel, Awile, Cantarutti, Castiglioni, Cattabiani, Del Marmol, Hepburn, King, Kotsalos, Kumbhar, Lallouette, Melchior, Schürmann and De Schutter.Recent advances in computational neuroscience have demonstrated the usefulness and importance of stochastic, spatial reaction-diffusion simulations. However, ever increasing model complexity renders traditional serial solvers, as well as naive parallel implementations, inadequate. This paper introduces a new generation of the STochastic Engine for Pathway Simulation (STEPS) project (http://steps.sourceforge.net/), denominated STEPS 4.0, and its core components which have been designed for improved scalability, performance, and memory efficiency. STEPS 4.0 aims to enable novel scientific studies of macroscopic systems such as whole cells while capturing their nanoscale details. This class of models is out of reach for serial solvers due to the vast quantity of computation in such detailed models, and also out of reach for naive parallel solvers due to the large memory footprint. Based on a distributed mesh solution, we introduce a new parallel stochastic reaction-diffusion solver and a deterministic membrane potential solver in STEPS 4.0. The distributed mesh, together with improved data layout and algorithm designs, significantly reduces the memory footprint of parallel simulations in STEPS 4.0. This enables massively parallel simulations on modern HPC clusters and overcomes the limitations of the previous parallel STEPS implementation. Current and future improvements to the solver are not sustainable without following proper software engineering principles. For this reason, we also give an overview of how the STEPS codebase and the development environment have been updated to follow modern software development practices. We benchmark performance improvement and memory footprint on three published models with different complexities, from a simple spatial stochastic reaction-diffusion model, to a more complex one that is coupled to a deterministic membrane potential solver to simulate the calcium burst activity of a Purkinje neuron. Simulation results of these models suggest that the new solution dramatically reduces the per-core memory consumption by more than a factor of 30, while maintaining similar or better performance and scalability.",
+                "authors": [
+                    {
+                        "name": "Awile O."
+                    },
+                    {
+                        "name": "Cantarutti N."
+                    },
+                    {
+                        "name": "Carel T."
+                    },
+                    {
+                        "name": "Castiglioni G."
+                    },
+                    {
+                        "name": "Cattabiani A."
+                    },
+                    {
+                        "name": "Chen W."
+                    },
+                    {
+                        "name": "De Schutter E."
+                    },
+                    {
+                        "name": "Del Marmol B."
+                    },
+                    {
+                        "name": "Hepburn I."
+                    },
+                    {
+                        "name": "King J.G."
+                    },
+                    {
+                        "name": "Kotsalos C."
+                    },
+                    {
+                        "name": "Kumbhar P."
+                    },
+                    {
+                        "name": "Lallouette J."
+                    },
+                    {
+                        "name": "Melchior S."
+                    },
+                    {
+                        "name": "Schurmann F."
+                    }
+                ],
+                "date": "2022-10-26T00:00:00Z",
+                "journal": "Frontiers in Neuroinformatics",
+                "title": "STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale"
+            },
+            "pmcid": "PMC9645802",
+            "pmid": "36387588"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Computer science",
+            "uri": "http://edamontology.org/topic_3316"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        }
+    ]
+}
diff --git a/data/strainselect/strainselect.biotools.json b/data/strainselect/strainselect.biotools.json
new file mode 100644
index 0000000000000..e065191e97885
--- /dev/null
+++ b/data/strainselect/strainselect.biotools.json
@@ -0,0 +1,119 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T10:21:39.616255Z",
+    "biotoolsCURIE": "biotools:strainselect",
+    "biotoolsID": "strainselect",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "todd.desantis@gmail.com",
+            "name": "Todd Z. DeSantis",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Cesar Cardona"
+        },
+        {
+            "name": "Cheryl-Emiliane Chow"
+        },
+        {
+            "name": "Shoko Iwai"
+        }
+    ],
+    "description": "A novel microbiome reference database that disambiguates all bacterial strains, genome assemblies and extant cultures worldwide.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Genome assembly",
+                    "uri": "http://edamontology.org/operation_0525"
+                },
+                {
+                    "term": "Mapping assembly",
+                    "uri": "http://edamontology.org/operation_0523"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://strainselect.secondgenome.com",
+    "lastUpdate": "2023-03-17T10:21:39.620897Z",
+    "name": "StrainSelect",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.HELIYON.2023.E13314",
+            "metadata": {
+                "abstract": "Motivation: Microbial metagenomic profiling software and databases are advancing rapidly for development of novel disease biomarkers and therapeutics yet three problems impede analyses: 1) the conflation of “genome assembly” and “strain” in reference databases; 2) difficulty connecting DNA biomarkers to a procurable strain for laboratory experimentation; and 3) absence of a comprehensive and unified strain-resolved reference database for integrating both shotgun metagenomics and 16S rRNA gene data. Results: We demarcated 681,087 strains, the largest collection of its kind, by filtering public data into a knowledge graph of vertices representing contiguous DNA sequences, genome assemblies, strain monikers and bio-resource center (BRC) catalog numbers then adding inter-vertex edges only for synonyms or direct derivatives. Surprisingly, for 10,043 important strains, we found replicate RefSeq genome assemblies obstructing interpretation of database searches. We organized each strain into eight taxonomic ranks with bootstrap confidence inversely correlated with genome assembly contamination. The StrainSelect database is suited for applications where a taxonomic, functional or procurement reference is needed for shotgun or amplicon metagenomics since 636,568 strains have at least one 16S rRNA gene, 245,005 have at least one annotated genome assembly, and 36,671 are procurable from at least one BRC. The database overcomes all three aforementioned problems since it disambiguates strains from assemblies, locates strains at BRCs, and unifies a taxonomic reference for both 16S rRNA and shotgun metagenomics. Availability: The StrainSelect database is available in igraph and tabular vertex-edge formats compatible with Neo4J. Dereplicated MinHash and fasta databases are distributed for sourmash and usearch pipelines at http://strainselect.secondgenome.com. Contact: todd.desantis@gmail.com. Supplementary information: Supplementary data are available online.",
+                "authors": [
+                    {
+                        "name": "Cardona C."
+                    },
+                    {
+                        "name": "Chow C.-E."
+                    },
+                    {
+                        "name": "DeSantis T.Z."
+                    },
+                    {
+                        "name": "Iwai S."
+                    },
+                    {
+                        "name": "Narayan N.R."
+                    },
+                    {
+                        "name": "Ravichandar D."
+                    },
+                    {
+                        "name": "Viswanatham S."
+                    },
+                    {
+                        "name": "Wee B."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "Heliyon",
+                "title": "StrainSelect: A novel microbiome reference database that disambiguates all bacterial strains, genome assemblies and extant cultures worldwide"
+            },
+            "pmcid": "PMC9939595",
+            "pmid": "36814618"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Metagenomic sequencing",
+            "uri": "http://edamontology.org/topic_3837"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Microbial ecology",
+            "uri": "http://edamontology.org/topic_3697"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/strike-goldd/strike-goldd.biotools.json b/data/strike-goldd/strike-goldd.biotools.json
new file mode 100644
index 0000000000000..a22a26367243e
--- /dev/null
+++ b/data/strike-goldd/strike-goldd.biotools.json
@@ -0,0 +1,77 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T14:19:48.017169Z",
+    "biotoolsCURIE": "biotools:strike-goldd",
+    "biotoolsID": "strike-goldd",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "afvillaverde@uvigo.gal",
+            "name": "Alejandro F Villaverde",
+            "orcidid": "https://orcid.org/0000-0001-7401-7380",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Sandra Díaz-Seoane",
+            "orcidid": "https://orcid.org/0000-0001-8642-1639"
+        },
+        {
+            "name": "Xabier Rey Barreiro",
+            "orcidid": "https://orcid.org/0000-0002-2390-4462"
+        }
+    ],
+    "description": "User-friendly, efficient analysis of structural identifiability and observability.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/afvillaverde/strike-goldd",
+    "language": [
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-02-10T14:19:48.019836Z",
+    "license": "GPL-3.0",
+    "name": "STRIKE-GOLDD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC748",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: STRIKE-GOLDD is a toolbox that analyses the structural identifiability and observability of possibly non-linear, non-rational ODE models that may have known and unknown inputs. Its broad applicability comes at the expense of a lower computational efficiency than other tools. RESULTS: STRIKE-GOLDD 4.0 includes a new algorithm, ProbObsTest, specifically designed for the analysis of rational models. ProbObsTest is significantly faster than the previously available FISPO algorithm when applied to computationally expensive models. Providing both algorithms in the same toolbox allows combining generality and computational efficiency. STRIKE-GOLDD 4.0 is implemented as a Matlab toolbox with a user-friendly graphical interface. AVAILABILITY AND IMPLEMENTATION: STRIKE-GOLDD 4.0 is a free and open-source tool available under a GPLv3 license. It can be downloaded from GitHub at https://github.com/afvillaverde/strike-goldd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Diaz-Seoane S."
+                    },
+                    {
+                        "name": "Rey Barreiro X."
+                    },
+                    {
+                        "name": "Villaverde A.F."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "STRIKE-GOLDD 4.0: user-friendly, efficient analysis of structural identifiability and observability"
+            },
+            "pmcid": "PMC9805590",
+            "pmid": "36398887"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Systems biology",
+            "uri": "http://edamontology.org/topic_2259"
+        }
+    ],
+    "version": [
+        "4.0"
+    ]
+}
diff --git a/data/strobealign/strobealign.biotools.json b/data/strobealign/strobealign.biotools.json
new file mode 100644
index 0000000000000..3fb63a33e0a08
--- /dev/null
+++ b/data/strobealign/strobealign.biotools.json
@@ -0,0 +1,99 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-13T16:46:06.053423Z",
+    "biotoolsCURIE": "biotools:strobealign",
+    "biotoolsID": "strobealign",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ksahlin@math.su.se",
+            "name": "Kristoffer Sahlin",
+            "orcidid": "https://orcid.org/0000-0001-7378-2320",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "Flexible seed size enables ultra-fast and accurate read alignment.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome indexing",
+                    "uri": "http://edamontology.org/operation_3211"
+                },
+                {
+                    "term": "Local alignment",
+                    "uri": "http://edamontology.org/operation_0495"
+                },
+                {
+                    "term": "Read mapping",
+                    "uri": "http://edamontology.org/operation_3198"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "k-mer counting",
+                    "uri": "http://edamontology.org/operation_3472"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ksahlin/strobealign",
+    "language": [
+        "C",
+        "C++"
+    ],
+    "lastUpdate": "2023-02-13T16:46:06.056127Z",
+    "license": "MIT",
+    "name": "strobealign",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13059-022-02831-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Read alignment is often the computational bottleneck in analyses. Recently, several advances have been made on seeding methods for fast sequence comparison. We combine two such methods, syncmers and strobemers, in a novel seeding approach for constructing dynamic-sized fuzzy seeds and implement the method in a short-read aligner, strobealign. The seeding is fast to construct and effectively reduces repetitiveness in the seeding step, as shown using a novel metric E-hits. strobealign is several times faster than traditional aligners at similar and sometimes higher accuracy while being both faster and more accurate than more recently proposed aligners for short reads of lengths 150nt and longer. Availability: https://github.com/ksahlin/strobealign",
+                "authors": [
+                    {
+                        "name": "Sahlin K."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Genome Biology",
+                "title": "Strobealign: flexible seed size enables ultra-fast and accurate read alignment"
+            },
+            "pmcid": "PMC9753264",
+            "pmid": "36522758"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Structure analysis",
+            "uri": "http://edamontology.org/topic_0081"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/stuart/stuart.biotools.json b/data/stuart/stuart.biotools.json
new file mode 100644
index 0000000000000..e0e523bbcf3ce
--- /dev/null
+++ b/data/stuart/stuart.biotools.json
@@ -0,0 +1,97 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T13:54:32.122487Z",
+    "biotoolsCURIE": "biotools:stuart",
+    "biotoolsID": "stuart",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xavier.montagutelli@pasteur.fr",
+            "name": "Xavier Montagutelli",
+            "orcidid": "http://orcid.org/0000-0002-9372-5398",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Marie Bourdon",
+            "orcidid": "http://orcid.org/0000-0002-6184-1888"
+        }
+    ],
+    "description": "An R package for the curation of SNP genotypes from experimental crosses.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genetic mapping",
+                    "uri": "http://edamontology.org/operation_0282"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "SNP detection",
+                    "uri": "http://edamontology.org/operation_0484"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://gitlab.pasteur.fr/mouselab/stuart/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-27T13:54:32.125105Z",
+    "license": "Not licensed",
+    "name": "stuart",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/g3journal/jkac219",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America.Genetic mapping in 2-generation crosses requires genotyping, usually performed with single nucleotide polymorphism markers arrays which provide high-density genetic information. However, genetic analysis on raw genotypes can lead to spurious or unreliable results due to defective single nucleotide polymorphism assays or wrong genotype interpretation. Here, we introduce stuart, an open-source R package, which analyzes raw genotyping data to filter single nucleotide polymorphism markers based on informativeness, Mendelian inheritance pattern, and consistency with parental genotypes. The functions of this package provide a curation pipeline and formatting adequate for genetic analysis with the R/qtl package. stuart is available with detailed documentation from https://gitlab.pasteur.fr/mouselab/stuart/.",
+                "authors": [
+                    {
+                        "name": "Bourdon M."
+                    },
+                    {
+                        "name": "Montagutelli X."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "G3: Genes, Genomes, Genetics",
+                "title": "stuart: an R package for the curation of SNP genotypes from experimental crosses"
+            },
+            "pmcid": "PMC9635635",
+            "pmid": "36000885"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        }
+    ]
+}
diff --git a/data/sub-spia/sub-spia.biotools.json b/data/sub-spia/sub-spia.biotools.json
index 9721aa3306a00..e24d5f0e09445 100644
--- a/data/sub-spia/sub-spia.biotools.json
+++ b/data/sub-spia/sub-spia.biotools.json
@@ -6,7 +6,22 @@
     "collectionID": [
         "PerMedCoE"
     ],
+    "confidence_flag": "tool",
     "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Liangzhong Shen"
+        },
+        {
+            "name": "Wenbin Liu"
+        },
+        {
+            "name": "Xianbin Li"
+        },
+        {
+            "name": "Xuequn Shang"
+        }
+    ],
     "description": "Signaling-pathway impact analysis (SPIA) is one such method and combines both the classical enrichment analysis and the actual perturbation on a given pathway. Because this method focuses on a single pathway, its resolution generally is not very high because the differentially expressed genes may be enriched in a local region of the pathway. In the present work, to identify cancer-related pathways, we incorporated a recent subpathway analysis method into the SPIA method to form the “sub-SPIA method.”",
     "download": [
         {
@@ -39,7 +54,7 @@
     "language": [
         "R"
     ],
-    "lastUpdate": "2022-12-09T23:03:51.402102Z",
+    "lastUpdate": "2023-01-11T01:16:06.402897Z",
     "license": "MIT",
     "name": "sub-SPIA",
     "operatingSystem": [
diff --git a/data/subatomic/subatomic.biotools.json b/data/subatomic/subatomic.biotools.json
new file mode 100644
index 0000000000000..9575377978fe8
--- /dev/null
+++ b/data/subatomic/subatomic.biotools.json
@@ -0,0 +1,88 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T10:27:36.928540Z",
+    "biotoolsCURIE": "biotools:subatomic",
+    "biotoolsID": "subatomic",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Jens Uwe Loers",
+            "orcidid": "http://orcid.org/0000-0002-7329-1772"
+        },
+        {
+            "name": "Vanessa Vermeirssen",
+            "orcidid": "http://orcid.org/0000-0002-1975-0712"
+        }
+    ],
+    "description": "A SUbgraph BAsed mulTi-OMIcs Clustering framework to analyze integrated multi-edge networks.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Gene-set enrichment analysis",
+                    "uri": "http://edamontology.org/operation_2436"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/CBIGR/SUBATOMIC",
+    "language": [
+        "Java",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T10:27:36.931227Z",
+    "license": "Not licensed",
+    "name": "SUBATOMIC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/s12859-022-04908-3",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Representing the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules. However, these methods are not adapted to deal with multi-omics data nor consider topological features. When deriving very large modules or ignoring the broader network context, interpretability remains limited. To address these issues, we developed a SUbgraph BAsed mulTi-OMIcs Clustering framework (SUBATOMIC), which infers small and interpretable modules with a specific topology while keeping track of connections to other modules and regulators. Results: SUBATOMIC groups specific molecular interactions in composite network subgraphs of two and three nodes and clusters them into topological modules. These are functionally annotated, visualized and overlaid with expression profiles to go from static to dynamic modules. To preserve the larger network context, SUBATOMIC investigates statistically the connections in between modules as well as between modules and regulators such as miRNAs and transcription factors. We applied SUBATOMIC to analyze a composite Homo sapiens network containing transcription factor-target gene, miRNA-target gene, protein–protein, homologous and co-functional interactions from different databases. We derived and annotated 5586 modules with diverse topological, functional and regulatory properties. We created novel functional hypotheses for unannotated genes. Furthermore, we integrated modules with condition specific expression data to study the influence of hypoxia in three cancer cell lines. We developed two prioritization strategies to identify the most relevant modules in specific biological contexts: one considering GO term enrichments and one calculating an activity score reflecting the degree of differential expression. Both strategies yielded modules specifically reacting to low oxygen levels. Conclusions: We developed the SUBATOMIC framework that generates interpretable modules from integrated multi-omics networks and applied it to hypoxia in cancer. SUBATOMIC can infer and contextualize modules, explore condition or disease specific modules, identify regulators and functionally related modules, and derive novel gene functions for uncharacterized genes. The software is available at https://github.com/CBIGR/SUBATOMIC.",
+                "authors": [
+                    {
+                        "name": "Loers J.U."
+                    },
+                    {
+                        "name": "Vermeirssen V."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks"
+            },
+            "pmcid": "PMC9442970",
+            "pmid": "36064320"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        }
+    ]
+}
diff --git a/data/sulfatlas/sulfatlas.biotools.json b/data/sulfatlas/sulfatlas.biotools.json
index 516ab7e1b326a..c26f32555b9fd 100644
--- a/data/sulfatlas/sulfatlas.biotools.json
+++ b/data/sulfatlas/sulfatlas.biotools.json
@@ -1,26 +1,126 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2019-11-05T14:13:27Z",
     "biotoolsCURIE": "biotools:SulfAtlas",
     "biotoolsID": "SulfAtlas",
-    "collectionID": [
-        "elixir-fr-sdp-2019"
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "gurvan@sb-roscoff.fr",
+            "name": "Gurvan Michel",
+            "orcidid": "https://orcid.org/0000-0002-3009-6205",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "tristan.barbeyron@sb-roscoff.fr",
+            "name": "Tristan Barbeyron",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Mark Stam"
+        },
+        {
+            "name": "Pernelle Lelièvre"
+        }
     ],
-    "description": "SulfAtlas describes the family and sub-families of structurally-related sulfatases. Sub-families are created based on phylogenetic analyses and essentially correspond to different substrate specificities.",
+    "description": "SulfAtlas describes the family and sub-families of structurally-related sulfatases. Sub-families are created based on phylogenetic analyses and essentially correspond to different substrate specificities",
     "editPermission": {
         "type": "private"
     },
-    "homepage": "http://abims.sb-roscoff.fr/sulfatlas",
-    "lastUpdate": "2020-01-24T09:54:04Z",
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://sulfatlas.sb-roscoff.fr/",
+    "lastUpdate": "2023-01-25T21:30:47.860975Z",
     "name": "SulfAtlas",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "jison",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC977",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.SulfAtlas (https://sulfatlas.sb-roscoff.fr/) is a knowledge-based resource dedicated to a sequence-based classification of sulfatases. Currently four sulfatase families exist (S1-S4) and the largest family (S1, formylglycine-dependent sulfatases) is divided into subfamilies by a phylogenetic approach, each subfamily corresponding to either a single characterized specificity (or few specificities in some cases) or to unknown substrates. Sequences are linked to their biochemical and structural information according to an expert scrutiny of the available literature. Database browsing was initially made possible both through a keyword search engine and a specific sequence similarity (BLAST) server. In this article, we will briefly summarize the experimental progresses in the sulfatase field in the last 6 years. To improve and speed up the (sub)family assignment of sulfatases in (meta)genomic data, we have developed a new, freely-accessible search engine using Hidden Markov model (HMM) for each (sub)family. This new tool (SulfAtlas HMM) is also a key part of the internal pipeline used to regularly update the database. SulfAtlas resource has indeed significantly grown since its creation in 2016, from 4550 sequences to 162 430 sequences in August 2022.",
+                "authors": [
+                    {
+                        "name": "Barbeyron T."
+                    },
+                    {
+                        "name": "Corre E."
+                    },
+                    {
+                        "name": "Hoebeke M."
+                    },
+                    {
+                        "name": "Lelievre P."
+                    },
+                    {
+                        "name": "Michel G."
+                    },
+                    {
+                        "name": "Stam M."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "SulfAtlas, the sulfatase database: state of the art and new developments"
+            },
+            "pmcid": "PMC9825549",
+            "pmid": "36318251"
+        }
+    ],
     "toolType": [
         "Database portal"
     ],
     "topic": [
         {
-            "term": "Gene and protein families",
-            "uri": "http://edamontology.org/topic_0623"
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
         }
-    ],
-    "validated": 1
+    ]
 }
diff --git a/data/supernatural_3.0/supernatural_3.0.biotools.json b/data/supernatural_3.0/supernatural_3.0.biotools.json
new file mode 100644
index 0000000000000..f37ad7c84325f
--- /dev/null
+++ b/data/supernatural_3.0/supernatural_3.0.biotools.json
@@ -0,0 +1,88 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T21:20:01.502420Z",
+    "biotoolsCURIE": "biotools:supernatural_3.0",
+    "biotoolsID": "supernatural_3.0",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "priyanka.banerjee@charite.de",
+            "name": "Priyanka Banerjee",
+            "orcidid": "https://orcid.org/0000-0001-8072-5594",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Emanuel Kemmler"
+        },
+        {
+            "name": "Kathleen Gallo"
+        },
+        {
+            "name": "Robert Preissner"
+        }
+    ],
+    "description": "Database of natural products and natural product-based derivatives.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Natural product identification",
+                    "uri": "http://edamontology.org/operation_3803"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bioinf-applied.charite.de/supernatural_3",
+    "lastUpdate": "2023-01-25T21:20:01.505400Z",
+    "name": "SuperNatural 3.0",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1008",
+            "pmcid": "PMC9825600",
+            "pmid": "36399452"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Biochemistry",
+            "uri": "http://edamontology.org/topic_3292"
+        },
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/surehyp/surehyp.biotools.json b/data/surehyp/surehyp.biotools.json
new file mode 100644
index 0000000000000..4db2f4f4b552b
--- /dev/null
+++ b/data/surehyp/surehyp.biotools.json
@@ -0,0 +1,88 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T15:46:10.725854Z",
+    "biotoolsCURIE": "biotools:surehyp",
+    "biotoolsID": "surehyp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tmiragli@mail.ubc.ca",
+            "name": "Thomas Miraglio",
+            "orcidid": "https://orcid.org/0000-0003-0428-5034",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Nicholas C Coops"
+        }
+    ],
+    "description": "An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/tmiraglio/SUREHYP",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-10T15:46:10.728415Z",
+    "license": "BSD-3-Clause",
+    "name": "SUREHYP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3390/S22239205",
+            "metadata": {
+                "abstract": "© 2022 by the authors.Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software’s over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale.",
+                "authors": [
+                    {
+                        "name": "Coops N.C."
+                    },
+                    {
+                        "name": "Miraglio T."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Sensors",
+                "title": "SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance"
+            },
+            "pmcid": "PMC9741222",
+            "pmid": "36501908"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/sv-gen/sv-gen.biotools.json b/data/sv-gen/sv-gen.biotools.json
index 5bddc99f23d63..d33e15339eb93 100644
--- a/data/sv-gen/sv-gen.biotools.json
+++ b/data/sv-gen/sv-gen.biotools.json
@@ -155,7 +155,7 @@
         "Java",
         "Python"
     ],
-    "lastUpdate": "2020-10-16T19:28:41Z",
+    "lastUpdate": "2023-02-02T10:07:20.009175Z",
     "license": "Apache-2.0",
     "link": [
         {
@@ -173,7 +173,7 @@
     "owner": "arnikz",
     "publication": [
         {
-            "doi": "10.5281/zenodo.3725664",
+            "doi": "10.5281/zenodo.3725663",
             "type": [
                 "Other"
             ]
@@ -203,6 +203,7 @@
     ],
     "validated": 1,
     "version": [
-        "1.0.0"
+        "1.0.0",
+        "1.1.0"
     ]
 }
diff --git a/data/svafotate/svafotate.biotools.json b/data/svafotate/svafotate.biotools.json
new file mode 100644
index 0000000000000..9304dbce8bc14
--- /dev/null
+++ b/data/svafotate/svafotate.biotools.json
@@ -0,0 +1,110 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T21:13:56.708086Z",
+    "biotoolsCURIE": "biotools:svafotate",
+    "biotoolsID": "svafotate",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "thomas.nicholas@utah.edu",
+            "name": "Thomas J. Nicholas",
+            "orcidid": "https://orcid.org/0000-0002-4198-6414",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Aaron R. Quinlan"
+        },
+        {
+            "name": "Michael J. Cormier"
+        }
+    ],
+    "description": "Annotation of structural variants with reported allele frequencies and related metrics from multiple datasets using SVAFotate.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "Variant prioritisation",
+                    "uri": "http://edamontology.org/operation_3226"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/fakedrtom/SVAFotate",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T21:13:56.711465Z",
+    "license": "MIT",
+    "name": "SVAFotate",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05008-Y",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Identification of deleterious genetic variants using DNA sequencing data relies on increasingly detailed filtering strategies to isolate the small subset of variants that are more likely to underlie a disease phenotype. Datasets reflecting population allele frequencies of different types of variants serve as powerful filtering tools, especially in the context of rare disease analysis. While such population-scale allele frequency datasets now exist for structural variants (SVs), it remains a challenge to match SV calls between multiple datasets, thereby complicating estimates of a putative SV's population allele frequency. Results: We introduce SVAFotate, a software tool that enables the annotation of SVs with variant allele frequency and related information from existing SV datasets. As a result, VCF files annotated by SVAFotate offer a variety of metrics to aid in the stratification of SVs as common or rare in the broader human population. Conclusions: Here we demonstrate the use of SVAFotate in the classification of SVs with regards to their population frequency and illustrate how SVAFotate's annotations can be used to filter and prioritize SVs. Lastly, we detail how best to utilize these SV annotations in the analysis of genetic variation in studies of rare disease.",
+                "authors": [
+                    {
+                        "name": "Cormier M.J."
+                    },
+                    {
+                        "name": "Nicholas T.J."
+                    },
+                    {
+                        "name": "Quinlan A.R."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "Annotation of structural variants with reported allele frequencies and related metrics from multiple datasets using SVAFotate"
+            },
+            "pmcid": "PMC9670370",
+            "pmid": "36384437"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Rare diseases",
+            "uri": "http://edamontology.org/topic_3325"
+        },
+        {
+            "term": "Structural variation",
+            "uri": "http://edamontology.org/topic_3175"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/svdss/svdss.biotools.json b/data/svdss/svdss.biotools.json
new file mode 100644
index 0000000000000..3f5b49b3285b9
--- /dev/null
+++ b/data/svdss/svdss.biotools.json
@@ -0,0 +1,134 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T15:42:01.687931Z",
+    "biotoolsCURIE": "biotools:svdss",
+    "biotoolsID": "svdss",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Luca Denti"
+        },
+        {
+            "name": "Parsoa Khorsand"
+        },
+        {
+            "name": "Fereydoun Hormozdiari",
+            "orcidid": "http://orcid.org/0000-0003-2703-9274"
+        },
+        {
+            "name": "Paola Bonizzoni",
+            "orcidid": "http://orcid.org/0000-0001-7289-4988"
+        },
+        {
+            "name": "Rayan Chikhi",
+            "orcidid": "http://orcid.org/0000-0003-1099-8735"
+        }
+    ],
+    "description": "Structural variation discovery in hard-to-call genomic regions using sample-specific strings from accurate long reads.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "De-novo assembly",
+                    "uri": "http://edamontology.org/operation_0524"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Split read mapping",
+                    "uri": "http://edamontology.org/operation_3199"
+                },
+                {
+                    "term": "Structural variation detection",
+                    "uri": "http://edamontology.org/operation_3228"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/Parsoa/SVDSS",
+    "language": [
+        "C++",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-10T15:42:01.690910Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/ldenti/SVDSS-experiments"
+        }
+    ],
+    "name": "SVDSS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41592-022-01674-1",
+            "metadata": {
+                "abstract": "© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.Structural variants (SVs) account for a large amount of sequence variability across genomes and play an important role in human genomics and precision medicine. Despite intense efforts over the years, the discovery of SVs in individuals remains challenging due to the diploid and highly repetitive structure of the human genome, and by the presence of SVs that vastly exceed sequencing read lengths. However, the recent introduction of low-error long-read sequencing technologies such as PacBio HiFi may finally enable these barriers to be overcome. Here we present SV discovery with sample-specific strings (SVDSS)—a method for discovery of SVs from long-read sequencing technologies (for example, PacBio HiFi) that combines and effectively leverages mapping-free, mapping-based and assembly-based methodologies for overall superior SV discovery performance. Our experiments on several human samples show that SVDSS outperforms state-of-the-art mapping-based methods for discovery of insertion and deletion SVs in PacBio HiFi reads and achieves notable improvements in calling SVs in repetitive regions of the genome.",
+                "authors": [
+                    {
+                        "name": "Bonizzoni P."
+                    },
+                    {
+                        "name": "Chikhi R."
+                    },
+                    {
+                        "name": "Denti L."
+                    },
+                    {
+                        "name": "Hormozdiari F."
+                    },
+                    {
+                        "name": "Khorsand P."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Nature Methods",
+                "title": "SVDSS: structural variation discovery in hard-to-call genomic regions using sample-specific strings from accurate long reads"
+            },
+            "pmid": "36550274"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Informatics",
+            "uri": "http://edamontology.org/topic_0605"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        },
+        {
+            "term": "Structural variation",
+            "uri": "http://edamontology.org/topic_3175"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/syblars/syblars.biotools.json b/data/syblars/syblars.biotools.json
new file mode 100644
index 0000000000000..b7343cec2a69c
--- /dev/null
+++ b/data/syblars/syblars.biotools.json
@@ -0,0 +1,118 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T21:09:35.972808Z",
+    "biotoolsCURIE": "biotools:syblars",
+    "biotoolsID": "syblars",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ivisatbilkent@gmail.com",
+            "name": "Ugur Dogrusoz",
+            "orcidid": "https://orcid.org/0000-0002-7153-0784",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Perman Atayev"
+        },
+        {
+            "name": "Yusuf Ziya Ozgul"
+        },
+        {
+            "name": "Hasan Balci",
+            "orcidid": "https://orcid.org/0000-0001-8319-7758"
+        }
+    ],
+    "description": "A web service for laying out, rendering and mining biological maps in SBGN, SBML and more.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
+                },
+                {
+                    "term": "Pathway analysis",
+                    "uri": "http://edamontology.org/operation_3928"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://syblars.cs.bilkent.edu.tr",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-01-25T21:09:35.976404Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/iVis-at-Bilkent/syblars"
+        }
+    ],
+    "name": "SyBLaRS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PCBI.1010635",
+            "metadata": {
+                "abstract": "© 2022 Balci et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Visualization is a key recurring requirement for effective analysis of relational data. Biology is no exception. It is imperative to annotate and render biological models in standard, widely accepted formats. Finding graph-theoretical properties of pathways as well as identifying certain paths or subgraphs of interest in a pathway are also essential for effective analysis of pathway data. Given the size of available biological pathway data nowadays, automatic layout is crucial in understanding the graphical representations of such data. Even though there are many available software tools that support graphical display of biological pathways in various formats, there is none available as a service for on-demand or batch processing of biological pathways for automatic layout, customized rendering and mining paths or subgraphs of interest. In addition, there are many tools with fine rendering capabilities lacking decent automatic layout support. To fill this void, we developed a web service named SyBLaRS (Systems Biology Layout and Rendering Service) for automatic layout of biological data in various standard formats as well as construction of customized images in both raster image and scalable vector formats of these maps. Some of the supported standards are more generic such as GraphML and JSON, whereas others are specialized to biology such as SBGNML (The Systems Biology Graphical Notation Markup Language) and SBML (The Systems Biology Markup Language). In addition, SyBLaRS supports calculation and highlighting of a number of wellknown graph-theoretical properties as well as some novel graph algorithms turning a specified set of objects of interest to a minimal pathway of interest. We demonstrate that SyBLaRS can be used both as an offline layout and rendering service to construct customized and annotated pictures of pathway models and as an online service to provide layout and rendering capabilities for systems biology software tools. SyBLaRS is open source and publicly available on GitHub and freely distributed under the MIT license. In addition, a sample deployment is available here for public consumption.",
+                "authors": [
+                    {
+                        "name": "Atayev P."
+                    },
+                    {
+                        "name": "Balci H."
+                    },
+                    {
+                        "name": "Dogrusoz U."
+                    },
+                    {
+                        "name": "Ozgul Y.Z."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "SyBLaRS: A web service for laying out, rendering and mining biological maps in SBGN, SBML and more"
+            },
+            "pmcid": "PMC9704760",
+            "pmid": "36374853"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Molecular biology",
+            "uri": "http://edamontology.org/topic_3047"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Systems biology",
+            "uri": "http://edamontology.org/topic_2259"
+        }
+    ]
+}
diff --git a/data/syllable-pbwt/syllable-pbwt.biotools.json b/data/syllable-pbwt/syllable-pbwt.biotools.json
new file mode 100644
index 0000000000000..53745b7258114
--- /dev/null
+++ b/data/syllable-pbwt/syllable-pbwt.biotools.json
@@ -0,0 +1,113 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T21:02:26.003314Z",
+    "biotoolsCURIE": "biotools:syllable-pbwt",
+    "biotoolsID": "syllable-pbwt",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "degui.zhi@uth.tmc.edu",
+            "name": "Degui Zhi",
+            "orcidid": "https://orcid.org/0000-0001-7754-1890",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "shzhang@cs.ucf.edu",
+            "name": "Shaojie Zhang",
+            "orcidid": "https://orcid.org/0000-0002-4051-5549",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Ardalan Naseri"
+        },
+        {
+            "name": "Victor Wang"
+        }
+    ],
+    "description": "Syllable-PBWT for space-efficient haplotype long-match query.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome indexing",
+                    "uri": "http://edamontology.org/operation_3211"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ZhiGroup/Syllable-PBWT",
+    "language": [
+        "C++"
+    ],
+    "lastUpdate": "2023-01-25T21:02:26.006435Z",
+    "license": "Not licensed",
+    "name": "Syllable-PBWT",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC734",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The positional Burrows-Wheeler transform (PBWT) has led to tremendous strides in haplotype matching on biobank-scale data. For genetic genealogical search, PBWT-based methods have optimized the asymptotic runtime of finding long matches between a query haplotype and a predefined panel of haplotypes. However, to enable fast query searches, the full-sized panel and PBWT data structures must be kept in memory, preventing existing algorithms from scaling up to modern biobank panels consisting of millions of haplotypes. In this work, we propose a space-efficient variation of PBWT named Syllable-PBWT, which divides every haplotype into syllables, builds the PBWT positional prefix arrays on the compressed syllabic panel, and leverages the polynomial rolling hash function for positional substring comparison. With the Syllable-PBWT data structures, we then present a long match query algorithm named Syllable-Query. RESULTS: Compared to the most time- and space-efficient previously published solution to the long match query problem, Syllable-Query reduced the memory use by a factor of over 100 on both the UK Biobank genotype data and the 1000 Genomes Project sequence data. Surprisingly, the smaller size of our syllabic data structures allows for more efficient iteration and CPU cache usage, granting Syllable-Query even faster runtime than existing solutions. AVAILABILITY AND IMPLEMENTATION: https://github.com/ZhiGroup/Syllable-PBWT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Naseri A."
+                    },
+                    {
+                        "name": "Wang V."
+                    },
+                    {
+                        "name": "Zhang S."
+                    },
+                    {
+                        "name": "Zhi D."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Syllable-PBWT for space-efficient haplotype long-match query"
+            },
+            "pmcid": "PMC9805553",
+            "pmid": "36440908"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biobank",
+            "uri": "http://edamontology.org/topic_3337"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/synergyfinder/synergyfinder.biotools.json b/data/synergyfinder/synergyfinder.biotools.json
index e82179ece45ec..56448fccce454 100644
--- a/data/synergyfinder/synergyfinder.biotools.json
+++ b/data/synergyfinder/synergyfinder.biotools.json
@@ -18,7 +18,7 @@
             "url": "https://scholar.google.com/citations?user=6sNdZq8AAAAJ&hl=en"
         }
     ],
-    "description": "Efficient implementations for all the popular synergy scoring models for drug combinations, including HSA, Loewe, Bliss and ZIP and visualization of the synergy scores as either a two-dimensional or a three-dimensional interaction surface over the dose matrix. R package available at https://bioconductor.org/packages/release/bioc/html/synergyfinder.html. The web server is available at http://www.synergyfinder.org or http://www.synergyfinderplus.org",
+    "description": "Efficient implementations for all the popular synergy scoring models for drug combinations, including HSA, Loewe, Bliss and ZIP and visualization of the synergy scores as either a two-dimensional or a three-dimensional interaction surface over the dose matrix. R package available at https://bioconductor.org/packages/release/bioc/html/synergyfinder.html. The web server is available at https://www.synergyfinder.org or https://www.synergyfinderplus.org",
     "documentation": [
         {
             "type": [
@@ -49,11 +49,11 @@
             ]
         }
     ],
-    "homepage": "http://www.synergyfinder.org",
+    "homepage": "https://www.synergyfinder.org",
     "language": [
         "R"
     ],
-    "lastUpdate": "2021-06-09T19:36:44Z",
+    "lastUpdate": "2023-02-07T09:45:05.659234Z",
     "license": "Artistic-2.0",
     "link": [
         {
@@ -112,7 +112,7 @@
                         "name": "Wennerberg K."
                     }
                 ],
-                "citationCount": 44,
+                "citationCount": 87,
                 "date": "2018-01-01T00:00:00Z",
                 "journal": "Methods in Molecular Biology",
                 "title": "Methods for high-throughput drug combination screening and synergy scoring"
@@ -136,7 +136,7 @@
                         "name": "Tang J."
                     }
                 ],
-                "citationCount": 154,
+                "citationCount": 267,
                 "date": "2017-08-01T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "SynergyFinder: A web application for analyzing drug combination dose-response matrix data"
diff --git a/data/synggen/synggen.biotools.json b/data/synggen/synggen.biotools.json
new file mode 100644
index 0000000000000..27900653c22c9
--- /dev/null
+++ b/data/synggen/synggen.biotools.json
@@ -0,0 +1,101 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T15:36:46.185888Z",
+    "biotoolsCURIE": "biotools:synggen",
+    "biotoolsID": "synggen",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "alessandro.romanel@unitn.it",
+            "name": "Alessandro Romanel",
+            "orcidid": "https://orcid.org/0000-0003-4855-8620",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Federico Calabrese"
+        },
+        {
+            "name": "Riccardo Scandino",
+            "orcidid": "https://orcid.org/0000-0002-1640-5307"
+        }
+    ],
+    "description": "Fast and data-driven generation of synthetic heterogeneous NGS cancer data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phasing",
+                    "uri": "http://edamontology.org/operation_3454"
+                },
+                {
+                    "term": "Read mapping",
+                    "uri": "http://edamontology.org/operation_3198"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://bitbucket.org/CibioBCG/synggen/",
+    "language": [
+        "C"
+    ],
+    "lastUpdate": "2023-02-10T15:36:46.188534Z",
+    "license": "MIT",
+    "name": "synggen",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC792",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.SUMMARY: Whole-exome and targeted sequencing are widely utilized both in translational cancer genomics and in the setting of precision medicine. The benchmarking of computational methods and tools that are in continuous development is fundamental for the correct interpretation of somatic genomic profiling results. To this aim we developed synggen, a tool for the fast generation of large-scale realistic and heterogeneous cancer whole-exome and targeted sequencing synthetic datasets, which enables the incorporation of phased germline single nucleotide polymorphisms and complex allele-specific somatic genomic events. Synggen performances and effectiveness in generating synthetic cancer data are shown across different scenarios and considering different platforms with distinct characteristics. AVAILABILITY AND IMPLEMENTATION: synggen is freely available at https://bitbucket.org/CibioBCG/synggen/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Calabrese F."
+                    },
+                    {
+                        "name": "Romanel A."
+                    },
+                    {
+                        "name": "Scandino R."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data"
+            },
+            "pmcid": "PMC9825741",
+            "pmid": "36484701"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        }
+    ]
+}
diff --git a/data/synr/synr.biotools.json b/data/synr/synr.biotools.json
new file mode 100644
index 0000000000000..52335f4d173e0
--- /dev/null
+++ b/data/synr/synr.biotools.json
@@ -0,0 +1,123 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T20:57:48.186008Z",
+    "biotoolsCURIE": "biotools:synr",
+    "biotoolsID": "synr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "lowe.wilsson@tobii.com",
+            "name": "Lowe Wilsson",
+            "orcidid": "http://orcid.org/0000-0003-1283-8399",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Janina Neufeld",
+            "orcidid": "https://orcid.org/0000-0002-7743-526X"
+        },
+        {
+            "name": "Tessa M. van Leeuwen",
+            "orcidid": "https://orcid.org/0000-0001-7810-6348"
+        }
+    ],
+    "description": "Explore synesthesia consistency test data, calculate consistency scores, and classify participant data as valid or invalid.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://datalowe.github.io/synr/"
+        },
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://cran.r-project.org/web/packages/synr/synr.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Anonymisation",
+                    "uri": "http://edamontology.org/operation_3283"
+                },
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/datalowe/synr",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-25T20:57:48.189517Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://cran.r-project.org/web/packages/synr/index.html"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/datalowe/synr-article-material"
+        }
+    ],
+    "name": "synr",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3758/S13428-022-02007-Y",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Synesthesia is a phenomenon where sensory stimuli or cognitive concepts elicit additional perceptual experiences. For instance, in a commonly studied type of synesthesia, stimuli such as words written in black font elicit experiences of other colors, e.g., red. In order to objectively verify synesthesia, participants are asked to choose colors for repeatedly presented stimuli and the consistency of their choices is evaluated (consistency test). Previously, there has been no publicly available and easy-to-use tool for analyzing consistency test results. Here, the R package synr is introduced, which provides an efficient interface for exploring consistency test data and applying common procedures for analyzing them. Importantly, synr also implements a novel method enabling identification of participants whose scores cannot be interpreted, e.g., who only give black or red color responses. To this end, density-based spatial clustering of applications with noise (DBSCAN) is applied in conjunction with a measure of spread in 3D space. An application of synr with pre-existing openly accessible data illustrating how synr is used in practice is presented. Also included is a comparison of synr’s data validation procedure and human ratings, which found that synr had high correspondence with human ratings and outperformed human raters in situations where human raters were easily mislead. Challenges for widespread adoption of synr as well as suggestions for using synr within the field of synesthesia and other areas of psychological research are discussed.",
+                "authors": [
+                    {
+                        "name": "Neufeld J."
+                    },
+                    {
+                        "name": "Wilsson L."
+                    },
+                    {
+                        "name": "van Leeuwen T.M."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Behavior Research Methods",
+                "title": "synr: An R package for handling synesthesia consistency test data"
+            },
+            "pmid": "36357762"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        }
+    ]
+}
diff --git a/data/sysbiolpgwas/sysbiolpgwas.biotools.json b/data/sysbiolpgwas/sysbiolpgwas.biotools.json
new file mode 100644
index 0000000000000..0f2f035eee2da
--- /dev/null
+++ b/data/sysbiolpgwas/sysbiolpgwas.biotools.json
@@ -0,0 +1,169 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T15:30:18.388541Z",
+    "biotoolsCURIE": "biotools:sysbiolpgwas",
+    "biotoolsID": "sysbiolpgwas",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ezekiel.adebiyi@covenantuniversity.edu.ng",
+            "name": "Ezekiel Adebiyi",
+            "orcidid": "https://orcid.org/0000-0002-1390-2359",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jelili Oyelade",
+            "orcidid": "https://orcid.org/0000-0002-5476-4992"
+        },
+        {
+            "name": "Oluwadamilare Falola",
+            "orcidid": "https://orcid.org/0000-0003-4448-7829"
+        },
+        {
+            "name": "Yagoub Adam",
+            "orcidid": "https://orcid.org/0000-0001-6874-2543"
+        }
+    ],
+    "description": "Simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "SNP annotation",
+                    "uri": "http://edamontology.org/operation_3661"
+                },
+                {
+                    "term": "SNP detection",
+                    "uri": "http://edamontology.org/operation_0484"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://spgwas.waslitbre.org",
+    "language": [
+        "Assembly language",
+        "C",
+        "Fortran"
+    ],
+    "lastUpdate": "2023-02-10T15:30:18.391146Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/covenant-university-bioinformatics/spgwas-custom"
+        }
+    ],
+    "name": "SysBiolPGWAS",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC791",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Post-genome-wide association studies (pGWAS) analysis is designed to decipher the functional consequences of significant single-nucleotide polymorphisms (SNPs) in the era of GWAS. This can be translated into research insights and clinical benefits such as the effectiveness of strategies for disease screening, treatment and prevention. However, the setup of pGWAS (pGWAS) tools can be quite complicated, and it mostly requires big data. The challenge however is, scientists are required to have sufficient experience with several of these technically complex and complicated tools in order to complete the pGWAS analysis. RESULTS: We present SysBiolPGWAS, a pGWAS web application that provides a comprehensive functionality for biologists and non-bioinformaticians to conduct several pGWAS analyses to overcome the above challenges. It provides unique functionalities for analysis involving multi-omics datasets and visualization using various bioinformatics tools. SysBiolPGWAS provides access to individual pGWAS tools and a novel custom pGWAS pipeline that integrates several individual pGWAS tools and data. The SysBiolPGWAS app was developed to be a one-stop shop for pGWAS analysis. It targets researchers in the area of the human genome and performs its analysis mainly in the autosomal chromosomes. AVAILABILITY AND IMPLEMENTATION: SysBiolPGWAS web app was developed using JavaScript/TypeScript web frameworks and is available at: https://spgwas.waslitbre.org/. All codes are available in this GitHub repository https://github.com/covenant-university-bioinformatics.",
+                "authors": [
+                    {
+                        "name": "Adam Y."
+                    },
+                    {
+                        "name": "Adebayo G."
+                    },
+                    {
+                        "name": "Adebiyi E."
+                    },
+                    {
+                        "name": "Adebiyi M."
+                    },
+                    {
+                        "name": "Adewale S."
+                    },
+                    {
+                        "name": "Ajayi M."
+                    },
+                    {
+                        "name": "Ajayi O."
+                    },
+                    {
+                        "name": "Akanle B."
+                    },
+                    {
+                        "name": "Emmanuel J."
+                    },
+                    {
+                        "name": "Erondu U."
+                    },
+                    {
+                        "name": "Falola O."
+                    },
+                    {
+                        "name": "Isewon I."
+                    },
+                    {
+                        "name": "Kumuthini J."
+                    },
+                    {
+                        "name": "Mosaku A."
+                    },
+                    {
+                        "name": "Nehemiah A."
+                    },
+                    {
+                        "name": "Oladipo O."
+                    },
+                    {
+                        "name": "Oyelade J."
+                    },
+                    {
+                        "name": "Rasaq S."
+                    },
+                    {
+                        "name": "Samtal C."
+                    },
+                    {
+                        "name": "Tchamga M.S.S."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "SysBiolPGWAS: simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets"
+            },
+            "pmcid": "PMC9825739",
+            "pmid": "36477976"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/tabix/tabix.biotools.json b/data/tabix/tabix.biotools.json
index 78e5f94e30c30..caf362cf744b7 100644
--- a/data/tabix/tabix.biotools.json
+++ b/data/tabix/tabix.biotools.json
@@ -1,7 +1,15 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2021-04-22T01:06:12Z",
     "biotoolsCURIE": "biotools:tabix",
     "biotoolsID": "tabix",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Heng Li"
+        }
+    ],
     "description": "Tabix indexes a TAB-delimited genome position file in.tab.bgz and creates an index file (in.tab.bgz.tbi or in.tab.bgz.csi) when region is absent from the command-line. The input data file must be position sorted and compressed by bgzip which has a gzip(1) like interface.\n\nAfter indexing, tabix is able to quickly retrieve data lines overlapping regions specified in the format \"chr:beginPos-endPos\". (Coordinates specified in this region format are 1-based and inclusive.)\n\nFast data retrieval also works over network if URI is given as a file name and in this case the index file will be downloaded if it is not present locally.\n\nThe tabix (.tbi) and BAI index formats can handle individual chromosomes up to 512 Mbp (2^29 bases) in length. If your input file might contain data lines with begin or end positions greater than that, you will need to use a CSI index.",
     "editPermission": {
         "authors": [
@@ -54,8 +62,13 @@
         }
     ],
     "homepage": "http://www.htslib.org/doc/tabix.html",
-    "lastUpdate": "2022-12-09T23:06:48.591627Z",
+    "lastUpdate": "2023-01-11T01:11:56.773187Z",
     "name": "tabix",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "leipzig",
     "toolType": [
         "Command-line tool"
diff --git a/data/tableau/tableau.biotools.json b/data/tableau/tableau.biotools.json
new file mode 100644
index 0000000000000..42c0d5394cfb5
--- /dev/null
+++ b/data/tableau/tableau.biotools.json
@@ -0,0 +1,64 @@
+{
+    "additionDate": "2023-01-27T13:02:30.702295Z",
+    "biotoolsCURIE": "biotools:tableau",
+    "biotoolsID": "tableau",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "Christian Chabot Chris Stolte Pat Hanrahan",
+            "url": "https://www.tableau.com/support/help"
+        }
+    ],
+    "description": "Tableau is a visual analytics platform transforming the way we use data to solve problems—empowering people and organizations to make the most of their data. \n\n\n\nAs the market-leading choice for modern business intelligence, our analytics platform makes it easier for people to explore and manage data, and faster to discover and share insights that can change businesses and the world.\n\nEverything we do is driven by our mission to help people see and understand data, which is why our products are designed to put the user first—whether they’re an analyst, data scientist, student, teacher, executive, or business user. From connection through collaboration, Tableau is the most powerful, secure, and flexible end-to-end analytics platform.",
+    "documentation": [
+        {
+            "type": [
+                "API documentation"
+            ],
+            "url": "https://www.tableau.com/developer/tools"
+        }
+    ],
+    "download": [
+        {
+            "note": "Homepage to Tableau",
+            "type": "Other",
+            "url": "https://www.tableau.com/products/trial"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.tableau.com/",
+    "lastUpdate": "2023-02-01T12:43:41.401030Z",
+    "license": "Proprietary",
+    "link": [
+        {
+            "note": "Homepage to Tableau",
+            "type": [
+                "Other"
+            ],
+            "url": "https://www.tableau.com/"
+        }
+    ],
+    "name": "Tableau",
+    "owner": "iacs-biocomputacion",
+    "version": [
+        "2022.4"
+    ]
+}
diff --git a/data/tadmaster/tadmaster.biotools.json b/data/tadmaster/tadmaster.biotools.json
new file mode 100644
index 0000000000000..6f4cdc5f44e70
--- /dev/null
+++ b/data/tadmaster/tadmaster.biotools.json
@@ -0,0 +1,117 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T14:17:21.780949Z",
+    "biotoolsCURIE": "biotools:tadmaster",
+    "biotoolsID": "tadmaster",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "ooluwada@uccs.edu",
+            "name": "Oluwatosin Oluwadare",
+            "orcidid": "https://orcid.org/0000-0002-5264-2342",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Allen Westcott"
+        },
+        {
+            "name": "Sean Higgins"
+        },
+        {
+            "name": "Victor Akpokiro"
+        }
+    ],
+    "description": "A comprehensive web-based tool for the analysis of topologically associated domains.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://tadmaster.io",
+    "language": [
+        "C++",
+        "MATLAB",
+        "Python",
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-25T14:17:21.784047Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/OluwadareLab/TADMaster"
+        }
+    ],
+    "name": "TADMaster",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05020-2",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Chromosome conformation capture and its derivatives have provided substantial genetic data for understanding how chromatin self-organizes. These techniques have identified regions of high intrasequence interactions called topologically associated domains (TADs). TADs are structural and functional units that shape chromosomes and influence genomic expression. Many of these domains differ across cell development and can be impacted by diseases. Thus, analysis of the identified domains can provide insight into genome regulation. Hence, there are many approaches to identifying such domains across many cell lines. Despite the availability of multiple tools for TAD detection, TAD callers' speed, flexibility, result inconsistency, and reproducibility remain challenges in this research area. Results: In this work, we developed a computational webserver called TADMaster that provides an analysis suite to directly evaluate the concordance level and robustness of two or more TAD data on any given genome region. The suite provides multiple visual and quantitative metrics to compare the identified domains' number, size, and various comparisons of shared domains, domain boundaries, and domain overlap. Conclusions: TADMaster is an efficient and easy-to-use web application that provides a set of consensus and unique TADs to inform the choice of TADs. It can be accessed at http://tadmaster.io and is also available as a containerized application that can be deployed and run locally on any platform or operating system.",
+                "authors": [
+                    {
+                        "name": "Akpokiro V."
+                    },
+                    {
+                        "name": "Higgins S."
+                    },
+                    {
+                        "name": "Oluwadare O."
+                    },
+                    {
+                        "name": "Westcott A."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "TADMaster: a comprehensive web-based tool for the analysis of topologically associated domains"
+            },
+            "pmcid": "PMC9636664",
+            "pmid": "36333787"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Chromosome conformation capture",
+            "uri": "http://edamontology.org/topic_3940"
+        },
+        {
+            "term": "DNA",
+            "uri": "http://edamontology.org/topic_0654"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        }
+    ]
+}
diff --git a/data/taguchiarray/taguchiarray.biotools.json b/data/taguchiarray/taguchiarray.biotools.json
deleted file mode 100644
index 8972d22a05c96..0000000000000
--- a/data/taguchiarray/taguchiarray.biotools.json
+++ /dev/null
@@ -1,49 +0,0 @@
-{
-    "accessibility": "Open access (with restrictions)",
-    "additionDate": "2021-05-05T11:11:32Z",
-    "biotoolsCURIE": "biotools:taguchiarray",
-    "biotoolsID": "taguchiarray",
-    "collectionID": [
-        "File Exchange",
-        "MATLAB"
-    ],
-    "cost": "Free of charge (with restrictions)",
-    "credit": [
-        {
-            "name": "Chixin Xiao",
-            "typeEntity": "Person",
-            "typeRole": [
-                "Primary contact"
-            ],
-            "url": "https://www.mathworks.com/matlabcentral/profile/6298843-chixin-xiao"
-        }
-    ],
-    "description": "This algorithm provides the Orthogonal(Taguchi) Array with inputs:  Q (the number of the levels) and N (the number of the factors).",
-    "download": [
-        {
-            "type": "Screenshot",
-            "url": "https://www.mathworks.com//matlabcentral/images/default_screenshot.jpg"
-        }
-    ],
-    "editPermission": {
-        "type": "private"
-    },
-    "homepage": "https://www.mathworks.com/matlabcentral/fileexchange/71628-taguchiarray",
-    "language": [
-        "MATLAB"
-    ],
-    "lastUpdate": "2021-05-22T13:07:42Z",
-    "name": "TaguchiArray",
-    "operatingSystem": [
-        "Linux",
-        "Mac",
-        "Windows"
-    ],
-    "owner": "zsmag19",
-    "toolType": [
-        "Library"
-    ],
-    "version": [
-        "2.0.0"
-    ]
-}
diff --git a/data/talend_open_studio/talend_open_studio.biotools.json b/data/talend_open_studio/talend_open_studio.biotools.json
new file mode 100644
index 0000000000000..dc48b854203f1
--- /dev/null
+++ b/data/talend_open_studio/talend_open_studio.biotools.json
@@ -0,0 +1,59 @@
+{
+    "additionDate": "2023-01-26T09:59:34.956729Z",
+    "biotoolsCURIE": "biotools:talend_open_studio",
+    "biotoolsID": "talend_open_studio",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "Ehtisham Zaidi, Sharat Menon, Robert Thanaraj, Nina Showell,",
+            "url": "https://www.talend.com/lp/gartner-magic-quadrant-data-integration/"
+        }
+    ],
+    "description": "Talend Open Studio for Data Integration is free-to-download software to kickstart your first data integration and ETL projects.\n\nFeatures:\n\n    Free open source Apache license\n    RDBMS connectors: Oracle, Teradata, Microsoft SQL server\n    SaaS connectors: Marketo, Salesforce, NetSuite\n    Packaged apps: SAP, Microsoft Dynamics, Sugar CRM",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://res.cloudinary.com/talend/image/upload/v1625103406/docs/talend-support-portal_u5oxsc.pdf"
+        }
+    ],
+    "download": [
+        {
+            "type": "API specification",
+            "url": "https://api.talend.com/apis/"
+        },
+        {
+            "type": "Downloads page",
+            "url": "https://www.talend.com/lp/open-studio-for-data-integration/"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.talend.com/products/talend-open-studio/",
+    "lastUpdate": "2023-02-01T12:20:53.778296Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Software catalogue"
+            ],
+            "url": "https://www.talend.com/products/talend-open-studio/"
+        }
+    ],
+    "name": "Talend Open Studio",
+    "owner": "iacs-biocomputacion"
+}
diff --git a/data/tampa/tampa.biotools.json b/data/tampa/tampa.biotools.json
new file mode 100644
index 0000000000000..9af30fd903602
--- /dev/null
+++ b/data/tampa/tampa.biotools.json
@@ -0,0 +1,13 @@
+{
+    "additionDate": "2023-01-07T02:04:10.610574Z",
+    "biotoolsCURIE": "biotools:tampa",
+    "biotoolsID": "tampa",
+    "description": "TAMPA (Taxonomic metagenome profiling evaluation) , is a robust and easy-to-use method that allows scientists to easily interpret and interact with taxonomic profiles produced by the many different taxonomic profiler methods beyond the standard metrics used by the scientific community.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/dkoslicki/TAMPA",
+    "lastUpdate": "2023-01-07T02:04:22.881318Z",
+    "name": "TAMPA",
+    "owner": "vsarwal"
+}
diff --git a/data/tarean/tarean.biotools.json b/data/tarean/tarean.biotools.json
index 6e7f6d585b613..f938feeceb5aa 100644
--- a/data/tarean/tarean.biotools.json
+++ b/data/tarean/tarean.biotools.json
@@ -70,7 +70,7 @@
         }
     ],
     "homepage": "http://repeatexplorer.org/?page_id=826",
-    "lastUpdate": "2022-12-09T23:09:39.192373Z",
+    "lastUpdate": "2023-01-11T01:03:30.556619Z",
     "license": "GPL-3.0",
     "link": [
         {
@@ -88,6 +88,11 @@
     ],
     "maturity": "Mature",
     "name": "TAREAN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "kavonrtep",
     "publication": [
         {
@@ -114,7 +119,7 @@
                         "name": "Vrbova I."
                     }
                 ],
-                "citationCount": 125,
+                "citationCount": 127,
                 "date": "2017-07-01T00:00:00Z",
                 "journal": "Nucleic Acids Research",
                 "title": "TAREAN: A computational tool for identification and characterization of satellite DNA from unassembled short reads"
@@ -123,6 +128,9 @@
             "pmid": "28402514"
         }
     ],
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "Sequence composition, complexity and repeats",
diff --git a/data/tatdb/tatdb.biotools.json b/data/tatdb/tatdb.biotools.json
new file mode 100644
index 0000000000000..bef4520dd24e5
--- /dev/null
+++ b/data/tatdb/tatdb.biotools.json
@@ -0,0 +1,96 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T14:14:00.128258Z",
+    "biotoolsCURIE": "biotools:tatdb",
+    "biotoolsID": "tatdb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "agrigoriev@camden.rutgers.edu",
+            "name": "Andrey Grigoriev",
+            "orcidid": "https://orcid.org/0000-0002-3227-532X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Lingyu Guan"
+        }
+    ],
+    "description": "A database of Ago1-mediated targets of transfer RNA fragments.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "miRNA target prediction",
+                    "uri": "http://edamontology.org/operation_0463"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://grigoriev-lab.camden.rutgers.edu/tatdb",
+    "lastUpdate": "2023-01-25T14:14:00.130793Z",
+    "name": "tatDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1018",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.tRNA-derived fragments (tRFs) are a class of emerging post-transcriptional regulators of gene expression likely binding to the transcripts of target genes. However, only a few tRFs targets have been experimentally validated, making it hard to extrapolate the functions or binding mechanisms of tRFs. The paucity of resources supporting the identification of the targets of tRFs creates a bottleneck in the fast-developing field. We have previously analyzed chimeric reads in crosslinked Argonaute1-RNA complexes to help infer the guide-target pairs and binding mechanisms of multiple tRFs based on experimental data in human HEK293 cells. To efficiently disseminate these results to the research community, we designed a web-based database tatDB (targets of tRFs DataBase) populated with close to 250 000 experimentally determined guide-target pairs with ∼23 000 tRF isoforms. tatDB has a user-friendly interface with flexible query options/filters allowing one to obtain comprehensive information on given tRFs (or targets). Modes of interactions are supported by secondary structures of potential guide-target hybrids and binding motifs, essential for understanding the targeting mechanisms of tRFs. Further, we illustrate the value of the database on an example of hypothesis-building for a tRFs potentially involved in the lifecycle of the SARS-CoV-2 virus. tatDB is freely accessible at https://grigoriev-lab.camden.rutgers.edu/tatdb.",
+                "authors": [
+                    {
+                        "name": "Grigoriev A."
+                    },
+                    {
+                        "name": "Guan L."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "tatDB: a database of Ago1-mediated targets of transfer RNA fragments"
+            },
+            "pmcid": "PMC9825446",
+            "pmid": "36350638"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/taxonium/taxonium.biotools.json b/data/taxonium/taxonium.biotools.json
new file mode 100644
index 0000000000000..043365400cd2f
--- /dev/null
+++ b/data/taxonium/taxonium.biotools.json
@@ -0,0 +1,109 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T10:19:15.561138Z",
+    "biotoolsCURIE": "biotools:taxonium",
+    "biotoolsID": "taxonium",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Theo Sanderson",
+            "orcidid": "http://orcid.org/0000-0003-4177-2851"
+        }
+    ],
+    "description": "A web-based tool for exploring large phylogenetic trees.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://docs.taxonium.org/en/latest/?_gl=1*nkaayq*_ga*MTkzNTg1NDA0Ni4xNjc3NDA2MDk5*_ga_TDTSH11X4S*MTY3NzQwNjA5OC4xLjAuMTY3NzQwNjA5OC4wLjAuMA.."
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phylogenetic inference",
+                    "uri": "http://edamontology.org/operation_0323"
+                },
+                {
+                    "term": "Phylogenetic tree comparison",
+                    "uri": "http://edamontology.org/operation_0325"
+                },
+                {
+                    "term": "Phylogenetic tree editing",
+                    "uri": "http://edamontology.org/operation_0326"
+                },
+                {
+                    "term": "Phylogenetic tree topology analysis",
+                    "uri": "http://edamontology.org/operation_0551"
+                },
+                {
+                    "term": "Phylogenetic tree visualisation",
+                    "uri": "http://edamontology.org/operation_0567"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://taxonium.org",
+    "language": [
+        "JavaScript",
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T10:19:15.563897Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "http://github.com/theosanderson/taxonium"
+        }
+    ],
+    "name": "Taxonium",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.7554/eLife.82392",
+            "metadata": {
+                "abstract": "© Sanderson.The COVID-19 pandemic has resulted in a step change in the scale of sequencing data, with more genomes of SARS-CoV-2 having been sequenced than any other organism on earth. These sequences reveal key insights when represented as a phylogenetic tree, which captures the evolutionary history of the virus, and allows the identification of transmission events and the emergence of new variants. However, existing web-based tools for exploring phylogenies do not scale to the size of datasets now available for SARS-CoV-2. We have developed Taxonium, a new tool that uses WebGL to allow the exploration of trees with tens of millions of nodes in the browser for the first time. Taxonium links each node to associated metadata and supports mutation-annotated trees, which are able to capture all known genetic variation in a dataset. It can either be run entirely locally in the browser, from a server-based backend, or as a desktop application. We describe insights that analysing a tree of five million sequences can provide into SARS-CoV-2 evolution, and provide a tool at cov2tree.org for exploring a public tree of more than five million SARS-CoV-2 sequences. Taxo-nium can be applied to any tree, and is available at taxonium.org, with source code at github.com/ theosanderson/taxonium.",
+                "authors": [
+                    {
+                        "name": "Sanderson T."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "eLife",
+                "title": "Taxonium, a web-based tool for exploring large phylogenetic trees"
+            },
+            "pmcid": "PMC9704803",
+            "pmid": "36377483"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        }
+    ]
+}
diff --git a/data/tcpb/tcpb.biotools.json b/data/tcpb/tcpb.biotools.json
new file mode 100644
index 0000000000000..832faf0ab4c83
--- /dev/null
+++ b/data/tcpb/tcpb.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T09:39:12.226691Z",
+    "biotoolsCURIE": "biotools:tcpb",
+    "biotoolsID": "tcpb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Edward G. Hohenstein",
+            "orcidid": "https://orcid.org/0000-0002-2119-2959"
+        },
+        {
+            "name": "Elisa Pieri",
+            "orcidid": "https://orcid.org/0000-0002-5377-5382"
+        },
+        {
+            "name": "Todd J. Martínez",
+            "orcidid": "https://orcid.org/0000-0002-4798-8947"
+        },
+        {
+            "name": "Vinícius Wilian D. Cruzeiro",
+            "orcidid": "https://orcid.org/0000-0002-4739-5447"
+        },
+        {
+            "name": "Yuanheng Wang",
+            "orcidid": "https://orcid.org/0000-0002-6147-0669"
+        }
+    ],
+    "description": "Accelerating QM and QM/MM simulations with a client-server model.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://mtzgroup.github.io/tcpb-client/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Modelling and simulation",
+                    "uri": "http://edamontology.org/operation_2426"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/mtzgroup/tcpb-client",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-03-17T09:39:12.231215Z",
+    "license": "MIT",
+    "name": "TCPB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1063/5.0130886",
+            "metadata": {
+                "abstract": "The routine use of electronic structures in many chemical simulation applications calls for efficient and easy ways to access electronic structure programs. We describe how the graphics processing unit (GPU) accelerated electronic structure program TeraChem can be set up as an electronic structure server, to be easily accessed by third-party client programs. We exploit Google's protocol buffer framework for data serialization and communication. The client interface, called TeraChem protocol buffers (TCPB), has been designed for ease of use and compatibility with multiple programming languages, such as C++, Fortran, and Python. To demonstrate the ease of coupling third-party programs with electronic structures using TCPB, we have incorporated the TCPB client into Amber for quantum mechanics/molecular mechanics (QM/MM) simulations. The TCPB interface saves time with GPU initialization and I/O operations, achieving a speedup of more than 2× compared to a prior file-based implementation for a QM region with ∼250 basis functions. We demonstrate the practical application of TCPB by computing the free energy profile of p-hydroxybenzylidene-2,3-dimethylimidazolinone (p-HBDI-) - a model chromophore in green fluorescent proteins - on the first excited singlet state using Hamiltonian replica exchange for enhanced sampling. All calculations in this work have been performed with the non-commercial freely-available version of TeraChem, which is sufficient for many QM region sizes in common use.",
+                "authors": [
+                    {
+                        "name": "Cruzeiro V.W.D."
+                    },
+                    {
+                        "name": "Hohenstein E.G."
+                    },
+                    {
+                        "name": "Martinez T.J."
+                    },
+                    {
+                        "name": "Pieri E."
+                    },
+                    {
+                        "name": "Wang Y."
+                    }
+                ],
+                "date": "2023-01-28T00:00:00Z",
+                "journal": "Journal of Chemical Physics",
+                "title": "TeraChem protocol buffers (TCPB): Accelerating QM and QM/MM simulations with a client-server model"
+            },
+            "pmid": "36725506"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Biomolecular simulation",
+            "uri": "http://edamontology.org/topic_3892"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Software engineering",
+            "uri": "http://edamontology.org/topic_3372"
+        }
+    ]
+}
diff --git a/data/tcrconv/tcrconv.biotools.json b/data/tcrconv/tcrconv.biotools.json
new file mode 100644
index 0000000000000..cf512d32c8165
--- /dev/null
+++ b/data/tcrconv/tcrconv.biotools.json
@@ -0,0 +1,144 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T15:21:43.519059Z",
+    "biotoolsCURIE": "biotools:tcrconv",
+    "biotoolsID": "tcrconv",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "emmi.jokinen@aalto.fi",
+            "name": "Emmi Jokinen",
+            "orcidid": "https://orcid.org/0000-0002-0060-6868",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "harri.lahdesmaki@aalto.fi",
+            "name": "Harri Lähdesmäki",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Markus Heinonen"
+        },
+        {
+            "name": "Alexandru Dumitrescu",
+            "orcidid": "https://orcid.org/0000-0003-0909-9484"
+        }
+    ],
+    "description": "Predicting recognition between T cell receptors and epitopes using contextualized motifs.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Dimensionality reduction",
+                    "uri": "http://edamontology.org/operation_3935"
+                },
+                {
+                    "term": "Epitope mapping",
+                    "uri": "http://edamontology.org/operation_0416"
+                },
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                },
+                {
+                    "term": "Sequence motif recognition",
+                    "uri": "http://edamontology.org/operation_0239"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/emmijokinen/tcrconv",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-02-10T15:21:43.521430Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/janihuuh/tcrconv_manu"
+        }
+    ],
+    "name": "TCRconv",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC788",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: T cells use T cell receptors (TCRs) to recognize small parts of antigens, called epitopes, presented by major histocompatibility complexes. Once an epitope is recognized, an immune response is initiated and T cell activation and proliferation by clonal expansion begin. Clonal populations of T cells with identical TCRs can remain in the body for years, thus forming immunological memory and potentially mappable immunological signatures, which could have implications in clinical applications including infectious diseases, autoimmunity and tumor immunology. RESULTS: We introduce TCRconv, a deep learning model for predicting recognition between TCRs and epitopes. TCRconv uses a deep protein language model and convolutions to extract contextualized motifs and provides state-of-the-art TCR-epitope prediction accuracy. Using TCR repertoires from COVID-19 patients, we demonstrate that TCRconv can provide insight into T cell dynamics and phenotypes during the disease. AVAILABILITY AND IMPLEMENTATION: TCRconv is available at https://github.com/emmijokinen/tcrconv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Bonneau R."
+                    },
+                    {
+                        "name": "Dumitrescu A."
+                    },
+                    {
+                        "name": "Gligorijevic V."
+                    },
+                    {
+                        "name": "Heinonen M."
+                    },
+                    {
+                        "name": "Huuhtanen J."
+                    },
+                    {
+                        "name": "Jokinen E."
+                    },
+                    {
+                        "name": "Lahdesmaki H."
+                    },
+                    {
+                        "name": "Mustjoki S."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs"
+            },
+            "pmcid": "PMC9825763",
+            "pmid": "36477794"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Immunoproteins and antigens",
+            "uri": "http://edamontology.org/topic_2830"
+        },
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Sequence sites, features and motifs",
+            "uri": "http://edamontology.org/topic_0160"
+        }
+    ]
+}
diff --git a/data/tedd/tedd.biotools.json b/data/tedd/tedd.biotools.json
new file mode 100644
index 0000000000000..961b1ce0337cc
--- /dev/null
+++ b/data/tedd/tedd.biotools.json
@@ -0,0 +1,139 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T14:11:13.541547Z",
+    "biotoolsCURIE": "biotools:tedd",
+    "biotoolsID": "tedd",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "elvisdong@cuhk.edu.hk",
+            "name": "Zirui Dong",
+            "orcidid": "https://orcid.org/0000-0002-3626-6500",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "richardchoy@cuhk.edu.hk",
+            "name": "Kwong Wai Choy",
+            "orcidid": "https://orcid.org/0000-0002-3616-6200",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Cong Tan"
+        },
+        {
+            "name": "Ziheng Zhou"
+        }
+    ],
+    "description": "A database of temporal gene expression patterns during multiple developmental periods in human and model organisms.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Expression profile clustering",
+                    "uri": "http://edamontology.org/operation_0313"
+                },
+                {
+                    "term": "Gene expression QTL analysis",
+                    "uri": "http://edamontology.org/operation_3232"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://TEDD.obg.cuhk.edu.hk/",
+    "lastUpdate": "2023-01-25T14:11:13.566942Z",
+    "name": "TEDD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC978",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Characterization of the specific expression and chromatin profiles of genes enables understanding how they contribute to tissue/organ development and the mechanisms leading to diseases. Whilst the number of single-cell sequencing studies is increasing dramatically; however, data mining and reanalysis remains challenging. Herein, we systematically curated the up-to-date and most comprehensive datasets of sequencing data originating from 2760 bulk samples and over 5.1 million single-cells from multiple developmental periods from humans and multiple model organisms. With unified and systematic analysis, we profiled the gene expression and chromatin accessibility among 481 cell-types, 79 tissue-types and 92 timepoints, and pinpointed cells with the co-expression of target genes. We also enabled the detection of gene(s) with a temporal and cell-type specific expression profile that is similar to or distinct from that of a target gene. Additionally, we illustrated the potential upstream and downstream gene-gene regulation interactions, particularly under the same biological process(es) or KEGG pathway(s). Thus, TEDD (Temporal Expression during Development Database), a value-added database with a user-friendly interface, not only enables researchers to identify cell-type/tissue-type specific and temporal gene expression and chromatin profiles but also facilitates the association of genes with undefined biological functions in development and diseases. The database URL is https://TEDD.obg.cuhk.edu.hk/.",
+                "authors": [
+                    {
+                        "name": "Bellgard M."
+                    },
+                    {
+                        "name": "Cao Y."
+                    },
+                    {
+                        "name": "Chau M.H.K."
+                    },
+                    {
+                        "name": "Chen X."
+                    },
+                    {
+                        "name": "Choy K.W."
+                    },
+                    {
+                        "name": "Dong Z."
+                    },
+                    {
+                        "name": "Jiang X."
+                    },
+                    {
+                        "name": "Ke Z."
+                    },
+                    {
+                        "name": "Kwok Y.K."
+                    },
+                    {
+                        "name": "Leung T.Y."
+                    },
+                    {
+                        "name": "Tan C."
+                    },
+                    {
+                        "name": "Zhou Z."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "TEDD: a database of temporal gene expression patterns during multiple developmental periods in human and model organisms"
+            },
+            "pmcid": "PMC9825605",
+            "pmid": "36350663"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Chromosome conformation capture",
+            "uri": "http://edamontology.org/topic_3940"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        }
+    ]
+}
diff --git a/data/tempo_prediction/tempo_prediction.biotools.json b/data/tempo_prediction/tempo_prediction.biotools.json
new file mode 100644
index 0000000000000..30392b0d59ff9
--- /dev/null
+++ b/data/tempo_prediction/tempo_prediction.biotools.json
@@ -0,0 +1,126 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T15:17:18.702869Z",
+    "biotoolsCURIE": "biotools:tempo_prediction",
+    "biotoolsID": "tempo_prediction",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Alex Chichung Kot"
+        },
+        {
+            "name": "Binbin Zhou"
+        },
+        {
+            "name": "Hang Zhou"
+        },
+        {
+            "name": "Zhan Zhou",
+            "orcidid": "https://orcid.org/0000-0002-2730-5483",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A transformer-based mutation prediction framework for SARS-CoV-2 evolution.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phylogenetic inference",
+                    "uri": "http://edamontology.org/operation_0323"
+                },
+                {
+                    "term": "Phylogenetic tree editing",
+                    "uri": "http://edamontology.org/operation_0326"
+                },
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ZJUDataIntelligence/TEMPO",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-10T15:17:18.705558Z",
+    "license": "Not licensed",
+    "name": "TEMPO",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106264",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdThe widespread of SARS-CoV-2 presents a significant threat to human society, as well as public health and economic development. Extensive efforts have been undertaken to battle against the pandemic, whereas effective approaches such as vaccination would be weakened by the continuous mutations, leading to considerable attention being attracted to the mutation prediction. However, most previous studies lack attention to phylogenetics. In this paper, we propose a novel and effective model TEMPO for predicting the mutation of SARS-CoV-2 evolution. Specifically, we design a phylogenetic tree-based sampling method to generate sequence evolution data. Then, a transformer-based model is presented for the site mutation prediction after learning the high-level representation of these sequence data. We conduct experiments to verify the effectiveness of TEMPO, leveraging a large-scale SARS-CoV- 2 dataset. Experimental results show that TEMPO is effective for mutation prediction of SARS- CoV-2 evolution and outperforms several state-of-the-art baseline methods. We further perform mutation prediction experiments of other infectious viruses, to explore the feasibility and robustness of TEMPO, and experimental results verify its superiority. The codes and datasets are freely available at https://github.com/ZJUDataIntelligence/TEMPO.",
+                "authors": [
+                    {
+                        "name": "Chai Y."
+                    },
+                    {
+                        "name": "Kot A.C."
+                    },
+                    {
+                        "name": "Xu X."
+                    },
+                    {
+                        "name": "Zhang X."
+                    },
+                    {
+                        "name": "Zheng Z."
+                    },
+                    {
+                        "name": "Zhou B."
+                    },
+                    {
+                        "name": "Zhou H."
+                    },
+                    {
+                        "name": "Zhou Z."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution"
+            },
+            "pmcid": "PMC9747230",
+            "pmid": "36535209"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        }
+    ]
+}
diff --git a/data/tensorflow/tensorflow.biotools.json b/data/tensorflow/tensorflow.biotools.json
index 0caca656ba836..4ab09bdded6eb 100644
--- a/data/tensorflow/tensorflow.biotools.json
+++ b/data/tensorflow/tensorflow.biotools.json
@@ -2,6 +2,9 @@
     "additionDate": "2020-01-14T09:37:44Z",
     "biotoolsCURIE": "biotools:tensorflow",
     "biotoolsID": "tensorflow",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
     "confidence_flag": "tool",
     "credit": [
         {
@@ -26,7 +29,7 @@
         }
     ],
     "homepage": "https://www.tensorflow.org",
-    "lastUpdate": "2020-12-28T11:15:02Z",
+    "lastUpdate": "2023-01-31T07:54:39.859773Z",
     "name": "TensorFlow",
     "owner": "zsmag19",
     "publication": [
@@ -108,7 +111,7 @@
                         "name": "Youn Y.C."
                     }
                 ],
-                "citationCount": 8,
+                "citationCount": 29,
                 "date": "2019-11-21T00:00:00Z",
                 "journal": "BMC Medical Informatics and Decision Making",
                 "title": "Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data"
diff --git a/data/termviewer/termviewer.biotools.json b/data/termviewer/termviewer.biotools.json
new file mode 100644
index 0000000000000..7ae3ad22d94f9
--- /dev/null
+++ b/data/termviewer/termviewer.biotools.json
@@ -0,0 +1,103 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T13:58:07.743525Z",
+    "biotoolsCURIE": "biotools:termviewer",
+    "biotoolsID": "termviewer",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "wangk@chop.edu",
+            "name": "Kai Wang",
+            "orcidid": "https://orcid.org/0000-0002-5585-982X"
+        },
+        {
+            "name": "James M. Havrilla"
+        },
+        {
+            "name": "Anna Nixon",
+            "orcidid": "https://orcid.org/0000-0002-6273-4275"
+        },
+        {
+            "name": "Li Fang",
+            "orcidid": "https://orcid.org/0000-0001-5456-0255"
+        }
+    ],
+    "description": "A Web Application for Streamlined Human Phenotype Ontology (HPO) Tagging and Document Annotation.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Filtering",
+                    "uri": "http://edamontology.org/operation_3695"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/WGLab/TermViewer",
+    "language": [
+        "JavaScript",
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-25T13:58:07.746041Z",
+    "license": "Not licensed",
+    "name": "TermViewer",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/CBDV.202200805",
+            "metadata": {
+                "abstract": "© 2022 Wiley-VHCA AG, Zurich, Switzerland.Clinical notes from electronic health records (EHRs) contain a large amount of clinical phenotype data on patients that can provide insights into the phenotypic presentation of various diseases. A number of Natural Language Processing (NLP) algorithms have been utilized in the past few years to annotate medical concepts, such as Human Phenotype Ontology (HPO) terms, from clinical notes. However, efficient use of NLP algorithms requires the use of high-quality clinical notes with phenotype descriptions, and erroneous annotations often exist in results from these NLP algorithms. Manual review by human experts is often needed to compile the correct phenotype information on individual patients. Here we develop TermViewer, a web application that allows multi-party collaborative annotation and quality assessment of clinical notes that have already been processed and tagged by NLP algorithms. TermViewer allows users to view clinical notes with HPO terms highlighted, and to easily classify high-quality notes and revise incorrect tagging of HPO terms. Currently, TermViewer combines MetaMap and cTAKES, two of the most widely used NLP tools for tagging medical terms, and identifies where these two tools agree and disagree, allowing users to perform collaborative manual reviews of computationally generated HPO annotations. TermViewer can be a stand-alone tool for analyzing notes or become part of a machine-learning pipeline where tagged HPO terms can be used as additional input data. TermViewer is available at https://github.com/WGLab/TermViewer.",
+                "authors": [
+                    {
+                        "name": "Fang L."
+                    },
+                    {
+                        "name": "Havrilla J.M."
+                    },
+                    {
+                        "name": "Nixon A."
+                    },
+                    {
+                        "name": "Wang K."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Chemistry and Biodiversity",
+                "title": "Termviewer – A Web Application for Streamlined Human Phenotype Ontology (HPO) Tagging and Document Annotation"
+            },
+            "pmid": "36328766"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Medical informatics",
+            "uri": "http://edamontology.org/topic_3063"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        }
+    ]
+}
diff --git a/data/teslatoto/teslatoto.biotools.json b/data/teslatoto/teslatoto.biotools.json
deleted file mode 100644
index 7a3aec2686527..0000000000000
--- a/data/teslatoto/teslatoto.biotools.json
+++ /dev/null
@@ -1,57 +0,0 @@
-{
-    "additionDate": "2022-01-02T02:06:28.669675Z",
-    "biotoolsCURIE": "biotools:teslatoto",
-    "biotoolsID": "teslatoto",
-    "description": "TESLATOTO Merupakan Bandar Togel Online terpercaya dengan diskon mencapai 66% menyediakan beragam jenis permainan hanya menggunakan 1 userid",
-    "editPermission": {
-        "type": "private"
-    },
-    "homepage": "https://139.59.99.87/",
-    "lastUpdate": "2022-01-02T02:10:48.167860Z",
-    "link": [
-        {
-            "note": "Daftar Sekarang - TeslaToto",
-            "type": [
-                "Service"
-            ],
-            "url": "https://cutt.ly/DaftarTeslaToto"
-        },
-        {
-            "note": "Facebook - TeslaToto",
-            "type": [
-                "Social media"
-            ],
-            "url": "https://cutt.ly/FbTeslatoto"
-        },
-        {
-            "note": "Instagram - TeslaToto",
-            "type": [
-                "Social media"
-            ],
-            "url": "https://cutt.ly/IGTeslaToto"
-        },
-        {
-            "note": "Live Chat 24 Jam",
-            "type": [
-                "Helpdesk"
-            ],
-            "url": "https://cutt.ly/LiveChatTeslaToto"
-        },
-        {
-            "note": "Promo-Promo TeslaToto",
-            "type": [
-                "Software catalogue"
-            ],
-            "url": "https://139.59.99.87/promotion.php"
-        },
-        {
-            "note": "WhatsApp - TeslaToto",
-            "type": [
-                "Social media"
-            ],
-            "url": "https://cutt.ly/WATeslaToto"
-        }
-    ],
-    "name": "TeslaToto",
-    "owner": "TeslaToto"
-}
diff --git a/data/tf-prioritizer/tf-prioritizer.biotools.json b/data/tf-prioritizer/tf-prioritizer.biotools.json
new file mode 100644
index 0000000000000..742c4af4023ab
--- /dev/null
+++ b/data/tf-prioritizer/tf-prioritizer.biotools.json
@@ -0,0 +1,38 @@
+{
+    "additionDate": "2023-02-01T09:57:11.697825Z",
+    "biotoolsCURIE": "biotools:tf-prioritizer",
+    "biotoolsID": "tf-prioritizer",
+    "description": "This pipeline gives you a full analysis of nfcore chromatine accessibility data (ChIP-Seq, DNAse-Seq or ATAC-Seq and nfcore RNA-seq count data. It performs DESeq2, TEPIC and DYNAMITE including all preprocessing and postprocessing steps necessary to transform the data. It also gives you plots for deep analysis of the data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://github.com/biomedbigdata/TF-Prioritizer",
+    "lastUpdate": "2023-02-02T09:24:38.569292Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "note": "GitHub Repo",
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/biomedbigdata/TF-Prioritizer"
+        }
+    ],
+    "maturity": "Emerging",
+    "name": "TF-Prioritizer",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "nictru",
+    "toolType": [
+        "Workflow"
+    ],
+    "topic": [
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        }
+    ]
+}
diff --git a/data/tiedie/tiedie.biotools.json b/data/tiedie/tiedie.biotools.json
index 43b431826a391..5ef3899c44bb2 100644
--- a/data/tiedie/tiedie.biotools.json
+++ b/data/tiedie/tiedie.biotools.json
@@ -6,7 +6,22 @@
     "collectionID": [
         "PerMedCoE"
     ],
+    "confidence_flag": "tool",
     "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Daniel E Carlin"
+        },
+        {
+            "name": "David Haussler"
+        },
+        {
+            "name": "Evan O Paull"
+        },
+        {
+            "name": "Joshua M Stuart"
+        }
+    ],
     "description": "Application of TieDIE to The Cancer Genome Atlas and a breast cancer cell line dataset identified key signaling pathways, with examples impinging on MYC activity. Interlinking genes are predicted to correspond to essential components of cancer signaling and may provide a mechanistic explanation of tumor character and suggest subtype-specific drug targets.",
     "editPermission": {
         "type": "public"
@@ -17,7 +32,7 @@
         "Python",
         "R"
     ],
-    "lastUpdate": "2022-12-09T23:11:53.120115Z",
+    "lastUpdate": "2023-01-11T00:57:02.907360Z",
     "license": "GPL-3.0",
     "name": "TieDie",
     "operatingSystem": [
@@ -51,13 +66,18 @@
                         "name": "Stuart J.M."
                     }
                 ],
-                "citationCount": 124,
+                "citationCount": 126,
                 "date": "2013-11-01T00:00:00Z",
                 "journal": "Bioinformatics",
                 "title": "Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE)"
-            }
+            },
+            "pmcid": "PMC3799471",
+            "pmid": "23986566"
         }
     ],
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "Molecular interactions, pathways and networks",
diff --git a/data/ties/ties.biotools.json b/data/ties/ties.biotools.json
new file mode 100644
index 0000000000000..6803417a5ffbe
--- /dev/null
+++ b/data/ties/ties.biotools.json
@@ -0,0 +1,132 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T09:24:57.943624Z",
+    "biotoolsCURIE": "biotools:ties",
+    "biotoolsID": "ties",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "p.v.coveney@ucl.ac.uk",
+            "name": "Peter V. Coveney",
+            "orcidid": "https://orcid.org/0000-0002-8787-7256",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Agastya P. Bhati"
+        },
+        {
+            "name": "Alexander D. Wade"
+        },
+        {
+            "name": "Mateusz K. Bieniek",
+            "orcidid": "https://orcid.org/0000-0002-3065-5417"
+        },
+        {
+            "name": "Shunzhou Wan",
+            "orcidid": "https://orcid.org/0000-0001-7192-1999"
+        }
+    ],
+    "description": "A Dual-Topology Open Source Relative Binding Free Energy Builder with Web Portal.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://ucl-ccs.github.io/TIES_MD/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Simulation analysis",
+                    "uri": "http://edamontology.org/operation_0244"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://ccs-ties.org",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T09:24:57.948583Z",
+    "license": "LGPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/UCL-CCS/TIES_MD"
+        }
+    ],
+    "name": "TIES",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCIM.2C01596",
+            "metadata": {
+                "abstract": "Relative binding free energy (RBFE) calculations are widely used to aid the process of drug discovery. TIES, Thermodynamic Integration with Enhanced Sampling, is a dual-topology approach to RBFE calculations with support for NAMD and OpenMM molecular dynamics engines. The software has been thoroughly validated on publicly available datasets. Here we describe the open source software along with a web portal (https://ccs-ties.org) that enables users to perform such calculations correctly and rapidly.",
+                "authors": [
+                    {
+                        "name": "Bhati A.P."
+                    },
+                    {
+                        "name": "Bieniek M.K."
+                    },
+                    {
+                        "name": "Coveney P.V."
+                    },
+                    {
+                        "name": "Wade A.D."
+                    },
+                    {
+                        "name": "Wan S."
+                    }
+                ],
+                "date": "2023-02-13T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "TIES 2.0: A Dual-Topology Open Source Relative Binding Free Energy Builder with Web Portal"
+            },
+            "pmcid": "PMC9930115",
+            "pmid": "36719676"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Molecular dynamics",
+            "uri": "http://edamontology.org/topic_0176"
+        },
+        {
+            "term": "Physics",
+            "uri": "http://edamontology.org/topic_3318"
+        }
+    ],
+    "version": [
+        "2.0"
+    ]
+}
diff --git a/data/timedb/timedb.biotools.json b/data/timedb/timedb.biotools.json
new file mode 100644
index 0000000000000..056157fa540f3
--- /dev/null
+++ b/data/timedb/timedb.biotools.json
@@ -0,0 +1,149 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T02:00:12.496062Z",
+    "biotoolsCURIE": "biotools:timedb",
+    "biotoolsID": "timedb",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "shuaicli@cityu.edu.hk",
+            "name": "Shuaicheng Li",
+            "orcidid": "https://orcid.org/0000-0001-6246-6349",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Haoyue Cheng"
+        },
+        {
+            "name": "Lingxi Chen",
+            "orcidid": "https://orcid.org/0000-0002-5229-7470"
+        },
+        {
+            "name": "Xueying Wang",
+            "orcidid": "https://orcid.org/0000-0001-8979-3737"
+        }
+    ],
+    "description": "Tumor immune micro-environment cell composition database with automatic analysis and interactive visualization.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deisotoping",
+                    "uri": "http://edamontology.org/operation_3629"
+                },
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://timedb.deepomics.org",
+    "lastUpdate": "2023-01-25T02:00:12.498631Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/deepomicslab/TIMEDB"
+        }
+    ],
+    "name": "TIMEDB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1006",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.Deciphering the cell-type composition in the tumor immune microenvironment (TIME) can significantly increase the efficacy of cancer treatment and improve the prognosis of cancer. Such a task has benefited from microarrays and RNA sequencing technologies, which have been widely adopted in cancer studies, resulting in extensive expression profiles with clinical phenotypes across multiple cancers. Current state-of-the-art tools can infer cell-type composition from bulk expression profiles, providing the possibility of investigating the inter-heterogeneity and intra-heterogeneity of TIME across cancer types. Much can be gained from these tools in conjunction with a well-curated database of TIME cell-type composition data, accompanied by the corresponding clinical information. However, currently available databases fall short in data volume, multi-platform dataset integration, and tool integration. In this work, we introduce TIMEDB (https://timedb.deepomics.org), an online database for human tumor immune microenvironment cell-type composition estimated from bulk expression profiles. TIMEDB stores manually curated expression profiles, cell-type composition profiles, and the corresponding clinical information of a total of 39,706 samples from 546 datasets across 43 cancer types. TIMEDB comes readily equipped with online tools for automatic analysis and interactive visualization, and aims to serve the community as a convenient tool for investigating the human tumor microenvironment.",
+                "authors": [
+                    {
+                        "name": "Chen L."
+                    },
+                    {
+                        "name": "Cheng H."
+                    },
+                    {
+                        "name": "Dong J."
+                    },
+                    {
+                        "name": "Lai Z."
+                    },
+                    {
+                        "name": "Li S."
+                    },
+                    {
+                        "name": "Liu D."
+                    },
+                    {
+                        "name": "Liu W."
+                    },
+                    {
+                        "name": "Shi S."
+                    },
+                    {
+                        "name": "Wang X."
+                    },
+                    {
+                        "name": "Zhang W."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zhao B."
+                    },
+                    {
+                        "name": "Zhou C."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "TIMEDB: tumor immune micro-environment cell composition database with automatic analysis and interactive visualization"
+            },
+            "pmcid": "PMC9825442",
+            "pmid": "36399488"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Allergy, clinical immunology and immunotherapeutics",
+            "uri": "http://edamontology.org/topic_3400"
+        },
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/timothy/timothy.biotools.json b/data/timothy/timothy.biotools.json
index e6e4e8c0e6ecf..ef2e3122797b3 100644
--- a/data/timothy/timothy.biotools.json
+++ b/data/timothy/timothy.biotools.json
@@ -6,14 +6,23 @@
     "collectionID": [
         "PerMedCoE"
     ],
+    "confidence_flag": "tool",
     "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Maciej Cytowski"
+        },
+        {
+            "name": "Zuzanna Szymanska"
+        }
+    ],
     "description": "Timothy is a novel large scale parallel computational model allowing 3-D simulations of cell colonies growing and interacting with variable environment in previously unavailable tissue scale.\nThe cells are modeled as individuals located in the lattice-free 3-D space. The model incorporates cellular environment modelled in a continuous manner, mathematical description based on partial differential equations is formulated for selected important components of the environment. Discrete and continuous formulations are efficiently coupled in one model and allow considerations on different scales: sub-cellular, cellular and tissue scale.\nHigh parallel scalability achieved allows simulation of up to 109 individual cells. This large scale computational approach allows for simulations to be carried out over realistic spatial scales up to 1cm in size i.e. the tissue scale.",
     "documentation": [
         {
             "type": [
-                "Other"
+                "General"
             ],
-            "url": "https://timothy.icm.edu.pl/examples.html"
+            "url": "https://timothy.icm.edu.pl/doc/index.html"
         }
     ],
     "download": [
@@ -50,8 +59,8 @@
     "language": [
         "C"
     ],
-    "lastUpdate": "2022-12-09T23:14:06.662181Z",
-    "license": "gnuplot",
+    "lastUpdate": "2023-01-10T16:57:19.384628Z",
+    "license": "GPL-2.0",
     "name": "Timothy",
     "operatingSystem": [
         "Linux"
@@ -77,6 +86,9 @@
             }
         }
     ],
+    "toolType": [
+        "Desktop application"
+    ],
     "topic": [
         {
             "term": "Cell biology",
diff --git a/data/tindl/tindl.biotools.json b/data/tindl/tindl.biotools.json
new file mode 100644
index 0000000000000..a29097fd28432
--- /dev/null
+++ b/data/tindl/tindl.biotools.json
@@ -0,0 +1,83 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T09:16:08.674310Z",
+    "biotoolsCURIE": "biotools:tindl",
+    "biotoolsID": "tindl",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Amin Emad"
+        },
+        {
+            "name": "David Earl Hostallero"
+        },
+        {
+            "name": "Junmei Cairns"
+        },
+        {
+            "name": "Liewei Wang"
+        },
+        {
+            "name": "Lixuan Wei"
+        }
+    ],
+    "description": "Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ddhostallero/tindl",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T09:16:08.678424Z",
+    "license": "Not licensed",
+    "name": "TINDL",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.GPB.2023.01.006",
+            "pmid": "36775056"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Biomarkers",
+            "uri": "http://edamontology.org/topic_3360"
+        },
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/tist/tist.biotools.json b/data/tist/tist.biotools.json
new file mode 100644
index 0000000000000..5cb84f628f39c
--- /dev/null
+++ b/data/tist/tist.biotools.json
@@ -0,0 +1,128 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T15:10:47.571248Z",
+    "biotoolsCURIE": "biotools:tist",
+    "biotoolsID": "tist",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Jin Gu",
+            "orcidid": "https://orcid.org/0000-0003-3968-8036"
+        },
+        {
+            "name": "Qian Zhang",
+            "orcidid": "https://orcid.org/0000-0002-2153-842X"
+        },
+        {
+            "name": "Qiuyu Lian",
+            "orcidid": "https://orcid.org/0000-0002-5279-1989"
+        },
+        {
+            "name": "Yiran Shan",
+            "orcidid": "https://orcid.org/0000-0002-9663-3499"
+        }
+    ],
+    "description": "Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://github.com/ShanYiran/TIST/wiki/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Expression profile clustering",
+                    "uri": "http://edamontology.org/operation_0313"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Statistical calculation",
+                    "uri": "http://edamontology.org/operation_2238"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://lifeome.net/software/tist/",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-02-10T15:10:47.573891Z",
+    "license": "Not licensed",
+    "name": "TIST",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.GPB.2022.11.012",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsSequencing-based spatial transcriptomics (ST) is an emerging technology to study in situ gene expression patterns at the whole-genome scale. Currently, ST data analysis is still complicated by high technical noises and low resolution. In addition to the transcriptomic data, matched histopathological images are usually generated for the same tissue sample along the ST experiment. The matched high-resolution histopathological images provide complementary cellular phenotypical information, serving as an opportunity to mitigate the noises in ST data. We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST (TIST), which enables the identification of spatial clusters (SCs) and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images. TIST devises a histopathological feature extraction method based on Markov random field (MRF) to learn the cellular features from histopathological images, and integrates them with the transcriptomic data and location information as a network, termed TIST-net. Based on TIST-net, the SCs are identified by a random walk-based strategy, and the gene expression patterns are enhanced by neighborhood smoothing. We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods. Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios. TIST is available at http://lifeome.net/software/tist/ and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.",
+                "authors": [
+                    {
+                        "name": "Gu J."
+                    },
+                    {
+                        "name": "Guo W."
+                    },
+                    {
+                        "name": "Lian Q."
+                    },
+                    {
+                        "name": "Miao Y."
+                    },
+                    {
+                        "name": "Shan Y."
+                    },
+                    {
+                        "name": "Wu Y."
+                    },
+                    {
+                        "name": "Xin H."
+                    },
+                    {
+                        "name": "Zhang Q."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Genomics, Proteomics and Bioinformatics",
+                "title": "TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics"
+            },
+            "pmid": "36549467"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/tmalphafold_database/tmalphafold_database.biotools.json b/data/tmalphafold_database/tmalphafold_database.biotools.json
new file mode 100644
index 0000000000000..5b4cc1eab248b
--- /dev/null
+++ b/data/tmalphafold_database/tmalphafold_database.biotools.json
@@ -0,0 +1,155 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T01:54:39.779541Z",
+    "biotoolsCURIE": "biotools:tmalphafold_database",
+    "biotoolsID": "tmalphafold_database",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "tusnady.gabor@ttk.hu",
+            "name": "Gábor E Tusnády",
+            "orcidid": "https://orcid.org/0000-0001-8105-0285",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "András Zeke"
+        },
+        {
+            "name": "Csongor Gerdán"
+        },
+        {
+            "name": "Laszlo Dobson"
+        },
+        {
+            "name": "Levente I Szekeres"
+        },
+        {
+            "name": "Tamás Langó"
+        }
+    ],
+    "description": "Membrane localization and evaluation of AlphaFold2 predicted alpha-helical transmembrane protein structures.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene name",
+                        "uri": "http://edamontology.org/data_2299"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Organism name",
+                        "uri": "http://edamontology.org/data_2909"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Protein name",
+                        "uri": "http://edamontology.org/data_1009"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "UniProt accession",
+                        "uri": "http://edamontology.org/data_3021"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Protein structure prediction",
+                    "uri": "http://edamontology.org/operation_0474"
+                },
+                {
+                    "term": "Transmembrane protein prediction",
+                    "uri": "http://edamontology.org/operation_0269"
+                },
+                {
+                    "term": "Transmembrane protein visualisation",
+                    "uri": "http://edamontology.org/operation_2241"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://tmalphafold.ttk.hu/",
+    "lastUpdate": "2023-01-25T01:54:39.782165Z",
+    "name": "TmAlphaFold database",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC928",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.AI-driven protein structure prediction, most notably AlphaFold2 (AF2) opens new frontiers for almost all fields of structural biology. As traditional structure prediction methods for transmembrane proteins were both complicated and error prone, AF2 is a great help to the community. Complementing the relatively meager number of experimental structures, AF2 provides 3D predictions for thousands of new alpha-helical membrane proteins. However, the lack of reliable structural templates and the fact that AF2 was not trained to handle phase boundaries also necessitates a delicate assessment of structural correctness. In our new database, Transmembrane AlphaFold database (TmAlphaFold database), we apply TMDET, a simple geometry-based method to visualize the likeliest position of the membrane plane. In addition, we calculate several parameters to evaluate the location of the protein into the membrane. This also allows TmAlphaFold database to show whether the predicted 3D structure is realistic or not. The TmAlphaFold database is available at https://tmalphafold.ttk.hu/.",
+                "authors": [
+                    {
+                        "name": "Dobson L."
+                    },
+                    {
+                        "name": "Gerdan C."
+                    },
+                    {
+                        "name": "Lango T."
+                    },
+                    {
+                        "name": "Szekeres L.I."
+                    },
+                    {
+                        "name": "Tusnady G.E."
+                    },
+                    {
+                        "name": "Zeke A."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "TmAlphaFold database: membrane localization and evaluation of AlphaFold2 predicted alpha-helical transmembrane protein structures"
+            },
+            "pmcid": "PMC9825488",
+            "pmid": "36318239"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Lipids",
+            "uri": "http://edamontology.org/topic_0153"
+        },
+        {
+            "term": "Membrane and lipoproteins",
+            "uri": "http://edamontology.org/topic_0820"
+        },
+        {
+            "term": "Protein structure analysis",
+            "uri": "http://edamontology.org/topic_2814"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        }
+    ]
+}
diff --git a/data/tmodbase/tmodbase.biotools.json b/data/tmodbase/tmodbase.biotools.json
new file mode 100644
index 0000000000000..4d7778f8a904c
--- /dev/null
+++ b/data/tmodbase/tmodbase.biotools.json
@@ -0,0 +1,121 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T01:49:13.313220Z",
+    "biotoolsCURIE": "biotools:tmodbase",
+    "biotoolsID": "tmodbase",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "zhengll33@mail.sysu.edu.cn",
+            "name": "Ling-Ling Zheng",
+            "orcidid": "https://orcid.org/0000-0002-7152-1095",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Hao-Tian Lei"
+        },
+        {
+            "name": "Zhang-Hao Wang"
+        },
+        {
+            "name": "Liang-Hu Qu",
+            "orcidid": "https://orcid.org/0000-0003-3657-2863"
+        }
+    ],
+    "description": "Deciphering the landscape of tRNA modifications and their dynamic changes from epitranscriptome data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Differential gene expression profiling",
+                    "uri": "http://edamontology.org/operation_3223"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                },
+                {
+                    "term": "tRNA gene prediction",
+                    "uri": "http://edamontology.org/operation_0464"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.tmodbase.com/",
+    "lastUpdate": "2023-01-25T01:49:13.315749Z",
+    "name": "tModBase",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1087",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.tRNA molecules contain dense, abundant modifications that affect tRNA structure, stability, mRNA decoding and tsRNA formation. tRNA modifications and related enzymes are responsive to environmental cues and are associated with a range of physiological and pathological processes. However, there is a lack of resources that can be used to mine and analyse these dynamically changing tRNA modifications. In this study, we established tModBase (https://www.tmodbase.com/) for deciphering the landscape of tRNA modification profiles from epitranscriptome data. We analysed 103 datasets generated with second- and third-generation sequencing technologies and illustrated the misincorporation and termination signals of tRNA modification sites in ten species. We thus systematically demonstrate the modification profiles across different tissues/cell lines and summarize the characteristics of tRNA-associated human diseases. By integrating transcriptome data from 32 cancers, we developed novel tools for analysing the relationships between tRNA modifications and RNA modification enzymes, the expression of 1442 tRNA-derived small RNAs (tsRNAs), and 654 DNA variations. Our database will provide new insights into the features of tRNA modifications and the biological pathways in which they participate.",
+                "authors": [
+                    {
+                        "name": "Lei H.-T."
+                    },
+                    {
+                        "name": "Li B."
+                    },
+                    {
+                        "name": "Mei S.-Q."
+                    },
+                    {
+                        "name": "Qu L.-H."
+                    },
+                    {
+                        "name": "Sun Y."
+                    },
+                    {
+                        "name": "Wang Z.-H."
+                    },
+                    {
+                        "name": "Yang J.-H."
+                    },
+                    {
+                        "name": "Zheng L.-L."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "tModBase: deciphering the landscape of tRNA modifications and their dynamic changes from epitranscriptome data"
+            },
+            "pmcid": "PMC9825477",
+            "pmid": "36408909"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/togovar/togovar.biotools.json b/data/togovar/togovar.biotools.json
new file mode 100644
index 0000000000000..0408856b6e262
--- /dev/null
+++ b/data/togovar/togovar.biotools.json
@@ -0,0 +1,125 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T15:05:14.639275Z",
+    "biotoolsCURIE": "biotools:togovar",
+    "biotoolsID": "togovar",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mitsuhashi@dbcls.rois.ac.jp",
+            "name": "Nobutaka Mitsuhashi",
+            "orcidid": "https://orcid.org/0000-0003-3300-7308",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Kazunori Miyazaki"
+        },
+        {
+            "name": "Licht Toyo-oka"
+        },
+        {
+            "name": "Toshihisa Takagi"
+        }
+    ],
+    "description": "A comprehensive Japanese genetic variation database.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "Variant filtering",
+                    "uri": "http://edamontology.org/operation_3675"
+                },
+                {
+                    "term": "Variant prioritisation",
+                    "uri": "http://edamontology.org/operation_3226"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://togovar.org",
+    "lastUpdate": "2023-02-10T15:05:14.641909Z",
+    "name": "TogoVar",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41439-022-00222-9",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).TogoVar (https://togovar.org) is a database that integrates allele frequencies derived from Japanese populations and provides annotations for variant interpretation. First, a scheme to reanalyze individual-level genome sequence data deposited in the Japanese Genotype-phenotype Archive (JGA), a controlled-access database, was established to make allele frequencies publicly available. As more Japanese individual-level genome sequence data are deposited in JGA, the sample size employed in TogoVar is expected to increase, contributing to genetic study as reference data for Japanese populations. Second, public datasets of Japanese and non-Japanese populations were integrated into TogoVar to easily compare allele frequencies in Japanese and other populations. Each variant detected in Japanese populations was assigned a TogoVar ID as a permanent identifier. Third, these variants were annotated with molecular consequence, pathogenicity, and literature information for interpreting and prioritizing variants. Here, we introduce the newly developed TogoVar database that compares allele frequencies among Japanese and non-Japanese populations and describes the integrated annotations.",
+                "authors": [
+                    {
+                        "name": "Katayama T."
+                    },
+                    {
+                        "name": "Kawashima M."
+                    },
+                    {
+                        "name": "Kawashima S."
+                    },
+                    {
+                        "name": "Mitsuhashi N."
+                    },
+                    {
+                        "name": "Miyazaki K."
+                    },
+                    {
+                        "name": "Takagi T."
+                    },
+                    {
+                        "name": "Toyo-oka L."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Human Genome Variation",
+                "title": "TogoVar: A comprehensive Japanese genetic variation database"
+            },
+            "pmcid": "PMC9744889",
+            "pmid": "36509753"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Population genetics",
+            "uri": "http://edamontology.org/topic_3056"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        }
+    ]
+}
diff --git a/data/tomexo/tomexo.biotools.json b/data/tomexo/tomexo.biotools.json
new file mode 100644
index 0000000000000..5b840074a5a92
--- /dev/null
+++ b/data/tomexo/tomexo.biotools.json
@@ -0,0 +1,93 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T15:01:09.861288Z",
+    "biotoolsCURIE": "biotools:tomexo",
+    "biotoolsID": "tomexo",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jens.lagergren@scilifelab.se",
+            "name": "Jens Lagergren",
+            "orcidid": "https://orcid.org/0000-0002-4552-0240",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Mohammadreza Mohaghegh Neyshabouri,",
+            "orcidid": "https://orcid.org/0000-0002-0782-8308"
+        }
+    ],
+    "description": "A probabilistic tree-structured model for cancer progression.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Phylogenetic inference (maximum likelihood and Bayesian methods)",
+                    "uri": "http://edamontology.org/operation_0547"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/mrmohaghegh/tomexo",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-10T15:01:09.863903Z",
+    "license": "MIT",
+    "name": "ToMExO",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PCBI.1010732",
+            "metadata": {
+                "abstract": "© 2022 Mohaghegh Neyshabouri, Lagergren. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method’s performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods.",
+                "authors": [
+                    {
+                        "name": "Lagergren J."
+                    },
+                    {
+                        "name": "Neyshabouri M.M."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "PLoS Computational Biology",
+                "title": "ToMExO: A probabilistic tree-structured model for cancer progression"
+            },
+            "pmcid": "PMC9754607",
+            "pmid": "36469540"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/tool_recommender_system_in_galaxy_using_deep_learning/tool_recommender_system_in_galaxy_using_deep_learning.biotools.json b/data/tool_recommender_system_in_galaxy_using_deep_learning/tool_recommender_system_in_galaxy_using_deep_learning.biotools.json
deleted file mode 100644
index fb7182b9d788c..0000000000000
--- a/data/tool_recommender_system_in_galaxy_using_deep_learning/tool_recommender_system_in_galaxy_using_deep_learning.biotools.json
+++ /dev/null
@@ -1,23 +0,0 @@
-{
-    "additionDate": "2020-05-13T22:04:27Z",
-    "biotoolsCURIE": "biotools:tool_recommender_system_in_galaxy_using_deep_learning",
-    "biotoolsID": "tool_recommender_system_in_galaxy_using_deep_learning",
-    "description": "A model is developed to recommend tools, by analysing workflows composed by researchers on the European Galaxy server, using a deep learning approach. The model is used to recommend tools in Galaxy.",
-    "editPermission": {
-        "type": "private"
-    },
-    "homepage": "https://github.com/anuprulez/galaxy_tool_recommendation",
-    "lastUpdate": "2022-12-09T23:16:07.929079Z",
-    "name": "Tool recommender system in Galaxy using deep learning",
-    "owner": "admin",
-    "topic": [
-        {
-            "term": "Machine learning",
-            "uri": "http://edamontology.org/topic_3474"
-        },
-        {
-            "term": "Workflows",
-            "uri": "http://edamontology.org/topic_0769"
-        }
-    ]
-}
diff --git a/data/toolkits-monte-carlo-dose-simulation-visualization/toolkits-monte-carlo-dose-simulation-visualization.biotools.json b/data/toolkits-monte-carlo-dose-simulation-visualization/toolkits-monte-carlo-dose-simulation-visualization.biotools.json
index f6955dd3b1e20..8ebf2bab930f7 100644
--- a/data/toolkits-monte-carlo-dose-simulation-visualization/toolkits-monte-carlo-dose-simulation-visualization.biotools.json
+++ b/data/toolkits-monte-carlo-dose-simulation-visualization/toolkits-monte-carlo-dose-simulation-visualization.biotools.json
@@ -7,6 +7,7 @@
         "File Exchange",
         "MATLAB"
     ],
+    "confidence_flag": "tool",
     "cost": "Free of charge (with restrictions)",
     "credit": [
         {
@@ -32,7 +33,8 @@
     "language": [
         "MATLAB"
     ],
-    "lastUpdate": "2021-05-21T18:49:30Z",
+    "lastUpdate": "2023-01-10T16:40:51.348990Z",
+    "license": "Not licensed",
     "name": "Toolkits for monte carlo dose simulation and visualization",
     "operatingSystem": [
         "Linux",
diff --git a/data/topiary/topiary.biotools.json b/data/topiary/topiary.biotools.json
new file mode 100644
index 0000000000000..6210a0ed01543
--- /dev/null
+++ b/data/topiary/topiary.biotools.json
@@ -0,0 +1,128 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T14:47:00.210469Z",
+    "biotoolsCURIE": "biotools:topiary",
+    "biotoolsID": "topiary",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "harms@uoregon.edu",
+            "name": "Michael J. Harms",
+            "orcidid": "https://orcid.org/0000-0002-0241-4122"
+        },
+        {
+            "name": "Joseph L. Harman"
+        },
+        {
+            "name": "Kona N. Orlandi"
+        },
+        {
+            "name": "Sophia R. Phillips"
+        },
+        {
+            "name": "Zachary R. Sailer"
+        }
+    ],
+    "description": "Pruning the manual labor from ancestral sequence reconstruction.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://topiary-asr.readthedocs.io/en/latest/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Ancestral reconstruction",
+                    "uri": "http://edamontology.org/operation_3745"
+                },
+                {
+                    "term": "Gene tree construction",
+                    "uri": "http://edamontology.org/operation_0553"
+                },
+                {
+                    "term": "Phylogenetic reconstruction",
+                    "uri": "http://edamontology.org/operation_3478"
+                },
+                {
+                    "term": "Phylogenetic tree reconciliation",
+                    "uri": "http://edamontology.org/operation_3947"
+                },
+                {
+                    "term": "Species tree construction",
+                    "uri": "http://edamontology.org/operation_0544"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/harmslab/topiary",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-10T14:47:00.213004Z",
+    "license": "MIT",
+    "name": "topiary",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1002/PRO.4551",
+            "metadata": {
+                "abstract": "© 2022 The Protein Society.Ancestral sequence reconstruction (ASR) is a powerful tool to study the evolution of proteins and thus gain deep insight into the relationships among protein sequence, structure, and function. A major barrier to its broad use is the complexity of the task: it requires multiple software packages, complex file manipulations, and expert phylogenetic knowledge. Here we introduce topiary, a software pipeline that aims to overcome this barrier. To use topiary, users prepare a spreadsheet with a handful of sequences. Topiary then: (1) Infers the taxonomic scope for the ASR study and finds relevant sequences by BLAST; (2) Does taxonomically informed sequence quality control and redundancy reduction; (3) Constructs a multiple sequence alignment; (4) Generates a maximum-likelihood gene tree; (5) Reconciles the gene tree to the species tree; (6) Reconstructs ancestral amino acid sequences; and (7) Determines branch supports. The pipeline returns annotated evolutionary trees, spreadsheets with sequences, and graphical summaries of ancestor quality. This is achieved by integrating modern phylogenetics software (Muscle5, RAxML-NG, GeneRax, and PastML) with online databases (NCBI and the Open Tree of Life). In this paper, we introduce non-expert readers to the steps required for ASR, describe the specific design choices made in topiary, provide a detailed protocol for users, and then validate the pipeline using datasets from a broad collection of protein families. Topiary is freely available for download: https://github.com/harmslab/topiary.",
+                "authors": [
+                    {
+                        "name": "Harman J.L."
+                    },
+                    {
+                        "name": "Harms M.J."
+                    },
+                    {
+                        "name": "Orlandi K.N."
+                    },
+                    {
+                        "name": "Phillips S.R."
+                    },
+                    {
+                        "name": "Sailer Z.R."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "Protein science : a publication of the Protein Society",
+                "title": "Topiary: Pruning the manual labor from ancestral sequence reconstruction"
+            },
+            "pmid": "36565302"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cladistics",
+            "uri": "http://edamontology.org/topic_3944"
+        },
+        {
+            "term": "Gene and protein families",
+            "uri": "http://edamontology.org/topic_0623"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/torchdiva/torchdiva.biotools.json b/data/torchdiva/torchdiva.biotools.json
new file mode 100644
index 0000000000000..0863bdfc55774
--- /dev/null
+++ b/data/torchdiva/torchdiva.biotools.json
@@ -0,0 +1,99 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T09:10:33.478894Z",
+    "biotoolsCURIE": "biotools:torchdiva",
+    "biotoolsID": "torchdiva",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "skinahan@asu.edu",
+            "name": "Sean P. Kinahan",
+            "orcidid": "https://orcid.org/0000-0002-2369-4063",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Julie M Liss"
+        },
+        {
+            "name": "Visar Berisha"
+        }
+    ],
+    "description": "An extensible computational model of speech production built on an open-source machine learning library.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Modelling and simulation",
+                    "uri": "http://edamontology.org/operation_2426"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/skinahan/DIVA_PyTorch",
+    "language": [
+        "MATLAB",
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T09:10:33.484530Z",
+    "license": "Not licensed",
+    "name": "TorchDIVA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PONE.0281306",
+            "metadata": {
+                "abstract": "The DIVA model is a computational model of speech motor control that combines a simulation of the brain regions responsible for speech production with a model of the human vocal tract. The model is currently implemented in Matlab Simulink; however, this is less than ideal as most of the development in speech technology research is done in Python. This means there is a wealth of machine learning tools which are freely available in the Python ecosystem that cannot be easily integrated with DIVA. We present TorchDIVA, a full rebuild of DIVA in Python using PyTorch tensors. DIVA source code was directly translated from Matlab to Python, and built-in Simulink signal blocks were implemented from scratch. After implementation, the accuracy of each module was evaluated via systematic block-by-block validation. The TorchDIVA model is shown to produce outputs that closely match those of the original DIVA model, with a negligible difference between the two. We additionally present an example of the extensibility of TorchDIVA as a research platform. Speech quality enhancement in TorchDIVA is achieved through an integration with an existing PyTorch generative vocoder called DiffWave. A modified DiffWave mel-spectrum upsampler was trained on human speech waveforms and conditioned on the TorchDIVA speech production. The results indicate improved speech quality metrics in the DiffWave-enhanced output as compared to the baseline. This enhancement would have been difficult or impossible to accomplish in the original Matlab implementation. This proof-of-concept demonstrates the value TorchDIVA can bring to the research community. Researchers can download the new implementation at: https://github.com/skinahan/DIVA_PyTorch.",
+                "authors": [
+                    {
+                        "name": "Berisha V."
+                    },
+                    {
+                        "name": "Kinahan S.P."
+                    },
+                    {
+                        "name": "Liss J.M."
+                    }
+                ],
+                "date": "2023-02-01T00:00:00Z",
+                "journal": "PLoS ONE",
+                "title": "TorchDIVA: An extensible computational model of speech production built on an open-source machine learning library"
+            },
+            "pmcid": "PMC9937462",
+            "pmid": "36800358"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Human biology",
+            "uri": "http://edamontology.org/topic_2815"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        }
+    ]
+}
diff --git a/data/tourmaline/tourmaline.biotools.json b/data/tourmaline/tourmaline.biotools.json
index cfb1ddb87b930..f498e4abeb9f2 100644
--- a/data/tourmaline/tourmaline.biotools.json
+++ b/data/tourmaline/tourmaline.biotools.json
@@ -1,17 +1,34 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2022-06-02T18:35:00.890528Z",
     "biotoolsCURIE": "biotools:tourmaline",
     "biotoolsID": "tourmaline",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "Tourmaline is an amplicon sequence processing workflow for Illumina sequence data that uses QIIME 2 and the software packages it wraps. Tourmaline manages commands, inputs, and outputs using the Snakemake workflow management system.",
     "editPermission": {
         "type": "private"
     },
     "homepage": "https://github.com/aomlomics/tourmaline",
-    "lastUpdate": "2022-12-09T23:42:50.683190Z",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-10T16:35:14.498312Z",
     "license": "BSD-3-Clause",
     "name": "Tourmaline",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
     "owner": "lukethompson",
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        },
         {
             "term": "Workflows",
             "uri": "http://edamontology.org/topic_0769"
diff --git a/data/toxric/toxric.biotools.json b/data/toxric/toxric.biotools.json
new file mode 100644
index 0000000000000..bdf08b2285535
--- /dev/null
+++ b/data/toxric/toxric.biotools.json
@@ -0,0 +1,135 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T01:43:42.708561Z",
+    "biotoolsCURIE": "biotools:toxric",
+    "biotoolsID": "toxric",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "boxc@bmi.ac.cn",
+            "name": "Xiaochen Bo",
+            "orcidid": "https://orcid.org/0000-0003-1911-7922",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "hes1224@163.com",
+            "name": "Song He",
+            "orcidid": "https://orcid.org/0000-0002-4136-6151",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Bowei Yan"
+        },
+        {
+            "name": "Lianlian Wu",
+            "orcidid": "https://orcid.org/0000-0002-9611-4488"
+        }
+    ],
+    "description": "A comprehensive database of toxicological data and benchmarks.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Chemical name (synonymous)",
+                        "uri": "http://edamontology.org/data_1001"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "SMILES string",
+                        "uri": "http://edamontology.org/data_2301"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://toxric.bioinforai.tech/",
+    "lastUpdate": "2023-01-25T01:43:42.711220Z",
+    "name": "TOXRIC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC1074",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.",
+                "authors": [
+                    {
+                        "name": "Bo X."
+                    },
+                    {
+                        "name": "Han J."
+                    },
+                    {
+                        "name": "He S."
+                    },
+                    {
+                        "name": "Li R."
+                    },
+                    {
+                        "name": "Wu L."
+                    },
+                    {
+                        "name": "Xiao J."
+                    },
+                    {
+                        "name": "Yan B."
+                    }
+                ],
+                "date": "2023-01-06T00:00:00Z",
+                "journal": "Nucleic acids research",
+                "title": "TOXRIC: a comprehensive database of toxicological data and benchmarks"
+            },
+            "pmcid": "PMC9825425",
+            "pmid": "36400569"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Drug discovery",
+            "uri": "http://edamontology.org/topic_3336"
+        },
+        {
+            "term": "Drug metabolism",
+            "uri": "http://edamontology.org/topic_3375"
+        },
+        {
+            "term": "Ecology",
+            "uri": "http://edamontology.org/topic_0610"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Toxicology",
+            "uri": "http://edamontology.org/topic_2840"
+        }
+    ]
+}
diff --git a/data/tps/tps.biotools.json b/data/tps/tps.biotools.json
index 85e1bd5055651..62d0a923fc03e 100644
--- a/data/tps/tps.biotools.json
+++ b/data/tps/tps.biotools.json
@@ -6,14 +6,25 @@
     "collectionID": [
         "PerMedCoE"
     ],
+    "confidence_flag": "tool",
     "cost": "Free of charge",
-    "description": "TPS is a tool for combining time series global phosphoproteomic data and protein-protein interaction networks to reconstruct the vast signaling pathways that control post-translational modifications.",
-    "download": [
+    "credit": [
+        {
+            "email": "gitter@biostat.wisc.edu",
+            "name": "Anthony Gitter",
+            "orcidid": "https://orcid.org/0000-0002-5324-9833"
+        },
+        {
+            "name": "Ali Sinan Köksal"
+        },
+        {
+            "name": "Jasmin Fisher"
+        },
         {
-            "type": "Source code",
-            "url": "https://github.com/koksal/tps"
+            "name": "Kirsten Beck"
         }
     ],
+    "description": "TPS is a tool for combining time series global phosphoproteomic data and protein-protein interaction networks to reconstruct the vast signaling pathways that control post-translational modifications.",
     "editPermission": {
         "type": "public"
     },
@@ -47,7 +58,7 @@
         "Python",
         "Scala"
     ],
-    "lastUpdate": "2022-12-09T23:45:43.254496Z",
+    "lastUpdate": "2023-01-10T16:26:59.255265Z",
     "license": "MIT",
     "name": "TPS",
     "operatingSystem": [
@@ -98,7 +109,7 @@
                         "name": "Wolf-Yadlin A."
                     }
                 ],
-                "citationCount": 14,
+                "citationCount": 15,
                 "date": "2018-09-25T00:00:00Z",
                 "journal": "Cell Reports",
                 "title": "Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data"
diff --git a/data/tpso-dbp/tpso-dbp.biotools.json b/data/tpso-dbp/tpso-dbp.biotools.json
new file mode 100644
index 0000000000000..442b559a7c21d
--- /dev/null
+++ b/data/tpso-dbp/tpso-dbp.biotools.json
@@ -0,0 +1,127 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T09:04:47.707503Z",
+    "biotoolsCURIE": "biotools:tpso-dbp",
+    "biotoolsID": "tpso-dbp",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Gui-Jun Zhang"
+        },
+        {
+            "name": "Wen-Wu Zeng"
+        },
+        {
+            "name": "Dong-Jun Yu",
+            "orcidid": "https://orcid.org/0000-0002-6786-8053"
+        },
+        {
+            "name": "Jun Hu",
+            "orcidid": "https://orcid.org/0000-0001-9202-7515"
+        }
+    ],
+    "description": "Improving DNA-Binding Protein Prediction Using Three-Part Sequence-Order Feature Extraction and a Deep Neural Network Algorithm.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "DNA-binding protein prediction",
+                    "uri": "http://edamontology.org/operation_3900"
+                },
+                {
+                    "term": "Feature extraction",
+                    "uri": "http://edamontology.org/operation_3937"
+                },
+                {
+                    "term": "Protein feature detection",
+                    "uri": "http://edamontology.org/operation_3092"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://jun-csbio.github.io/TPSO-DBP/",
+    "lastUpdate": "2023-03-17T09:04:47.715727Z",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/wwzll123/TPSO"
+        }
+    ],
+    "name": "TPSO-DBP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1021/ACS.JCIM.2C00943",
+            "metadata": {
+                "abstract": "Identification of the DNA-binding protein (DBP) helps dig out information embedded in the DNA-protein interaction, which is significant to understanding the mechanisms of DNA replication, transcription, and repair. Although existing computational methods for predicting the DBPs based on protein sequences have obtained great success, there is still room for improvement since the sequence-order information is not fully mined in these methods. In this study, a new three-part sequence-order feature extraction (called TPSO) strategy is developed to extract more discriminative information from protein sequences for predicting the DBPs. For each query protein, TPSO first divides its primary sequence features into N- and C-terminal fragments and then extracts the numerical pseudo features of three parts including the full sequence and these two fragments, respectively. Based on TPSO, a novel deep learning-based method, called TPSO-DBP, is proposed, which employs the sequence-based single-view features, the bidirectional long short-term memory (BiLSTM) and fully connected (FC) neural networks to learn the DBP prediction model. Empirical outcomes reveal that TPSO-DBP can achieve an accuracy of 87.01%, covering 85.30% of all DBPs, while achieving a Matthew’s correlation coefficient value (0.741) that is significantly higher than most existing state-of-the-art DBP prediction methods. Detailed data analyses have indicated that the advantages of TPSO-DBP lie in the utilization of TPSO, which helps extract more concealed prominent patterns, and the deep neural network framework composed of BiLSTM and FC that learns the nonlinear relationships between input features and DBPs. The standalone package and web server of TPSO-DBP are freely available at https://jun-csbio.github.io/TPSO-DBP/.",
+                "authors": [
+                    {
+                        "name": "Arif M."
+                    },
+                    {
+                        "name": "Hu J."
+                    },
+                    {
+                        "name": "Jia N.-X."
+                    },
+                    {
+                        "name": "Yu D.-J."
+                    },
+                    {
+                        "name": "Zeng W.-W."
+                    },
+                    {
+                        "name": "Zhang G.-J."
+                    }
+                ],
+                "date": "2023-02-13T00:00:00Z",
+                "journal": "Journal of Chemical Information and Modeling",
+                "title": "Improving DNA-Binding Protein Prediction Using Three-Part Sequence-Order Feature Extraction and a Deep Neural Network Algorithm"
+            },
+            "pmid": "36719781"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Proteins",
+            "uri": "http://edamontology.org/topic_0078"
+        }
+    ]
+}
diff --git a/data/tracespipe/tracespipe.biotools.json b/data/tracespipe/tracespipe.biotools.json
index 8dd41401a61ec..eada9a73e23d0 100644
--- a/data/tracespipe/tracespipe.biotools.json
+++ b/data/tracespipe/tracespipe.biotools.json
@@ -3,6 +3,7 @@
     "additionDate": "2020-07-21T16:42:49Z",
     "biotoolsCURIE": "biotools:tracespipe",
     "biotoolsID": "tracespipe",
+    "confidence_flag": "tool",
     "cost": "Free of charge",
     "description": "A hybrid pipeline for reconstruction and analysis of viral and host genomes at multi-organ level.",
     "editPermission": {
@@ -12,7 +13,7 @@
     "language": [
         "Shell"
     ],
-    "lastUpdate": "2022-12-09T23:47:27.419610Z",
+    "lastUpdate": "2023-01-10T16:21:34.602836Z",
     "license": "GPL-3.0",
     "maturity": "Emerging",
     "name": "TRACESPipe",
diff --git a/data/tractoinferno/tractoinferno.biotools.json b/data/tractoinferno/tractoinferno.biotools.json
new file mode 100644
index 0000000000000..0ee38040edb4f
--- /dev/null
+++ b/data/tractoinferno/tractoinferno.biotools.json
@@ -0,0 +1,138 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T01:37:41.121024Z",
+    "biotoolsCURIE": "biotools:tractoinferno",
+    "biotoolsID": "tractoinferno",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "philippe.poulin2@usherbrooke.ca",
+            "name": "Philippe Poulin",
+            "orcidid": "https://orcid.org/0000-0002-0116-4352",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Guillaume Theaud"
+        },
+        {
+            "name": "Pierre-Marc Jodoin"
+        },
+        {
+            "name": "Maxime Descoteaux",
+            "orcidid": "http://orcid.org/0000-0002-8191-2129"
+        }
+    ],
+    "description": "A large-scale, open-source, multi-site database for machine learning dMRI tractography.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/scil-vital/TractoInferno/",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-25T01:37:41.123791Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "http://github.com/ppoulin91/tractoinferno_compute_sh_flow"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "http://github.com/ppoulin91/tractoinferno_tracking_flow"
+        }
+    ],
+    "name": "TractoInferno",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41597-022-01833-1",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).TractoInferno is the world’s largest open-source multi-site tractography database, including both research- and clinical-like human acquisitions, aimed specifically at machine learning tractography approaches and related ML algorithms. It provides 284 samples acquired from 3 T scanners across 6 different sites. Available data includes T1-weighted images, single-shell diffusion MRI (dMRI) acquisitions, spherical harmonics fitted to the dMRI signal, fiber ODFs, and reference streamlines for 30 delineated bundles generated using 4 tractography algorithms, as well as masks needed to run tractography algorithms. Manual quality control was additionally performed at multiple steps of the pipeline. We showcase TractoInferno by benchmarking the learn2track algorithm and 5 variations of the same recurrent neural network architecture. Creating the TractoInferno database required approximately 20,000 CPU-hours of processing power, 200 man-hours of manual QC, 3,000 GPU-hours of training baseline models, and 4 Tb of storage, to produce a final database of 350 Gb. By providing a standardized training dataset and evaluation protocol, TractoInferno is an excellent tool to address common issues in machine learning tractography.",
+                "authors": [
+                    {
+                        "name": "Bore A."
+                    },
+                    {
+                        "name": "Descoteaux M."
+                    },
+                    {
+                        "name": "Guay S."
+                    },
+                    {
+                        "name": "Jodoin P.-M."
+                    },
+                    {
+                        "name": "Poulin P."
+                    },
+                    {
+                        "name": "Renauld E."
+                    },
+                    {
+                        "name": "Rheault F."
+                    },
+                    {
+                        "name": "St-Onge E."
+                    },
+                    {
+                        "name": "Theaud G."
+                    },
+                    {
+                        "name": "de Beaumont L."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Scientific Data",
+                "title": "TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography"
+            },
+            "pmcid": "PMC9700736",
+            "pmid": "36433966"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "MRI",
+            "uri": "http://edamontology.org/topic_3444"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/transaap/transaap.biotools.json b/data/transaap/transaap.biotools.json
new file mode 100644
index 0000000000000..f96e99c2f11d7
--- /dev/null
+++ b/data/transaap/transaap.biotools.json
@@ -0,0 +1,152 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T08:53:00.418311Z",
+    "biotoolsCURIE": "biotools:transaap",
+    "biotoolsID": "transaap",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Brendan Wilson-Mortier"
+        },
+        {
+            "name": "Ian T. Paulsen"
+        },
+        {
+            "name": "Sasha G. Tetu"
+        },
+        {
+            "name": "Liam D. H. Elbourne",
+            "orcidid": "https://orcid.org/0000-0002-9784-0959"
+        }
+    ],
+    "description": "an automated annotation pipeline for membrane transporter prediction in bacterial genomes.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "http://www.membranetransport.org/transportDB2/downloads/TransAAP_userguide.pdf"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Genus name",
+                        "uri": "http://edamontology.org/data_1870"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Species name",
+                        "uri": "http://edamontology.org/data_1045"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Strain name",
+                        "uri": "http://edamontology.org/data_1046"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Genome annotation",
+                    "uri": "http://edamontology.org/operation_0362"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://www.membranetransport.org/transportDB2/TransAAP_login.html",
+    "lastUpdate": "2023-03-17T08:53:00.422574Z",
+    "name": "TransAAP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1099/mgen.0.000927",
+            "metadata": {
+                "abstract": "Membrane transporters are a large group of proteins that span cell membranes and contribute to critical cell processes, including delivery of essential nutrients, ejection of waste products, and assisting the cell in sensing environmental conditions. Obtaining an accurate and specific annotation of the transporter proteins encoded by a micro-organism can provide details of its likely nutritional preferences and environmental niche(s), and identify novel transporters that could be utilized in small molecule production in industrial biotechnology. The Transporter Automated Annotation Pipeline (TransAAP) (http://www.membranetransport. org/transportDB2/TransAAP_login.html) is a fully automated web service for the prediction and annotation of membrane transport proteins in an organism from its genome sequence, by using comparisons with both curated databases such as the TCDB (Transporter Classification Database) and TDB, as well as selected Pfams and TIGRFAMs of transporter families and other methodologies. TransAAP was used to annotate transporter genes in the prokaryotic genomes in the National Center for Biotechnology Information (NCBI) RefSeq; these are presented in the transporter database TransportDB (http://www. membranetransport.org) website, which has a suite of data visualization and analysis tools. Creation and maintenance of a bioinformatic database specific for transporters in all genomic datasets is essential for microbiology research groups and the general research/biotechnology community to obtain a detailed picture of membrane transporter systems in various environments, as well as comprehensive information on specific membrane transport proteins.",
+                "authors": [
+                    {
+                        "name": "Elbourne L.D.H."
+                    },
+                    {
+                        "name": "Hassan K.A."
+                    },
+                    {
+                        "name": "Paulsen I.T."
+                    },
+                    {
+                        "name": "Ren Q."
+                    },
+                    {
+                        "name": "Tetu S.G."
+                    },
+                    {
+                        "name": "Wilson-Mortier B."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Microbial Genomics",
+                "title": "Transaap: An automated annotation pipeline for membrane transporter prediction in bacterial genomes"
+            },
+            "pmcid": "PMC9973855",
+            "pmid": "36748555"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Biotechnology",
+            "uri": "http://edamontology.org/topic_3297"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Microbiology",
+            "uri": "http://edamontology.org/topic_3301"
+        },
+        {
+            "term": "Nutritional science",
+            "uri": "http://edamontology.org/topic_3390"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/transdfl/transdfl.biotools.json b/data/transdfl/transdfl.biotools.json
new file mode 100644
index 0000000000000..32eca2c2e532b
--- /dev/null
+++ b/data/transdfl/transdfl.biotools.json
@@ -0,0 +1,86 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T10:07:17.679420Z",
+    "biotoolsCURIE": "biotools:transdfl",
+    "biotoolsID": "transdfl",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Bin Liu",
+            "orcidid": "http://orcid.org/0000-0002-8520-8374"
+        },
+        {
+            "name": "Yihe Pang",
+            "orcidid": "http://orcid.org/0000-0001-7338-5739"
+        }
+    ],
+    "description": "Identification of Disordered Flexible Linkers in Proteins by Transfer Learning.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Protein disorder prediction",
+                    "uri": "http://edamontology.org/operation_3904"
+                },
+                {
+                    "term": "Protein identification",
+                    "uri": "http://edamontology.org/operation_3767"
+                },
+                {
+                    "term": "Protein secondary structure prediction (coils)",
+                    "uri": "http://edamontology.org/operation_0470"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bliulab.net/TransDFL/",
+    "lastUpdate": "2023-02-26T10:07:17.682077Z",
+    "name": "TransDFL",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.gpb.2022.10.004",
+            "pmid": "36272675"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Protein disordered structure",
+            "uri": "http://edamontology.org/topic_3538"
+        },
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        }
+    ]
+}
diff --git a/data/transflow/transflow.biotools.json b/data/transflow/transflow.biotools.json
new file mode 100644
index 0000000000000..0df7c53763b97
--- /dev/null
+++ b/data/transflow/transflow.biotools.json
@@ -0,0 +1,143 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T14:38:28.803972Z",
+    "biotoolsCURIE": "biotools:transflow",
+    "biotoolsID": "transflow",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xmwang@cdc.zj.cn",
+            "name": "Xiaomeng Wang",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "zhwliu@cdc.zj.cn",
+            "name": "Zhengwei Liu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Junhang Pan"
+        },
+        {
+            "name": "Junshun Gao",
+            "orcidid": "https://orcid.org/0000-0001-9040-2289",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A Snakemake workflow for transmission analysis of Mycobacterium tuberculosis whole-genome sequencing data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/cvn001/transflow",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-10T14:38:28.808594Z",
+    "license": "GPL-3.0",
+    "name": "TransFlow",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC785",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Whole-genome sequencing (WGS) is increasingly used to aid the understanding of Mycobacterium tuberculosis (MTB) transmission. The epidemiological analysis of tuberculosis based on the WGS technique requires a diverse collection of bioinformatics tools. Effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts. RESULTS: Here, we present TransFlow (Transmission Workflow), a user-friendly, fast, efficient and comprehensive WGS-based transmission analysis pipeline. TransFlow combines some state-of-the-art tools to take transmission analysis from raw sequencing data, through quality control, sequence alignment and variant calling, into downstream transmission clustering, transmission network reconstruction and transmission risk factor inference, together with summary statistics and data visualization in a summary report. TransFlow relies on Snakemake and Conda to resolve dependencies among consecutive processing steps and can be easily adapted to any computation environment. AVAILABILITY AND IMPLEMENTATION: TransFlow is free available at https://github.com/cvn001/transflow. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Chen B."
+                    },
+                    {
+                        "name": "Gao J."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Liu Z."
+                    },
+                    {
+                        "name": "Lu Y."
+                    },
+                    {
+                        "name": "Pan J."
+                    },
+                    {
+                        "name": "Wang W."
+                    },
+                    {
+                        "name": "Wang X."
+                    },
+                    {
+                        "name": "Wu K."
+                    },
+                    {
+                        "name": "Wu Y."
+                    },
+                    {
+                        "name": "Zhang M."
+                    },
+                    {
+                        "name": "Zhu Y."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "TransFlow: a Snakemake workflow for transmission analysis of Mycobacterium tuberculosis whole-genome sequencing data"
+            },
+            "pmcid": "PMC9825751",
+            "pmid": "36469333"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Public health and epidemiology",
+            "uri": "http://edamontology.org/topic_3305"
+        },
+        {
+            "term": "Whole genome sequencing",
+            "uri": "http://edamontology.org/topic_3673"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/transposable_element_finder/transposable_element_finder.biotools.json b/data/transposable_element_finder/transposable_element_finder.biotools.json
new file mode 100644
index 0000000000000..828d4d7c4cffd
--- /dev/null
+++ b/data/transposable_element_finder/transposable_element_finder.biotools.json
@@ -0,0 +1,109 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T14:08:26.197963Z",
+    "biotoolsCURIE": "biotools:transposable_element_finder",
+    "biotoolsID": "transposable_element_finder",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "miyao@affrc.go.jp",
+            "name": "Akio Miyao",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Utako Yamanouchi"
+        }
+    ],
+    "description": "Finding active transposable elements from next generation sequencing data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Recombination detection",
+                    "uri": "http://edamontology.org/operation_0451"
+                },
+                {
+                    "term": "Sorting",
+                    "uri": "http://edamontology.org/operation_3802"
+                },
+                {
+                    "term": "Transposon prediction",
+                    "uri": "http://edamontology.org/operation_0427"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/akiomiyao/tef",
+    "language": [
+        "Perl"
+    ],
+    "lastUpdate": "2023-01-25T14:08:26.201083Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://akiomiyao.github.io/tef/"
+        }
+    ],
+    "name": "TEF",
+    "operatingSystem": [
+        "Linux"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05011-3",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Detection of newly transposed events by transposable elements (TEs) from next generation sequence (NGS) data is difficult, due to their multiple distribution sites over the genome containing older TEs. The previously reported Transposon Insertion Finder (TIF) detects TE transpositions on the reference genome from NGS short reads using end sequences of target TE. TIF requires the sequence of target TE and is not able to detect transpositions for TEs with an unknown sequence. Result: The new algorithm Transposable Element Finder (TEF) enables the detection of TE transpositions, even for TEs with an unknown sequence. TEF is a finding tool of transposed TEs, in contrast to TIF as a detection tool of transposed sites for TEs with a known sequence. The transposition event is often accompanied with a target site duplication (TSD). Focusing on TSD, two algorithms to detect both ends of TE, TSDs and target sites are reported here. One is based on the grouping with TSDs and direct comparison of k-mers from NGS without similarity search. The other is based on the junction mapping of TE end sequence candidates. Both methods succeed to detect both ends and TSDs of known active TEs in several tests with rice, Arabidopsis and Drosophila data and discover several new TEs in new locations. PCR confirmed the detected transpositions of TEs in several test cases in rice. Conclusions: TEF detects transposed TEs with TSDs as a result of TE transposition, sequences of both ends and their inserted positions of transposed TEs by direct comparison of NGS data between two samples. Genotypes of transpositions are verified by counting of junctions of head and tail, and non-insertion sequences in NGS reads. TEF is easy to run and independent of any TE library, which makes it useful to detect insertions from unknown TEs bypassed by common TE annotation pipelines.",
+                "authors": [
+                    {
+                        "name": "Miyao A."
+                    },
+                    {
+                        "name": "Yamanouchi U."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "Transposable element finder (TEF): finding active transposable elements from next generation sequencing data"
+            },
+            "pmcid": "PMC9682801",
+            "pmid": "36418944"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Mobile genetic elements",
+            "uri": "http://edamontology.org/topic_0798"
+        },
+        {
+            "term": "PCR experiment",
+            "uri": "http://edamontology.org/topic_3519"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/transvod/transvod.biotools.json b/data/transvod/transvod.biotools.json
new file mode 100644
index 0000000000000..93617d8461c13
--- /dev/null
+++ b/data/transvod/transvod.biotools.json
@@ -0,0 +1,108 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T01:10:21.044022Z",
+    "biotoolsCURIE": "biotools:transvod",
+    "biotoolsID": "transvod",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Dacheng Tao",
+            "orcidid": "https://orcid.org/0000-0001-7225-5449"
+        },
+        {
+            "name": "Lizhuang Ma",
+            "orcidid": "https://orcid.org/0000-0003-1653-4341"
+        },
+        {
+            "name": "Qianyu Zhou",
+            "orcidid": "https://orcid.org/0000-0002-5331-050X"
+        },
+        {
+            "name": "Xiangtai Li",
+            "orcidid": "https://orcid.org/0000-0002-0550-8247"
+        }
+    ],
+    "description": "End-to-End Video Object Detection With Spatial-Temporal Transformers.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Aggregation",
+                    "uri": "http://edamontology.org/operation_3436"
+                },
+                {
+                    "term": "Backbone modelling",
+                    "uri": "http://edamontology.org/operation_0479"
+                },
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/SJTU-LuHe/TransVOD",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T01:10:21.046555Z",
+    "license": "Apache-2.0",
+    "name": "TransVOD",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1109/TPAMI.2022.3223955",
+            "metadata": {
+                "abstract": "IEEEDetection Transformer (DETR) and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous complex hand-crafted detectors. However, their performance on Video Object Detection (VOD) has <italic>not</italic> been well explored. In this paper, we present <bold>TransVOD</bold>, the <italic>first end-to-end</italic> video object detection system based on simple yet effective spatial-temporal Transformer architectures. The first goal of this paper is to streamline the pipeline of current VOD, effectively removing the need for many hand-crafted components for feature aggregation, <italic>e.g.,</italic> optical flow model, relation networks. Besides, benefited from the object query design in DETR, our method does not need post-processing methods such as Seq-NMS. In particular, we present a temporal Transformer to aggregate both the spatial object queries and the feature memories of each frame. Our temporal transformer consists of two components: Temporal Query Encoder (TQE) to fuse object queries, and Temporal Deformable Transformer Decoder (TDTD) to obtain current frame detection results. These designs boost the strong baseline deformable DETR by a significant margin (3 &#x0025;-4 &#x0025; mAP) on the ImageNet VID dataset. TransVOD yields comparable performances on the benchmark of ImageNet VID. Then, we present two improved versions of TransVOD including TransVOD++ and TransVOD Lite. The former fuses object-level information into object query via dynamic convolution while the latter models the entire video clips as the output to speed up the inference time. We give detailed analysis of all three models in the experiment part. In particular, our proposed TransVOD++ sets a new state-of-the-art record in terms of accuracy on ImageNet VID with 90.0 &#x0025; mAP. Our proposed TransVOD Lite also achieves the best speed and accuracy trade-off with 83.7 &#x0025; mAP while running at around 30 FPS on a single V100 GPU device. Code and models are available at <uri>https://github.com/SJTU-LuHe/TransVOD</uri>.",
+                "authors": [
+                    {
+                        "name": "Cheng G."
+                    },
+                    {
+                        "name": "He L."
+                    },
+                    {
+                        "name": "Li X."
+                    },
+                    {
+                        "name": "Ma L."
+                    },
+                    {
+                        "name": "Tao D."
+                    },
+                    {
+                        "name": "Tong Y."
+                    },
+                    {
+                        "name": "Yang Y."
+                    },
+                    {
+                        "name": "Zhou Q."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "IEEE Transactions on Pattern Analysis and Machine Intelligence",
+                "title": "TransVOD: End-to-End Video Object Detection With Spatial-Temporal Transformers"
+            },
+            "pmid": "36417746"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "RNA immunoprecipitation",
+            "uri": "http://edamontology.org/topic_3794"
+        }
+    ]
+}
diff --git a/data/treat_web/treat_web.biotools.json b/data/treat_web/treat_web.biotools.json
new file mode 100644
index 0000000000000..142e90baea990
--- /dev/null
+++ b/data/treat_web/treat_web.biotools.json
@@ -0,0 +1,142 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T01:02:00.849234Z",
+    "biotoolsCURIE": "biotools:treat_web",
+    "biotoolsID": "treat_web",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "biozy@ict.ac.cn",
+            "name": "Yi Zhao",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "budechao@ict.ac.cn",
+            "name": "Dechao Bu",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "crs@ibp.ac.cn",
+            "name": "Runsheng Chen",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Yufan Luo"
+        }
+    ],
+    "description": "Therapeutic RNAs exploration inspired by artificial intelligence technology.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Backbone modelling",
+                    "uri": "http://edamontology.org/operation_0479"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://rna.org.cn/treat",
+    "lastUpdate": "2023-01-25T01:02:00.851786Z",
+    "name": "TREAT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2022.10.011",
+            "metadata": {
+                "abstract": "© 2022 The AuthorsRecent advances in RNA engineering have enabled the development of RNA-based therapeutics for a broad spectrum of applications. Developing RNA therapeutics start with targeted RNA screening and move to the drug design and optimization. However, existing target screening tools ignore noncoding RNAs and their disease-relevant regulatory relationships. And designing therapeutic RNAs encounters high computational complexity of multi-objective optimization to overcome the immunogenicity, instability and inefficient translational production. To unlock the therapeutic potential of noncoding RNAs and enable one-stop screening and design of therapeutic RNAs, we have built the platform TREAT. It incorporates 43,087,953 regulatory relationships between coding and noncoding genes from 81 biological networks under different physiological conditions. TREAT introduces graph representation learning with Random Walk Diffusions to perform disease-relevant target screening, in addition to the commonly utilized Topological Degree and PageRank algorithms. Design and optimization of large RNAs or interfering RNAs are both available. To reduce the computational complexity of multi-objective optimization for large RNA, we stratified the features into local and global features. The local features are evaluated on the fixed-length or dynamic-length local bins, whereas the latter are inspired by AI language models of protein sequence. Then the global assessment is performed on refined candidates, thus reducing the enormous search space. Overall, TREAT is a one-stop platform for the screening and designing of therapeutic RNAs, with particular attention to noncoding RNAs and cutting-edge AI technology embedded, leading the progress of innovative therapeutics for challenging diseases. TREAT is freely accessible at https://rna.org.cn/treat.",
+                "authors": [
+                    {
+                        "name": "Bu D."
+                    },
+                    {
+                        "name": "Chen R."
+                    },
+                    {
+                        "name": "He Z."
+                    },
+                    {
+                        "name": "Huo P."
+                    },
+                    {
+                        "name": "Jiaxin Q."
+                    },
+                    {
+                        "name": "Liu L."
+                    },
+                    {
+                        "name": "Luo Y."
+                    },
+                    {
+                        "name": "Wang Z."
+                    },
+                    {
+                        "name": "Wu Y."
+                    },
+                    {
+                        "name": "Zhang D."
+                    },
+                    {
+                        "name": "Zhang S."
+                    },
+                    {
+                        "name": "Zhao L."
+                    },
+                    {
+                        "name": "Zhao Y."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology"
+            },
+            "pmcid": "PMC9589171",
+            "pmid": "36320935"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Immunogenetics",
+            "uri": "http://edamontology.org/topic_3930"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        }
+    ]
+}
diff --git a/data/trinetx/trinetx.biotools.json b/data/trinetx/trinetx.biotools.json
new file mode 100644
index 0000000000000..e6ebd7594cff3
--- /dev/null
+++ b/data/trinetx/trinetx.biotools.json
@@ -0,0 +1,49 @@
+{
+    "additionDate": "2023-01-31T06:36:35.605653Z",
+    "biotoolsCURIE": "biotools:trinetx",
+    "biotoolsID": "trinetx",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "email": "join@trinetx.com",
+            "url": "https://trinetx.com/about-trinetx/contact/"
+        }
+    ],
+    "description": "Welcome to the world’s largest, living ecosystem of real-world data and evidence for the life sciences and healthcare industries. Global data, for global health.",
+    "documentation": [
+        {
+            "type": [
+                "General"
+            ],
+            "url": "https://trinetx.com/real-world-resources/events/"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://trinetx.com/",
+    "lastUpdate": "2023-02-01T13:12:16.828971Z",
+    "license": "Other",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://trinetx.com/"
+        }
+    ],
+    "name": "TriNetX",
+    "owner": "iacs-biocomputacion"
+}
diff --git a/data/tssnote-cyaprombert/tssnote-cyaprombert.biotools.json b/data/tssnote-cyaprombert/tssnote-cyaprombert.biotools.json
new file mode 100644
index 0000000000000..2e08dc2f3af31
--- /dev/null
+++ b/data/tssnote-cyaprombert/tssnote-cyaprombert.biotools.json
@@ -0,0 +1,105 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T14:31:08.029654Z",
+    "biotoolsCURIE": "biotools:tssnote-cyaprombert",
+    "biotoolsID": "tssnote-cyaprombert",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "eunylee@khu.ac.kr",
+            "name": "Eun Yeol Lee",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Dung Hoang Anh Mai"
+        },
+        {
+            "name": "Linh Thanh Nguyen"
+        }
+    ],
+    "description": "A repo containing supplemental data for TSSNote-CyaPromBERT an integrated pipeline for constructing curated promoter prediction tools using SOTA BERT through public dRNA-seq datasets.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Operon prediction",
+                    "uri": "http://edamontology.org/operation_0435"
+                },
+                {
+                    "term": "Promoter prediction",
+                    "uri": "http://edamontology.org/operation_0440"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/hanepira/TSSnote-CyaPromBert",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-10T14:31:08.032330Z",
+    "license": "Not licensed",
+    "name": "TSSNote-CyaPromBERT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FGENE.2022.1067562",
+            "metadata": {
+                "abstract": "Copyright © 2022 Mai, Nguyen and Lee.Since the introduction of the first transformer model with a unique self-attention mechanism, natural language processing (NLP) models have attained state-of-the-art (SOTA) performance on various tasks. As DNA is the blueprint of life, it can be viewed as an unusual language, with its characteristic lexicon and grammar. Therefore, NLP models may provide insights into the meaning of the sequential structure of DNA. In the current study, we employed and compared the performance of popular SOTA NLP models (i.e., XLNET, BERT, and a variant DNABERT trained on the human genome) to predict and analyze the promoters in freshwater cyanobacterium Synechocystis sp. PCC 6803 and the fastest growing cyanobacterium Synechococcus elongatus sp. UTEX 2973. These freshwater cyanobacteria are promising hosts for phototrophically producing value-added compounds from CO2. Through a custom pipeline, promoters and non-promoters from Synechococcus elongatus sp. UTEX 2973 were used to train the model. The trained model achieved an AUROC score of 0.97 and F1 score of 0.92. During cross-validation with promoters from Synechocystis sp. PCC 6803, the model achieved an AUROC score of 0.96 and F1 score of 0.91. To increase accessibility, we developed an integrated platform (TSSNote-CyaPromBERT) to facilitate large dataset extraction, model training, and promoter prediction from public dRNA-seq datasets. Furthermore, various visualization tools have been incorporated to address the “black box” issue of deep learning and feature analysis. The learning transfer ability of large language models may help identify and analyze promoter regions for newly isolated strains with similar lineages.",
+                "authors": [
+                    {
+                        "name": "Lee E.Y."
+                    },
+                    {
+                        "name": "Mai D.H.A."
+                    },
+                    {
+                        "name": "Nguyen L.T."
+                    }
+                ],
+                "date": "2022-11-29T00:00:00Z",
+                "journal": "Frontiers in Genetics",
+                "title": "TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT"
+            },
+            "pmcid": "PMC9745317",
+            "pmid": "36523764"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Gene structure",
+            "uri": "http://edamontology.org/topic_0114"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/tvir/tvir.biotools.json b/data/tvir/tvir.biotools.json
new file mode 100644
index 0000000000000..ce8ec992115d5
--- /dev/null
+++ b/data/tvir/tvir.biotools.json
@@ -0,0 +1,137 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T14:23:58.410030Z",
+    "biotoolsCURIE": "biotools:tvir",
+    "biotoolsID": "tvir",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "songxm@ncst.edu.cn",
+            "name": "Xiaoming Song",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Fulei Nie"
+        },
+        {
+            "name": "Tong Yu"
+        },
+        {
+            "name": "Xiao Ma"
+        }
+    ],
+    "description": "A comprehensive vegetable information resource database for comparative and functional genomic studies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene ID",
+                        "uri": "http://edamontology.org/data_2295"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "PCR primer design",
+                    "uri": "http://edamontology.org/operation_0308"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://tvir.bio2db.com",
+    "lastUpdate": "2023-02-10T14:23:58.412649Z",
+    "name": "TVIR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/HR/UHAC213",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press on behalf of Nanjing Agricultural University.Vegetables are an indispensable part of the daily diet of humans. Therefore, it is vital to systematically study the genomic data of vegetables and build a platform for data sharing and analysis. In this study, a comprehensive platform for vegetables with a user-friendly Web interface - The Vegetable Information Resource (TVIR, http://tvir.bio2db.com) - was built based on the genomes of 59 vegetables. TVIR database contains numerous important functional genes, including 5215 auxin genes, 2437 anthocyanin genes, 15 002 flowering genes, 79 830 resistance genes, and 2639 glucosinolate genes of 59 vegetables. In addition, 2597 N6-methyladenosine (m6A) genes were identified, including 513 writers, 1058 erasers, and 1026 readers. A total of 2 101 501 specific clustered regularly interspaced short palindromic repeat (CRISPR) guide sequences and 17 377 miRNAs were detected and deposited in TVIR database. Information on gene synteny, duplication, and orthologs is also provided for 59 vegetable species. TVIR database contains 2 346 850 gene annotations by the Swiss-Prot, TrEMBL, Gene Ontology (GO), Pfam, and Non-redundant (Nr) databases. Synteny, Primer Design, Blast, and JBrowse tools are provided to facilitate users in conducting comparative genomic analyses. This is the first large-scale collection of vegetable genomic data and bioinformatic analysis. All genome and gene sequences, annotations, and bioinformatic results can be easily downloaded from TVIR. Furthermore, transcriptome data of 98 vegetables have been collected and collated, and can be searched by species, tissues, or different growth stages. TVIR is expected to become a key hub for vegetable research globally. The database will be updated with newly assembled vegetable genomes and comparative genomic studies in the future.",
+                "authors": [
+                    {
+                        "name": "Cao R."
+                    },
+                    {
+                        "name": "Feng X."
+                    },
+                    {
+                        "name": "Liu Z."
+                    },
+                    {
+                        "name": "Ma X."
+                    },
+                    {
+                        "name": "Nie F."
+                    },
+                    {
+                        "name": "Ren J."
+                    },
+                    {
+                        "name": "Song X."
+                    },
+                    {
+                        "name": "Wang Z."
+                    },
+                    {
+                        "name": "Yu T."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Horticulture Research",
+                "title": "TVIR: a comprehensive vegetable information resource database for comparative and functional genomic studies"
+            },
+            "pmcid": "PMC9719039",
+            "pmid": "36483087"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Probes and primers",
+            "uri": "http://edamontology.org/topic_0632"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/twineqtl/twineqtl.biotools.json b/data/twineqtl/twineqtl.biotools.json
new file mode 100644
index 0000000000000..975f398fb2747
--- /dev/null
+++ b/data/twineqtl/twineqtl.biotools.json
@@ -0,0 +1,142 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-20T01:04:26.604216Z",
+    "biotoolsCURIE": "biotools:twineqtl",
+    "biotoolsID": "twineqtl",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "fzou@bios.unc.edu",
+            "name": "Fei Zou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Andrey A. Shabalin"
+        },
+        {
+            "name": "Zhaoyu Yin"
+        },
+        {
+            "name": "Kai Xia",
+            "orcidid": "http://orcid.org/0000-0003-1326-0891"
+        },
+        {
+            "name": "Wonil Chung",
+            "orcidid": "http://orcid.org/0000-0002-5766-6247"
+        }
+    ],
+    "description": "Ultra Fast and Powerful Association Analysis for eQTL and GWAS in Twin Studies.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Collapsing methods",
+                    "uri": "http://edamontology.org/operation_3791"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Imputation",
+                    "uri": "http://edamontology.org/operation_3557"
+                },
+                {
+                    "term": "Regression analysis",
+                    "uri": "http://edamontology.org/operation_3659"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/andreyshabalin/TwinEQTL",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-20T01:04:26.606629Z",
+    "license": "Not licensed",
+    "name": "TwinEQTL",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/genetics/iyac088",
+            "metadata": {
+                "abstract": "© 2022 The Author(s). Published by Oxford University Press on behalf of Genetics Society of America. All rights reserved.We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model for twin genome-wide association study data. Instead of analyzing all twin samples together with linear mixed-effects model, TwinEQTL first splits twin samples into 2 independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the 2 nonindependent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between 2 dependent test statistics at each single-nucleotide polymorphism is independent of its minor allele frequency. Thus, the correlation is constant across all single-nucleotide polymorphisms. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared with the gold-standard linear mixed-effects models. To accommodate expression quantitative loci analysis with twin subjects, we further implement TwinEQTL into an R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for genome-wide association study and expression quantitative loci analysis with twin samples.",
+                "authors": [
+                    {
+                        "name": "Chung W."
+                    },
+                    {
+                        "name": "Gilmore J.H."
+                    },
+                    {
+                        "name": "Santelli R.C."
+                    },
+                    {
+                        "name": "Shabalin A.A."
+                    },
+                    {
+                        "name": "Styner M."
+                    },
+                    {
+                        "name": "Sullivan P.F."
+                    },
+                    {
+                        "name": "Wright F.A."
+                    },
+                    {
+                        "name": "Xia K."
+                    },
+                    {
+                        "name": "Yin Z."
+                    },
+                    {
+                        "name": "Zou F."
+                    }
+                ],
+                "date": "2022-08-01T00:00:00Z",
+                "journal": "Genetics",
+                "title": "TwinEQTL: ultrafast and powerful association analysis for eQTL and GWAS in twin studies"
+            },
+            "pmcid": "PMC9339336",
+            "pmid": "35689615"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "GWAS study",
+            "uri": "http://edamontology.org/topic_3517"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "Microarray experiment",
+            "uri": "http://edamontology.org/topic_3518"
+        }
+    ]
+}
diff --git a/data/twobitinfo/twobitinfo.biotools.json b/data/twobitinfo/twobitinfo.biotools.json
index 3f1e3056c449f..cda7c2500b324 100644
--- a/data/twobitinfo/twobitinfo.biotools.json
+++ b/data/twobitinfo/twobitinfo.biotools.json
@@ -1,16 +1,41 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2021-04-22T19:54:37Z",
     "biotoolsCURIE": "biotools:twobitinfo",
     "biotoolsID": "twobitinfo",
     "collectionID": [
         "ucsc-utilities"
     ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "get information about sequences in a .2bit file",
     "editPermission": {
         "type": "private"
     },
     "homepage": "http://hgdownload.cse.ucsc.edu/admin/exe/",
-    "lastUpdate": "2021-04-22T19:57:50Z",
+    "lastUpdate": "2023-01-10T16:14:50.457425Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/ucscGenomeBrowser/kent/tree/master/src/utils/twoBitInfo"
+        }
+    ],
     "name": "twobitinfo",
-    "owner": "leipzig"
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "leipzig",
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Sequence analysis",
+            "uri": "http://edamontology.org/topic_0080"
+        }
+    ]
 }
diff --git a/data/ugdr/ugdr.biotools.json b/data/ugdr/ugdr.biotools.json
new file mode 100644
index 0000000000000..c5b3ac09560c4
--- /dev/null
+++ b/data/ugdr/ugdr.biotools.json
@@ -0,0 +1,99 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T12:59:18.259510Z",
+    "biotoolsCURIE": "biotools:ugdr",
+    "biotoolsID": "ugdr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "abedrat@mcw.edu",
+            "name": "Amina Bedrat",
+            "typeEntity": "Person"
+        }
+    ],
+    "description": "A generic pipeline to detect recombined regions in polyploid and complex hybrid yeast genomes.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genome indexing",
+                    "uri": "http://edamontology.org/operation_3211"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Recombination detection",
+                    "uri": "http://edamontology.org/operation_0451"
+                },
+                {
+                    "term": "SNP detection",
+                    "uri": "http://edamontology.org/operation_0484"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/AnimaTardeb/Meiogenix-UGDR",
+    "language": [
+        "Python",
+        "R",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-10T12:59:18.261954Z",
+    "license": "GPL-3.0",
+    "name": "UGDR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05113-Y",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Motivation: In eukaryotes, homologous recombination between the parental genomes frequently occurs during the evolutionary conserved process of meiosis, generating the genetic diversity transmitted by the gametes. The genome-wide determination of the frequency and location of the recombination events can now be efficiently performed by genotyping the offspring’s polymorphic markers. However, genotyping recombination in complex hybrid genomes with existing methods remains challenging because of their strain and ploidy specificity and the degree of diversity and complexity of the parental genomes, especially in > 2 n polyploids. Results: We present UGDR, a pipeline to genotype the polymorphisms of complex hybrid yeast genomes. It is based on optimal mapping strategies of NGS reads, comparative analyses of the allelic ratio variation and read depth coverage. We tested the UGDR pipeline with sequencing reads from recombined hybrid diploid yeast strains and various clinical strains exhibiting different degrees of ploidy. UGDR allows to plot the markers distribution and recombination profile per chromosome. Conclusion: UGDR detects and plots recombination events in haploids and polyploid yeasts, which facilitates the discovery and understanding of the yeast genetic recombination map and identify new out-performing recombinants.",
+                "authors": [
+                    {
+                        "name": "Bedrat A."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "UGDR: a generic pipeline to detect recombined regions in polyploid and complex hybrid yeast genomes"
+            },
+            "pmcid": "PMC9773435",
+            "pmid": "36544090"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "DNA replication and recombination",
+            "uri": "http://edamontology.org/topic_3127"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/ultraliser/ultraliser.biotools.json b/data/ultraliser/ultraliser.biotools.json
new file mode 100644
index 0000000000000..4da47818350ed
--- /dev/null
+++ b/data/ultraliser/ultraliser.biotools.json
@@ -0,0 +1,101 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T00:50:44.329594Z",
+    "biotoolsCURIE": "biotools:ultraliser",
+    "biotoolsID": "ultraliser",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "marwan.abdellah@epfl.ch",
+            "name": "Marwan Abdellah",
+            "orcidid": "https://orcid.org/0000-0002-7549-9657",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "felix.schuermann@epfl.ch",
+            "name": "Felix Schürmann",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Henry Markram"
+        },
+        {
+            "name": "Juan José García Cantero"
+        }
+    ],
+    "description": "A framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://github.com/BlueBrain/Ultraliser/wiki"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Modelling and simulation",
+                    "uri": "http://edamontology.org/operation_2426"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/BlueBrain/Ultraliser",
+    "language": [
+        "C++"
+    ],
+    "lastUpdate": "2023-01-25T00:50:44.332131Z",
+    "license": "GPL-3.0",
+    "name": "Ultraliser",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC491",
+            "pmid": "36434788"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Anatomy",
+            "uri": "http://edamontology.org/topic_3067"
+        },
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Mathematics",
+            "uri": "http://edamontology.org/topic_3315"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        }
+    ]
+}
diff --git a/data/umic/umic.biotools.json b/data/umic/umic.biotools.json
index f6b724d1be7a9..8177fcb932569 100644
--- a/data/umic/umic.biotools.json
+++ b/data/umic/umic.biotools.json
@@ -3,6 +3,7 @@
     "additionDate": "2021-05-05T19:18:02Z",
     "biotoolsCURIE": "biotools:umic",
     "biotoolsID": "umic",
+    "confidence_flag": "tool",
     "cost": "Free of charge",
     "credit": [
         {
@@ -13,6 +14,15 @@
             "typeRole": [
                 "Primary contact"
             ]
+        },
+        {
+            "name": "Anastasia Chatzidimitriou"
+        },
+        {
+            "name": "Maria Christina Maniou"
+        },
+        {
+            "name": "Maria Tsagiopoulou"
         }
     ],
     "description": "UMIc is an alignment free framework serving as a pre-processing step of FASTQ files for deduplication and correction of reads building consensus sequences from each UMI. The tool takes into account the frequency and the Phred quality of nucleotides and the distances between the UMIs and the actual sequences, and produces FASTQ files that contain the corrected sequences (without the UMI) and their quality.",
@@ -44,7 +54,7 @@
     "language": [
         "R"
     ],
-    "lastUpdate": "2022-12-09T23:49:58.250735Z",
+    "lastUpdate": "2023-01-10T15:47:28.890180Z",
     "license": "MIT",
     "maturity": "Emerging",
     "name": "UMIc",
@@ -83,15 +93,21 @@
                         "name": "Tsagiopoulou M."
                     }
                 ],
+                "citationCount": 1,
                 "date": "2021-05-28T00:00:00Z",
                 "journal": "Frontiers in Genetics",
                 "title": "UMIc: A Preprocessing Method for UMI Deduplication and Reads Correction"
             },
+            "pmcid": "PMC8193862",
+            "pmid": "34122513",
             "type": [
                 "Method"
             ]
         }
     ],
+    "toolType": [
+        "Library"
+    ],
     "version": [
         "1.0"
     ]
diff --git a/data/uncertainsci/uncertainsci.biotools.json b/data/uncertainsci/uncertainsci.biotools.json
new file mode 100644
index 0000000000000..feb872d1fb353
--- /dev/null
+++ b/data/uncertainsci/uncertainsci.biotools.json
@@ -0,0 +1,138 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T12:56:31.884351Z",
+    "biotoolsCURIE": "biotools:uncertainsci",
+    "biotoolsID": "uncertainsci",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "akil@sci.utah.edu",
+            "name": "Akil Narayan",
+            "orcidid": "https://orcid.org/0000-0002-5914-4207",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Akil Narayan",
+            "orcidid": "https://orcid.org/0000-0002-5914-4207"
+        },
+        {
+            "name": "Jess Tate",
+            "orcidid": "https://orcid.org/0000-0002-2934-1453"
+        },
+        {
+            "name": "Rob S. MacLeod",
+            "orcidid": "https://orcid.org/0000-0002-0000-0356"
+        },
+        {
+            "name": "Zexin Liu",
+            "orcidid": "https://orcid.org/0000-0003-3409-5709"
+        }
+    ],
+    "description": "Uncertainty quantification for computational models in biomedicine and bioengineering.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://uncertainsci.readthedocs.io/en/latest/index.html"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Modelling and simulation",
+                    "uri": "http://edamontology.org/operation_2426"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/SCIInstitute/UncertainSCI",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-10T12:56:31.887050Z",
+    "license": "MIT",
+    "name": "UncertainSCI",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106407",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdBackground: Computational biomedical simulations frequently contain parameters that model physical features, material coefficients, and physiological effects, whose values are typically assumed known a priori. Understanding the effect of variability in those assumed values is currently a topic of great interest. A general-purpose software tool that quantifies how variation in these parameters affects model outputs is not broadly available in biomedicine. For this reason, we developed the ‘UncertainSCI’ uncertainty quantification software suite to facilitate analysis of uncertainty due to parametric variability. Methods: We developed and distributed a new open-source Python-based software tool, UncertainSCI, which employs advanced parameter sampling techniques to build polynomial chaos (PC) emulators that can be used to predict model outputs for general parameter values. Uncertainty of model outputs is studied by modeling parameters as random variables, and model output statistics and sensitivities are then easily computed from the emulator. Our approaches utilize modern, near-optimal techniques for sampling and PC construction based on weighted Fekete points constructed by subsampling from a suitably randomized candidate set. Results: Concentrating on two test cases—modeling bioelectric potentials in the heart and electric stimulation in the brain—we illustrate the use of UncertainSCI to estimate variability, statistics, and sensitivities associated with multiple parameters in these models. Conclusion: UncertainSCI is a powerful yet lightweight tool enabling sophisticated probing of parametric variability and uncertainty in biomedical simulations. Its non-intrusive pipeline allows users to leverage existing software libraries and suites to accurately ascertain parametric uncertainty in a variety of applications.",
+                "authors": [
+                    {
+                        "name": "Bergquist J.A."
+                    },
+                    {
+                        "name": "Brooks D."
+                    },
+                    {
+                        "name": "Charlebois C."
+                    },
+                    {
+                        "name": "Liu Z."
+                    },
+                    {
+                        "name": "MacLeod R.S."
+                    },
+                    {
+                        "name": "Narayan A."
+                    },
+                    {
+                        "name": "Rampersad S."
+                    },
+                    {
+                        "name": "Rupp L."
+                    },
+                    {
+                        "name": "Tate J."
+                    },
+                    {
+                        "name": "White D."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "UncertainSCI: Uncertainty quantification for computational models in biomedicine and bioengineering"
+            },
+            "pmcid": "PMC9812870",
+            "pmid": "36521358"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Bioengineering",
+            "uri": "http://edamontology.org/topic_3398"
+        },
+        {
+            "term": "Physics",
+            "uri": "http://edamontology.org/topic_3318"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/unidoc/unidoc.biotools.json b/data/unidoc/unidoc.biotools.json
new file mode 100644
index 0000000000000..dcbc181075833
--- /dev/null
+++ b/data/unidoc/unidoc.biotools.json
@@ -0,0 +1,127 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T08:40:50.204968Z",
+    "biotoolsCURIE": "biotools:unidoc",
+    "biotoolsID": "unidoc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "yangjy@sdu.edu.cn",
+            "name": "Jianyi Yang",
+            "orcidid": "https://orcid.org/0000-0003-2912-7737",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Hong Su"
+        },
+        {
+            "name": "Kun Zhu"
+        },
+        {
+            "name": "Zhenling Peng",
+            "orcidid": "https://orcid.org/0000-0003-0303-6693"
+        }
+    ],
+    "description": "A unified approach to protein domain parsing with inter-residue distance matrix.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Protein sequence",
+                        "uri": "http://edamontology.org/data_2976"
+                    },
+                    "format": [
+                        {
+                            "term": "FASTA",
+                            "uri": "http://edamontology.org/format_1929"
+                        },
+                        {
+                            "term": "PDB",
+                            "uri": "http://edamontology.org/format_1476"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                },
+                {
+                    "term": "Protein structure prediction",
+                    "uri": "http://edamontology.org/operation_0474"
+                },
+                {
+                    "term": "Residue contact prediction",
+                    "uri": "http://edamontology.org/operation_0272"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://yanglab.nankai.edu.cn/UniDoc/",
+    "lastUpdate": "2023-03-17T08:40:50.210853Z",
+    "name": "UniDoc",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD070",
+            "metadata": {
+                "abstract": "MOTIVATION: It is fundamental to cut multi-domain proteins into individual domains, for precise domain-based structural and functional studies. In the past, sequence-based and structure-based domain parsing was carried out independently with different methodologies. The recent progress in deep learning-based protein structure prediction provides the opportunity to unify sequence-based and structure-based domain parsing. RESULTS: Based on the inter-residue distance matrix, which can be either derived from the input structure or predicted by trRosettaX, we can decode the domain boundaries under a unified framework. We name the proposed method UniDoc. The principle of UniDoc is based on the well-accepted physical concept of maximizing intra-domain interaction while minimizing inter-domain interaction. Comprehensive tests on five benchmark datasets indicate that UniDoc outperforms other state-of-the-art methods in terms of both accuracy and speed, for both sequence-based and structure-based domain parsing. The major contribution of UniDoc is providing a unified framework for structure-based and sequence-based domain parsing. We hope that UniDoc would be a convenient tool for protein domain analysis. AVAILABILITY AND IMPLEMENTATION: https://yanglab.nankai.edu.cn/UniDoc/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Peng Z."
+                    },
+                    {
+                        "name": "Su H."
+                    },
+                    {
+                        "name": "Yang J."
+                    },
+                    {
+                        "name": "Zhu K."
+                    }
+                ],
+                "date": "2023-02-03T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "A unified approach to protein domain parsing with inter-residue distance matrix"
+            },
+            "pmcid": "PMC9919455",
+            "pmid": "36734597"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Physics",
+            "uri": "http://edamontology.org/topic_3318"
+        },
+        {
+            "term": "Protein expression",
+            "uri": "http://edamontology.org/topic_0108"
+        },
+        {
+            "term": "Protein folds and structural domains",
+            "uri": "http://edamontology.org/topic_0736"
+        },
+        {
+            "term": "Structure prediction",
+            "uri": "http://edamontology.org/topic_0082"
+        }
+    ]
+}
diff --git a/data/uniport/uniport.biotools.json b/data/uniport/uniport.biotools.json
new file mode 100644
index 0000000000000..7c31a50d763e1
--- /dev/null
+++ b/data/uniport/uniport.biotools.json
@@ -0,0 +1,112 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T09:59:22.045760Z",
+    "biotoolsCURIE": "biotools:uniport",
+    "biotoolsID": "uniport",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Kai Cao"
+        },
+        {
+            "name": "Qiyu Gong"
+        },
+        {
+            "name": "Yiguang Hong"
+        },
+        {
+            "name": "Lin Wan",
+            "orcidid": "https://orcid.org/0000-0002-3511-0512"
+        }
+    ],
+    "description": "A unified computational framework for single-cell data integration with optimal transport.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://uniport.readthedocs.io/en/latest/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deisotoping",
+                    "uri": "http://edamontology.org/operation_3629"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/caokai1073/uniPort",
+    "language": [
+        "Python",
+        "R"
+    ],
+    "lastUpdate": "2023-02-26T09:59:22.048248Z",
+    "license": "MIT",
+    "name": "uniPort",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/s41467-022-35094-8",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Single-cell data integration can provide a comprehensive molecular view of cells. However, how to integrate heterogeneous single-cell multi-omics as well as spatially resolved transcriptomic data remains a major challenge. Here we introduce uniPort, a unified single-cell data integration framework that combines a coupled variational autoencoder (coupled-VAE) and minibatch unbalanced optimal transport (Minibatch-UOT). It leverages both highly variable common and dataset-specific genes for integration to handle the heterogeneity across datasets, and it is scalable to large-scale datasets. uniPort jointly embeds heterogeneous single-cell multi-omics datasets into a shared latent space. It can further construct a reference atlas for gene imputation across datasets. Meanwhile, uniPort provides a flexible label transfer framework to deconvolute heterogeneous spatial transcriptomic data using an optimal transport plan, instead of embedding latent space. We demonstrate the capability of uniPort by applying it to integrate a variety of datasets, including single-cell transcriptomics, chromatin accessibility, and spatially resolved transcriptomic data.",
+                "authors": [
+                    {
+                        "name": "Cao K."
+                    },
+                    {
+                        "name": "Gong Q."
+                    },
+                    {
+                        "name": "Hong Y."
+                    },
+                    {
+                        "name": "Wan L."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "A unified computational framework for single-cell data integration with optimal transport"
+            },
+            "pmcid": "PMC9715710",
+            "pmid": "36456571"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Chromosome conformation capture",
+            "uri": "http://edamontology.org/topic_3940"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/uroccomp/uroccomp.biotools.json b/data/uroccomp/uroccomp.biotools.json
index 0c6c3fb0e45ba..09dfce75a5222 100644
--- a/data/uroccomp/uroccomp.biotools.json
+++ b/data/uroccomp/uroccomp.biotools.json
@@ -7,6 +7,7 @@
         "File Exchange",
         "MATLAB"
     ],
+    "confidence_flag": "tool",
     "cost": "Free of charge (with restrictions)",
     "credit": [
         {
@@ -42,7 +43,7 @@
     "language": [
         "MATLAB"
     ],
-    "lastUpdate": "2022-12-09T23:48:59.665877Z",
+    "lastUpdate": "2023-01-10T15:53:26.534377Z",
     "license": "GPL-3.0",
     "link": [
         {
diff --git a/data/usagi/usagi.biotools.json b/data/usagi/usagi.biotools.json
new file mode 100644
index 0000000000000..f944df949c2e7
--- /dev/null
+++ b/data/usagi/usagi.biotools.json
@@ -0,0 +1,59 @@
+{
+    "additionDate": "2023-01-26T08:58:40.143653Z",
+    "biotoolsCURIE": "biotools:usagi",
+    "biotoolsID": "usagi",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "Observational Health Data Sciences and Informatics   OHDSI",
+            "url": "https://www.ohdsi.org/"
+        }
+    ],
+    "description": "Usagi is a software tool used to help in the process of mapping codes from a source system into terminologies, preferably standard ones, stored in the Observational Medical Outcomes Partnership (OMOP) Vocabulary (http://www.ohdsi.org/data-standardization/vocabulary-resources/). The word Usagi is Japanese for rabbit and was named after the first mapping exercise it was used for; mapping source codes used in a Japanese dataset into OMOP Vocabulary concepts.",
+    "documentation": [
+        {
+            "type": [
+                "Installation instructions"
+            ],
+            "url": "https://www.ohdsi.org/web/wiki/doku.php?id=documentation:software:usagi"
+        }
+    ],
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://github.com/OHDSI/Usagi"
+        }
+    ],
+    "editPermission": {
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                },
+                {
+                    "term": "Mapping",
+                    "uri": "http://edamontology.org/operation_2429"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://www.ohdsi.org/analytic-tools/usagi/",
+    "lastUpdate": "2023-02-01T12:13:00.462481Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Software catalogue"
+            ],
+            "url": "https://www.ohdsi.org/analytic-tools/usagi/"
+        }
+    ],
+    "name": "Usagi",
+    "owner": "iacs-biocomputacion"
+}
diff --git a/data/usat/usat.biotools.json b/data/usat/usat.biotools.json
new file mode 100644
index 0000000000000..f8095de230569
--- /dev/null
+++ b/data/usat/usat.biotools.json
@@ -0,0 +1,111 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-20T01:15:43.518565Z",
+    "biotoolsCURIE": "biotools:usat",
+    "biotoolsID": "usat",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "Xuewen.wang@unthscc.edu",
+            "name": "Xuewen Wang",
+            "orcidid": "http://orcid.org/0000-0003-2820-9255",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Bruce Budowle"
+        },
+        {
+            "name": "Jianye Ge"
+        }
+    ],
+    "description": "A Bioinformatic Toolkit to Facilitate Interpretation and Comparative Visualization of Tandem Repeat Sequences.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Haplotype mapping",
+                    "uri": "http://edamontology.org/operation_0487"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/XuewenWangUGA/USAT",
+    "language": [
+        "Java",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-20T01:17:37.689993Z",
+    "license": "LGPL-2.1",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/Ge-Lab/USAT"
+        }
+    ],
+    "name": "USAT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/s12859-022-05021-1",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Tandem repeats (TR), highly variable genomic variants, are widely used in individual identification, disease diagnostics, and evolutionary studies. The recent advances in sequencing technologies and bioinformatic tools facilitate calling TR haplotypes genome widely. Both length-based and sequence-based TR alleles are used in different applications. However, sequence-based TR alleles could provide the highest precision in characterizing TR haplotypes. The need to identify the differences at the single nucleotide level between or among TR haplotypes with an easy-use bioinformatic tool is essential. Results: In this study, we developed a Universal STR Allele Toolkit (USAT) for TR haplotype analysis, which takes TR haplotype output from existing tools to perform allele size conversion, sequence comparison of haplotypes, figure plotting, comparison for allele distribution, and interactive visualization. An exemplary application of USAT for analysis of the CODIS core STR loci for DNA forensics with benchmarking human individuals demonstrated the capabilities of USAT. USAT has user-friendly graphic interfaces and runs fast in major computing operating systems with parallel computing enabled. Conclusion: USAT is a user-friendly bioinformatics software for interpretation, visualization, and comparisons of TRs.",
+                "authors": [
+                    {
+                        "name": "Budowle B."
+                    },
+                    {
+                        "name": "Ge J."
+                    },
+                    {
+                        "name": "Wang X."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "USAT: a bioinformatic toolkit to facilitate interpretation and comparative visualization of tandem repeat sequences"
+            },
+            "pmcid": "PMC9675219",
+            "pmid": "36402991"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Sequence composition, complexity and repeats",
+            "uri": "http://edamontology.org/topic_0157"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/vaeda/vaeda.biotools.json b/data/vaeda/vaeda.biotools.json
new file mode 100644
index 0000000000000..7356f48d9cfa8
--- /dev/null
+++ b/data/vaeda/vaeda.biotools.json
@@ -0,0 +1,102 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T00:33:52.016648Z",
+    "biotoolsCURIE": "biotools:vaeda",
+    "biotoolsID": "vaeda",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "kostka@pitt.edu",
+            "name": "Dennis Kostka",
+            "orcidid": "https://orcid.org/0000-0002-1460-5487",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Hannah Schriever"
+        }
+    ],
+    "description": "Vaeda computationally annotates doublets in single-cell RNA sequencing data.",
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://doi.org/10.5281/zenodo.7199783"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/kostkalab/vaeda",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T00:33:52.019192Z",
+    "license": "MIT",
+    "name": "vaeda",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC720",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Single-cell RNA sequencing (scRNA-seq) continues to expand our knowledge by facilitating the study of transcriptional heterogeneity at the level of single cells. Despite this technology's utility and success in biomedical research, technical artifacts are present in scRNA-seq data. Doublets/multiplets are a type of artifact that occurs when two or more cells are tagged by the same barcode, and therefore they appear as a single cell. Because this introduces non-existent transcriptional profiles, doublets can bias and mislead downstream analysis. To address this limitation, computational methods to annotate and remove doublets form scRNA-seq datasets are needed. RESULTS: We introduce vaeda (Variational Auto-Encoder for Doublet Annotation), a new approach for computational annotation of doublets in scRNA-seq data. Vaeda integrates a variational auto-encoder and Positive-Unlabeled learning to produce doublet scores and binary doublet calls. We apply vaeda, along with seven existing doublet annotation methods, to 16 benchmark datasets and find that vaeda performs competitively in terms of doublet scores and doublet calls. Notably, vaeda outperforms other python-based methods for doublet annotation. Altogether, vaeda is a robust and competitive method for scRNA-seq doublet annotation and may be of particular interest in the context of python-based workflows. AVAILABILITY AND IMPLEMENTATION: Vaeda is available at https://github.com/kostkalab/vaeda, and the version used for the results we present here is archived at zenodo (https://doi.org/10.5281/zenodo.7199783). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Kostka D."
+                    },
+                    {
+                        "name": "Schriever H."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "Vaeda computationally annotates doublets in single-cell RNA sequencing data"
+            },
+            "pmcid": "PMC9805559",
+            "pmid": "36342203"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        },
+        {
+            "term": "RNA",
+            "uri": "http://edamontology.org/topic_0099"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/variantspark/variantspark.biotools.json b/data/variantspark/variantspark.biotools.json
index 12f0105286ef3..c1cc702e9c818 100644
--- a/data/variantspark/variantspark.biotools.json
+++ b/data/variantspark/variantspark.biotools.json
@@ -1,15 +1,53 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2020-04-30T12:25:08Z",
     "biotoolsCURIE": "biotools:variantspark",
     "biotoolsID": "variantspark",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "VariantSpark is a tailored Apache Spark-based machine learning framework that creates insights from high-dimensional data, including genomics and clinical data. VariantSpark’s machine learning method overcomes the limitation of traditional approaches that requires data to be eliminated or identifying only independent markers. Especially complex events are triggered by multiple contributing factors. VariantSpark is able to detect such sets of interacting features thereby identifying more accurate predictive markers. VariantSpark builds on the Random Forest Machine Learning method, which allows to interrogate the tree-based models and identify which features contributed in what proportion to the overall prediction outcome. We also provide a visualization engine that shows the interplay between features and their label association.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://variantspark.readthedocs.io/en/latest/index.html"
+        }
+    ],
     "editPermission": {
         "type": "private"
     },
     "homepage": "https://bioinformatics.csiro.au/variantspark/",
-    "lastUpdate": "2022-12-09T23:50:59.063091Z",
+    "language": [
+        "JavaScript"
+    ],
+    "lastUpdate": "2023-01-10T15:43:14.003083Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Other"
+            ],
+            "url": "https://aws.amazon.com/marketplace/pp/prodview-pgna4dj6xqqde"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/aehrc/VariantSpark"
+        }
+    ],
     "name": "VariantSpark",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "ntwine",
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "Machine learning",
diff --git a/data/vcfpop/vcfpop.biotools.json b/data/vcfpop/vcfpop.biotools.json
new file mode 100644
index 0000000000000..febb195207458
--- /dev/null
+++ b/data/vcfpop/vcfpop.biotools.json
@@ -0,0 +1,140 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-23T10:12:35.663506Z",
+    "biotoolsCURIE": "biotools:vcfpop",
+    "biotoolsID": "vcfpop",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Baoguo Li"
+        },
+        {
+            "name": "Bing Yang"
+        },
+        {
+            "name": "Jincuo Ao"
+        },
+        {
+            "name": "Kang Huang",
+            "orcidid": "https://orcid.org/0000-0002-8357-117X"
+        }
+    ],
+    "description": "Performing population genetics analyses for polyploids and anisoploids based on next-generation sequencing variant calling dataset.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Ecological modelling",
+                    "uri": "http://edamontology.org/operation_3946"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://github.com/huangkang1987/vcfpop",
+    "language": [
+        "C",
+        "C++"
+    ],
+    "lastUpdate": "2023-01-23T10:12:35.665990Z",
+    "license": "GPL-3.0",
+    "name": "VCFPOP",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1111/1755-0998.13744",
+            "metadata": {
+                "abstract": "© 2022 John Wiley & Sons Ltd.Polyploids are cells or organisms with a genome consisting of more than two sets of homologous chromosomes. Polyploid plants have important traits that facilitate speciation and are thus often model systems for evolutionary, molecular ecology and agricultural studies. However, due to their unusual mode of inheritance and double-reduction, diploid models of population genetic analysis cannot properly be applied to autopolyploids. To overcome this problem, we developed a software package entitled vcfpop to perform a variety of population genetic analyses for autopolyploids, such as parentage analysis, analysis of molecular variance, principal coordinates analysis, hierarchical clustering analysis and Bayesian clustering. We used three data sets to evaluate the capability of vcfpop to analyse large data sets on a desktop computer. This software is freely available at http://github.com/huangkang1987/vcfpop.",
+                "authors": [
+                    {
+                        "name": "Ao J."
+                    },
+                    {
+                        "name": "Cui Y."
+                    },
+                    {
+                        "name": "Dunn D.W."
+                    },
+                    {
+                        "name": "He S."
+                    },
+                    {
+                        "name": "Huang K."
+                    },
+                    {
+                        "name": "Kong Y."
+                    },
+                    {
+                        "name": "Li B."
+                    },
+                    {
+                        "name": "Li N."
+                    },
+                    {
+                        "name": "Li W."
+                    },
+                    {
+                        "name": "Li W."
+                    },
+                    {
+                        "name": "Li Y."
+                    },
+                    {
+                        "name": "Shen Y."
+                    },
+                    {
+                        "name": "Wang D."
+                    },
+                    {
+                        "name": "Yang B."
+                    }
+                ],
+                "date": "2022-01-01T00:00:00Z",
+                "journal": "Molecular Ecology Resources",
+                "title": "vcfpop: Performing population genetics analyses for autopolyploids and aneuploids based on next-generation sequencing data sets"
+            },
+            "pmid": "36458971"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Agricultural science",
+            "uri": "http://edamontology.org/topic_3810"
+        },
+        {
+            "term": "Ecology",
+            "uri": "http://edamontology.org/topic_0610"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Population genetics",
+            "uri": "http://edamontology.org/topic_3056"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/vdjminer/vdjminer.biotools.json b/data/vdjminer/vdjminer.biotools.json
new file mode 100644
index 0000000000000..a464076d4f9f5
--- /dev/null
+++ b/data/vdjminer/vdjminer.biotools.json
@@ -0,0 +1,143 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T12:41:02.643146Z",
+    "biotoolsCURIE": "biotools:vdjminer",
+    "biotoolsID": "vdjminer",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jianhua.yao@gmail.com",
+            "name": "Jianhua Yao",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Yu Zhao"
+        },
+        {
+            "name": "Bing He",
+            "orcidid": "https://orcid.org/0000-0003-1719-9290"
+        },
+        {
+            "name": "Jiangning Song",
+            "orcidid": "https://orcid.org/0000-0001-8031-9086"
+        }
+    ],
+    "description": "Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire.",
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://github.com/TencentAILabHealthcare/VDJMiner/tree/main/data"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Peptide immunogenicity prediction",
+                    "uri": "http://edamontology.org/operation_0252"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://gene.ai.tencent.com/VDJMiner/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-10T12:41:02.645829Z",
+    "license": "Not licensed",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/TencentAILabHealthcare/VDJMiner"
+        }
+    ],
+    "name": "VDJMiner",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC555",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Underlying medical conditions, such as cancer, kidney disease and heart failure, are associated with a higher risk for severe COVID-19. Accurate classification of COVID-19 patients with underlying medical conditions is critical for personalized treatment decision and prognosis estimation. In this study, we propose an interpretable artificial intelligence model termed VDJMiner to mine the underlying medical conditions and predict the prognosis of COVID-19 patients according to their immune repertoires. In a cohort of more than 1400 COVID-19 patients, VDJMiner accurately identifies multiple underlying medical conditions, including cancers, chronic kidney disease, autoimmune disease, diabetes, congestive heart failure, coronary artery disease, asthma and chronic obstructive pulmonary disease, with an average area under the receiver operating characteristic curve (AUC) of 0.961. Meanwhile, in this same cohort, VDJMiner achieves an AUC of 0.922 in predicting severe COVID-19. Moreover, VDJMiner achieves an accuracy of 0.857 in predicting the response of COVID-19 patients to tocilizumab treatment on the leave-one-out test. Additionally, VDJMiner interpretively mines and scores V(D)J gene segments of the T-cell receptors that are associated with the disease. The identified associations between single-cell V(D)J gene segments and COVID-19 are highly consistent with previous studies. The source code of VDJMiner is publicly accessible at https://github.com/TencentAILabHealthcare/VDJMiner. The web server of VDJMiner is available at https://gene.ai.tencent.com/VDJMiner/.",
+                "authors": [
+                    {
+                        "name": "Duan L."
+                    },
+                    {
+                        "name": "He B."
+                    },
+                    {
+                        "name": "Huang Z.-A."
+                    },
+                    {
+                        "name": "Song J."
+                    },
+                    {
+                        "name": "Wang L."
+                    },
+                    {
+                        "name": "Xu Z."
+                    },
+                    {
+                        "name": "Yang F."
+                    },
+                    {
+                        "name": "Yao J."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    },
+                    {
+                        "name": "Zhao X."
+                    },
+                    {
+                        "name": "Zhao Y."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire"
+            },
+            "pmid": "36567255"
+        }
+    ],
+    "toolType": [
+        "Command-line tool",
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Cardiology",
+            "uri": "http://edamontology.org/topic_3335"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Surgery",
+            "uri": "http://edamontology.org/topic_3421"
+        },
+        {
+            "term": "Urology and nephrology",
+            "uri": "http://edamontology.org/topic_3422"
+        }
+    ]
+}
diff --git a/data/vechat/vechat.biotools.json b/data/vechat/vechat.biotools.json
new file mode 100644
index 0000000000000..e6e539032efda
--- /dev/null
+++ b/data/vechat/vechat.biotools.json
@@ -0,0 +1,107 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T00:16:14.913180Z",
+    "biotoolsCURIE": "biotools:vechat",
+    "biotoolsID": "vechat",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "a.schoenhuth@cwi.nl",
+            "name": "Alexander Schönhuth",
+            "orcidid": "https://orcid.org/0000-0003-3529-0856",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Xiao Luo"
+        },
+        {
+            "name": "Xiongbin Kang"
+        }
+    ],
+    "description": "Correcting errors in long reads using variation graphs.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "De-novo assembly",
+                    "uri": "http://edamontology.org/operation_0524"
+                },
+                {
+                    "term": "Genome assembly",
+                    "uri": "http://edamontology.org/operation_0525"
+                },
+                {
+                    "term": "Haplotype mapping",
+                    "uri": "http://edamontology.org/operation_0487"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Sequencing error detection",
+                    "uri": "http://edamontology.org/operation_3195"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/HaploKit/vechat",
+    "language": [
+        "C++",
+        "Python"
+    ],
+    "lastUpdate": "2023-01-25T00:16:14.915840Z",
+    "license": "GPL-3.0",
+    "name": "VeChat",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41467-022-34381-8",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Error correction is the canonical first step in long-read sequencing data analysis. Current self-correction methods, however, are affected by consensus sequence induced biases that mask true variants in haplotypes of lower frequency showing in mixed samples. Unlike consensus sequence templates, graph-based reference systems are not affected by such biases, so do not mistakenly mask true variants as errors. We present VeChat, as an approach to implement this idea: VeChat is based on variation graphs, as a popular type of data structure for pangenome reference systems. Extensive benchmarking experiments demonstrate that long reads corrected by VeChat contain 4 to 15 (Pacific Biosciences) and 1 to 10 times (Oxford Nanopore Technologies) less errors than when being corrected by state of the art approaches. Further, using VeChat prior to long-read assembly significantly improves the haplotype awareness of the assemblies. VeChat is an easy-to-use open-source tool and publicly available at https://github.com/HaploKit/vechat.",
+                "authors": [
+                    {
+                        "name": "Kang X."
+                    },
+                    {
+                        "name": "Luo X."
+                    },
+                    {
+                        "name": "Schonhuth A."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "VeChat: correcting errors in long reads using variation graphs"
+            },
+            "pmcid": "PMC9636371",
+            "pmid": "36333324"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA polymorphism",
+            "uri": "http://edamontology.org/topic_2885"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        },
+        {
+            "term": "Sequencing",
+            "uri": "http://edamontology.org/topic_3168"
+        }
+    ]
+}
diff --git a/data/vfuzz/vfuzz.biotools.json b/data/vfuzz/vfuzz.biotools.json
deleted file mode 100644
index 6273db44580ad..0000000000000
--- a/data/vfuzz/vfuzz.biotools.json
+++ /dev/null
@@ -1,20 +0,0 @@
-{
-    "additionDate": "2021-01-18T10:56:20Z",
-    "biotoolsCURIE": "biotools:vfuzz",
-    "biotoolsID": "vfuzz",
-    "confidence_flag": "tool",
-    "description": "Vulnerability Prediction-Assisted Evolutionary Fuzzing for Binary Programs.\n\nFuzzing is a technique of finding bugs by executing a target program recurrently with a large number of abnormal inputs. Most of the coverage-based fuzzers consider all parts of a program equally and pay too much attention to how to improve the code coverage. It is inefficient as the vulnerable code only takes a tiny fraction of the entire code. In this article, we design and implement an evolutionary fuzzing framework called V-Fuzz, which aims to find bugs efficiently and quickly in limited time for binary programs. V-Fuzz consists of two main components: 1) a vulnerability prediction model and 2) a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of a program are more likely to be vulnerable",
-    "editPermission": {
-        "type": "private"
-    },
-    "homepage": "https://github.com/guidovranken/vfuzz",
-    "lastUpdate": "2021-03-20T12:30:29Z",
-    "name": "VFuzz",
-    "owner": "zsmag19",
-    "publication": [
-        {
-            "doi": "10.1109/TCYB.2020.3013675",
-            "pmid": "32946405"
-        }
-    ]
-}
diff --git a/data/vgsc-db/vgsc-db.biotools.json b/data/vgsc-db/vgsc-db.biotools.json
new file mode 100644
index 0000000000000..c06e42393f1af
--- /dev/null
+++ b/data/vgsc-db/vgsc-db.biotools.json
@@ -0,0 +1,127 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-25T00:11:11.356221Z",
+    "biotoolsCURIE": "biotools:vgsc-db",
+    "biotoolsID": "vgsc-db",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "panpeichen@zju.edu.cn",
+            "name": "Peichen Pan",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "tingjunhou@zju.edu.cn",
+            "name": "Tingjun Hou",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Gaoang Wang"
+        },
+        {
+            "name": "Jiahui Yu"
+        }
+    ],
+    "description": "An online database of voltage-gated sodium channels.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Virtual screening",
+                    "uri": "http://edamontology.org/operation_3938"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://cadd.zju.edu.cn/vgsc/",
+    "lastUpdate": "2023-01-25T00:11:11.359587Z",
+    "name": "VGSC-DB",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S13321-022-00655-Y",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).As an important member of ion channels family, the voltage-gated sodium channel (VGSC/Nav) is associated with a variety of diseases, including epilepsy, migraine, ataxia, etc., and has always been a hot target for drug design and discovery. Many subtype-selective modulators targeting VGSCs have been reported, and some of them have been approved for clinical applications. However, the drug design resources related to VGSCs are insufficient, especially the lack of accurate and extensive compound data toward VGSCs. To fulfill this demand, we develop the Voltage-gated Sodium Channels Database (VGSC-DB). VGSC-DB is the first open-source database for VGSCs, which provides open access to 6055 data records, including 3396 compounds from 173 references toward nine subtypes of Navs (Nav1.1 ~ Nav1.9). A total of 28 items of information is included in each data record, including the chemical structure, biological activity (IC50/EC50), target, binding site, organism, chemical and physical properties, etc. VGSC-DB collects the data from small-molecule compounds, toxins and various derivatives. Users can search the information of compounds by text or structure, and the advanced search function is also supported to realize batch query. VGSC-DB is freely accessible at http://cadd.zju.edu.cn/vgsc/, and all the data can be downloaded in XLSX/SDF file formats. Graphical Abstract: [Figure not available: see fulltext.].",
+                "authors": [
+                    {
+                        "name": "Cao D."
+                    },
+                    {
+                        "name": "Du H."
+                    },
+                    {
+                        "name": "Hou T."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Pan P."
+                    },
+                    {
+                        "name": "Shen C."
+                    },
+                    {
+                        "name": "Wang G."
+                    },
+                    {
+                        "name": "Yu J."
+                    },
+                    {
+                        "name": "Zhang X."
+                    },
+                    {
+                        "name": "Zhang Y."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Journal of Cheminformatics",
+                "title": "VGSC-DB: an online database of voltage-gated sodium channels"
+            },
+            "pmcid": "PMC9628066",
+            "pmid": "36320030"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Biological databases",
+            "uri": "http://edamontology.org/topic_3071"
+        },
+        {
+            "term": "Drug development",
+            "uri": "http://edamontology.org/topic_3373"
+        },
+        {
+            "term": "Medicinal chemistry",
+            "uri": "http://edamontology.org/topic_0209"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/vilon/vilon.biotools.json b/data/vilon/vilon.biotools.json
new file mode 100644
index 0000000000000..80e64a2987a1d
--- /dev/null
+++ b/data/vilon/vilon.biotools.json
@@ -0,0 +1,94 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-24T23:51:23.303826Z",
+    "biotoolsCURIE": "biotools:vilon",
+    "biotoolsID": "vilon",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "nar21@kreil.org",
+            "name": "David P Kreil",
+            "orcidid": "https://orcid.org/0000-0001-7538-2056",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "eric.kolaczyk@mcgill.ca",
+            "name": "Eric D Kolaczyk",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Alexander D Aldoshin"
+        },
+        {
+            "name": "Swati Singh"
+        },
+        {
+            "name": "Maciej M Kańduła",
+            "orcidid": "https://orcid.org/0000-0001-5727-1491"
+        }
+    ],
+    "description": "Multi-layer network approach to data integration demonstrated for patient stratification.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Expression profile clustering",
+                    "uri": "http://edamontology.org/operation_0313"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/data-int/vilon/",
+    "language": [
+        "Perl",
+        "R"
+    ],
+    "lastUpdate": "2023-01-24T23:51:23.306639Z",
+    "license": "GPL-3.0",
+    "name": "ViLoN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/NAR/GKAC988",
+            "pmid": "36395816"
+        }
+    ],
+    "toolType": [
+        "Script"
+    ],
+    "topic": [
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Pathology",
+            "uri": "http://edamontology.org/topic_0634"
+        },
+        {
+            "term": "Personalised medicine",
+            "uri": "http://edamontology.org/topic_3577"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/vimrt/vimrt.biotools.json b/data/vimrt/vimrt.biotools.json
new file mode 100644
index 0000000000000..ffe84068e5465
--- /dev/null
+++ b/data/vimrt/vimrt.biotools.json
@@ -0,0 +1,157 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-24T23:43:12.519115Z",
+    "biotoolsCURIE": "biotools:vimrt",
+    "biotoolsID": "vimrt",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xyzhang@tongji.edu.cn",
+            "name": "Xiaoyan Zhang",
+            "orcidid": "https://orcid.org/0000-0003-1562-0045",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "nadger_wang@139.com",
+            "name": "Ying Wang",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Fanglin Tan"
+        },
+        {
+            "name": "Yuantao Tong"
+        }
+    ],
+    "description": "A text-mining tool and search engine for automated virus mutation recognition.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Disease name",
+                        "uri": "http://edamontology.org/data_3668"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Gene name",
+                        "uri": "http://edamontology.org/data_2299"
+                    }
+                },
+                {
+                    "data": {
+                        "term": "Virus identifier",
+                        "uri": "http://edamontology.org/data_2913"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Small molecule design",
+                    "uri": "http://edamontology.org/operation_4009"
+                },
+                {
+                    "term": "Text mining",
+                    "uri": "http://edamontology.org/operation_0306"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://bmtongji.cn:1225/mutation/index",
+    "lastUpdate": "2023-01-24T23:43:12.522495Z",
+    "name": "ViMRT",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAC721",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Virus mutation is one of the most important research issues which plays a critical role in disease progression and has prompted substantial scientific publications. Mutation extraction from published literature has become an increasingly important task, benefiting many downstream applications such as vaccine design and drug usage. However, most existing approaches have low performances in extracting virus mutation due to both lack of precise virus mutation information and their development based on human gene mutations. RESULTS: We developed ViMRT, a text-mining tool and search engine for automated virus mutation recognition using natural language processing. ViMRT mainly developed 8 optimized rules and 12 regular expressions based on a development dataset comprising 830 papers of 5 human severe disease-related viruses. It achieved higher performance than other tools in a test dataset (1662 papers, 99.17% in F1-score) and has been applied well to two other viruses, influenza virus and severe acute respiratory syndrome coronavirus-2 (212 papers, 96.99% in F1-score). These results indicate that ViMRT is a high-performance method for the extraction of virus mutation from the biomedical literature. Besides, we present a search engine for researchers to quickly find and accurately search virus mutation-related information including virus genes and related diseases. AVAILABILITY AND IMPLEMENTATION: ViMRT software is freely available at http://bmtongji.cn:1225/mutation/index.",
+                "authors": [
+                    {
+                        "name": "Cheng S."
+                    },
+                    {
+                        "name": "Crabbe M.J.C."
+                    },
+                    {
+                        "name": "Fang M."
+                    },
+                    {
+                        "name": "Huang D."
+                    },
+                    {
+                        "name": "Huang H."
+                    },
+                    {
+                        "name": "Tan F."
+                    },
+                    {
+                        "name": "Tong Y."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Wei Z."
+                    },
+                    {
+                        "name": "Xie Y."
+                    },
+                    {
+                        "name": "Zhang X."
+                    },
+                    {
+                        "name": "Zhang Z."
+                    },
+                    {
+                        "name": "Zong H."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "ViMRT: a text-mining tool and search engine for automated virus mutation recognition"
+            },
+            "pmcid": "PMC9805560",
+            "pmid": "36342236"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Data mining",
+            "uri": "http://edamontology.org/topic_3473"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Medicine",
+            "uri": "http://edamontology.org/topic_3303"
+        },
+        {
+            "term": "Natural language processing",
+            "uri": "http://edamontology.org/topic_0218"
+        },
+        {
+            "term": "Vaccinology",
+            "uri": "http://edamontology.org/topic_3966"
+        }
+    ]
+}
diff --git a/data/vir2drug/vir2drug.biotools.json b/data/vir2drug/vir2drug.biotools.json
new file mode 100644
index 0000000000000..4e3d2c8a96498
--- /dev/null
+++ b/data/vir2drug/vir2drug.biotools.json
@@ -0,0 +1,91 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T12:37:11.194864Z",
+    "biotoolsCURIE": "biotools:vir2drug",
+    "biotoolsID": "vir2drug",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "georgem@cing.ac.cy",
+            "name": "George Minadakis",
+            "orcidid": "https://orcid.org/0000-0002-7752-2208",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Marios Tomazou"
+        },
+        {
+            "name": "Nikolas Dietis"
+        },
+        {
+            "name": "George M Spyrou",
+            "orcidid": "https://orcid.org/0000-0002-2470-3363"
+        }
+    ],
+    "description": "A drug repurposing framework based on protein similarities between pathogens.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "https://bioinformatics.cing.ac.cy/vir2drug",
+    "lastUpdate": "2023-02-10T12:37:11.197607Z",
+    "name": "Vir2Drug",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC536",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.We draw from the assumption that similarities between pathogens at both pathogen protein and host protein level, may provide the appropriate framework to identify and rank candidate drugs to be used against a specific pathogen. Vir2Drug is a drug repurposing tool that uses network-based approaches to identify and rank candidate drugs for a specific pathogen, combining information obtained from: (a) ranked pathogen-to-pathogen networks based on protein similarities between pathogens, (b) taxonomy distance between pathogens and (c) drugs targeting specific pathogen's and host proteins. The underlying pathogen networks are used to screen drugs by means of specific methodologies that account for either the host or pathogen's protein targets. Vir2Drug is a useful and yet informative tool for drug repurposing against known or unknown pathogens especially in periods where the emergence for repurposed drugs plays significant role in handling viral outbreaks, until reaching a vaccine. The web tool is available at: https://bioinformatics.cing.ac.cy/vir2drug, https://vir2drug.cing-big.hpcf.cyi.ac.cy.",
+                "authors": [
+                    {
+                        "name": "Dietis N."
+                    },
+                    {
+                        "name": "Minadakis G."
+                    },
+                    {
+                        "name": "Spyrou G.M."
+                    },
+                    {
+                        "name": "Tomazou M."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "Vir2Drug: a drug repurposing framework based on protein similarities between pathogens"
+            },
+            "pmcid": "PMC9851336",
+            "pmid": "36513376"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Pharmacology",
+            "uri": "http://edamontology.org/topic_0202"
+        },
+        {
+            "term": "Preclinical and clinical studies",
+            "uri": "http://edamontology.org/topic_3379"
+        },
+        {
+            "term": "Protein interactions",
+            "uri": "http://edamontology.org/topic_0128"
+        },
+        {
+            "term": "Protein targeting and localisation",
+            "uri": "http://edamontology.org/topic_0140"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        }
+    ]
+}
diff --git a/data/viralcc/viralcc.biotools.json b/data/viralcc/viralcc.biotools.json
new file mode 100644
index 0000000000000..c2cb55f46a0e5
--- /dev/null
+++ b/data/viralcc/viralcc.biotools.json
@@ -0,0 +1,114 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-17T08:33:38.524003Z",
+    "biotoolsCURIE": "biotools:viralcc",
+    "biotoolsID": "viralcc",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "fsun@usc.edu",
+            "name": "Fengzhu Sun",
+            "orcidid": "https://orcid.org/0000-0002-8552-043X",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Jed A. Fuhrman",
+            "orcidid": "http://orcid.org/0000-0002-2361-1985"
+        },
+        {
+            "name": "Yuxuan Du",
+            "orcidid": "http://orcid.org/0000-0002-0568-3838"
+        }
+    ],
+    "description": "ViralCC retrieves complete viral genomes and virus-host pairs from metagenomic Hi-C data.",
+    "download": [
+        {
+            "type": "Source code",
+            "url": "https://github.com/dyxstat/Reproduce_ViralCC/tree/main/Scripts"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/dyxstat/ViralCC",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-17T08:33:38.528566Z",
+    "license": "GPL-3.0",
+    "name": "ViralCC",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/S41467-023-35945-Y",
+            "metadata": {
+                "abstract": "The introduction of high-throughput chromosome conformation capture (Hi-C) into metagenomics enables reconstructing high-quality metagenome-assembled genomes (MAGs) from microbial communities. Despite recent advances in recovering eukaryotic, bacterial, and archaeal genomes using Hi-C contact maps, few of Hi-C-based methods are designed to retrieve viral genomes. Here we introduce ViralCC, a publicly available tool to recover complete viral genomes and detect virus-host pairs using Hi-C data. Compared to other Hi-C-based methods, ViralCC leverages the virus-host proximity structure as a complementary information source for the Hi-C interactions. Using mock and real metagenomic Hi-C datasets from several different microbial ecosystems, including the human gut, cow fecal, and wastewater, we demonstrate that ViralCC outperforms existing Hi-C-based binning methods as well as state-of-the-art tools specifically dedicated to metagenomic viral binning. ViralCC can also reveal the taxonomic structure of viruses and virus-host pairs in microbial communities. When applied to a real wastewater metagenomic Hi-C dataset, ViralCC constructs a phage-host network, which is further validated using CRISPR spacer analyses. ViralCC is an open-source pipeline available at https://github.com/dyxstat/ViralCC.",
+                "authors": [
+                    {
+                        "name": "Du Y."
+                    },
+                    {
+                        "name": "Fuhrman J.A."
+                    },
+                    {
+                        "name": "Sun F."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "ViralCC retrieves complete viral genomes and virus-host pairs from metagenomic Hi-C data"
+            },
+            "pmcid": "PMC9889337",
+            "pmid": "36720887"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Chromosome conformation capture",
+            "uri": "http://edamontology.org/topic_3940"
+        },
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Metagenomics",
+            "uri": "http://edamontology.org/topic_3174"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/virmutsig/virmutsig.biotools.json b/data/virmutsig/virmutsig.biotools.json
new file mode 100644
index 0000000000000..c18f2d91db61e
--- /dev/null
+++ b/data/virmutsig/virmutsig.biotools.json
@@ -0,0 +1,147 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-24T15:50:49.569918Z",
+    "biotoolsCURIE": "biotools:virmutsig",
+    "biotoolsID": "virmutsig",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "alex.graudenzi@ibfm.cnr.it",
+            "name": "Alex Graudenzi",
+            "orcidid": "https://orcid.org/0000-0001-5452-1918",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "daniele.ramazzotti@unimib.it",
+            "name": "Daniele Ramazzotti",
+            "orcidid": "https://orcid.org/0000-0002-6087-2666",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "d.maspero@campus.unimib.it",
+            "name": "Davide Maspero",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Danilo Porro"
+        },
+        {
+            "name": "Fabrizio Angaroni"
+        },
+        {
+            "name": "Rocco Piazza"
+        }
+    ],
+    "description": "Discovery and assignment of viral mutational signatures from sequencing data.",
+    "download": [
+        {
+            "type": "Container file",
+            "url": "https://hub.docker.com/r/dcblab/virmutsig_img"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Variant calling",
+                    "uri": "http://edamontology.org/operation_3227"
+                },
+                {
+                    "term": "de Novo sequencing",
+                    "uri": "http://edamontology.org/operation_3644"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/BIMIB-DISCo/VirMutSig",
+    "language": [
+        "R"
+    ],
+    "lastUpdate": "2023-01-24T15:50:49.573238Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/BIMIB-DISCo/VirMutSig.git"
+        }
+    ],
+    "name": "VirMutSig",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.XPRO.2021.100911",
+            "metadata": {
+                "abstract": "© 2021 The Author(s)We describe the procedures to perform the following: (1) the de novo discovery of mutational signatures from raw sequencing data of viral samples and (2) the association of existing viral mutational signatures to the samples of a given dataset. The goal is to identify and characterize the nucleotide substitution patterns related to the mutational processes that underlie the origination of variants in viral genomes. The VirMutSig protocol is available at this link: https://github.com/BIMIB-DISCo/VirMutSig. For complete information on the theoretical aspects of this protocol, please refer to Graudenzi et al. (2021).",
+                "authors": [
+                    {
+                        "name": "Angaroni F."
+                    },
+                    {
+                        "name": "Graudenzi A."
+                    },
+                    {
+                        "name": "Maspero D."
+                    },
+                    {
+                        "name": "Piazza R."
+                    },
+                    {
+                        "name": "Porro D."
+                    },
+                    {
+                        "name": "Ramazzotti D."
+                    }
+                ],
+                "citationCount": 1,
+                "date": "2021-12-17T00:00:00Z",
+                "journal": "STAR Protocols",
+                "title": "VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data"
+            },
+            "pmcid": "PMC9680118"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Systems biology",
+            "uri": "http://edamontology.org/topic_2259"
+        }
+    ]
+}
diff --git a/data/virnatrap/virnatrap.biotools.json b/data/virnatrap/virnatrap.biotools.json
new file mode 100644
index 0000000000000..e5dbd07890df9
--- /dev/null
+++ b/data/virnatrap/virnatrap.biotools.json
@@ -0,0 +1,135 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-27T13:38:30.942471Z",
+    "biotoolsCURIE": "biotools:virnatrap",
+    "biotoolsID": "virnatrap",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "nauslander@wistar.org",
+            "name": "Noam Auslander",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Abdurrahman Elbasir"
+        },
+        {
+            "name": "Alejandro A. Schäffer"
+        },
+        {
+            "name": "Daniel E. Schäffer",
+            "orcidid": "http://orcid.org/0000-0003-3608-152X"
+        }
+    ],
+    "description": "Characterizing the landscape of viral expression in cancer by deep learning.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Read mapping",
+                    "uri": "http://edamontology.org/operation_3198"
+                },
+                {
+                    "term": "Sequence assembly",
+                    "uri": "http://edamontology.org/operation_0310"
+                },
+                {
+                    "term": "Sequence motif recognition",
+                    "uri": "http://edamontology.org/operation_0239"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/AuslanderLab/virnatrap",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-27T13:38:30.945495Z",
+    "license": "MIT",
+    "name": "viRNAtrap",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1038/s41467-023-36336-z",
+            "metadata": {
+                "abstract": "© 2023, The Author(s).About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we develop viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We utilize viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA). Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer and disclose human endogenous viruses whose expression is associated with poor overall survival. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions.",
+                "authors": [
+                    {
+                        "name": "Auslander N."
+                    },
+                    {
+                        "name": "Elbasir A."
+                    },
+                    {
+                        "name": "Hao X."
+                    },
+                    {
+                        "name": "Lieberman P.M."
+                    },
+                    {
+                        "name": "Long Q."
+                    },
+                    {
+                        "name": "Morris Q."
+                    },
+                    {
+                        "name": "Schaffer A.A."
+                    },
+                    {
+                        "name": "Schaffer D.E."
+                    },
+                    {
+                        "name": "Tsingas K."
+                    },
+                    {
+                        "name": "Wickramasinghe J."
+                    },
+                    {
+                        "name": "Ye Y."
+                    },
+                    {
+                        "name": "Zhang R."
+                    }
+                ],
+                "date": "2023-12-01T00:00:00Z",
+                "journal": "Nature Communications",
+                "title": "A deep learning approach reveals unexplored landscape of viral expression in cancer"
+            },
+            "pmcid": "PMC9922274",
+            "pmid": "36774364"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Sequence assembly",
+            "uri": "http://edamontology.org/topic_0196"
+        }
+    ]
+}
diff --git a/data/virtual_fly_brain/virtual_fly_brain.biotools.json b/data/virtual_fly_brain/virtual_fly_brain.biotools.json
new file mode 100644
index 0000000000000..2e4f4dbca30a4
--- /dev/null
+++ b/data/virtual_fly_brain/virtual_fly_brain.biotools.json
@@ -0,0 +1,140 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-03T22:40:52.427541Z",
+    "biotoolsCURIE": "biotools:virtual_fly_brain",
+    "biotoolsID": "virtual_fly_brain",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "davidos@ebi.ac.uk",
+            "name": "David Osumi-Sutherland",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Gregory S X E Jefferis"
+        },
+        {
+            "name": "Marta Costa"
+        },
+        {
+            "name": "Robert Court"
+        }
+    ],
+    "description": "Interactive atlas of the Drosophila nervous system.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Database search",
+                    "uri": "http://edamontology.org/operation_2421"
+                },
+                {
+                    "term": "Image annotation",
+                    "uri": "http://edamontology.org/operation_3553"
+                },
+                {
+                    "term": "Neurite measurement",
+                    "uri": "http://edamontology.org/operation_3450"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://virtualflybrain.org",
+    "lastUpdate": "2023-03-03T22:40:52.429975Z",
+    "name": "Virtual Fly Brain",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FPHYS.2023.1076533",
+            "metadata": {
+                "abstract": "As a model organism, Drosophila is uniquely placed to contribute to our understanding of how brains control complex behavior. Not only does it have complex adaptive behaviors, but also a uniquely powerful genetic toolkit, increasingly complete dense connectomic maps of the central nervous system and a rapidly growing set of transcriptomic profiles of cell types. But this also poses a challenge: Given the massive amounts of available data, how are researchers to Find, Access, Integrate and Reuse (FAIR) relevant data in order to develop an integrated anatomical and molecular picture of circuits, inform hypothesis generation, and find reagents for experiments to test these hypotheses? The Virtual Fly Brain (virtualflybrain.org) web application & API provide a solution to this problem, using FAIR principles to integrate 3D images of neurons and brain regions, connectomics, transcriptomics and reagent expression data covering the whole CNS in both larva and adult. Users can search for neurons, neuroanatomy and reagents by name, location, or connectivity, via text search, clicking on 3D images, search-by-image, and queries by type (e.g., dopaminergic neuron) or properties (e.g., synaptic input in the antennal lobe). Returned results include cross-registered 3D images that can be explored in linked 2D and 3D browsers or downloaded under open licenses, and extensive descriptions of cell types and regions curated from the literature. These solutions are potentially extensible to cover similar atlasing and data integration challenges in vertebrates.",
+                "authors": [
+                    {
+                        "name": "Armstrong J.D."
+                    },
+                    {
+                        "name": "Brown N.H."
+                    },
+                    {
+                        "name": "Costa M."
+                    },
+                    {
+                        "name": "Court R."
+                    },
+                    {
+                        "name": "Holmes A."
+                    },
+                    {
+                        "name": "Jefferis G.S.X.E."
+                    },
+                    {
+                        "name": "Kir H."
+                    },
+                    {
+                        "name": "Larkin A."
+                    },
+                    {
+                        "name": "Matentzoglu N."
+                    },
+                    {
+                        "name": "McLachlan A."
+                    },
+                    {
+                        "name": "Millburn G."
+                    },
+                    {
+                        "name": "O'Kane C.J."
+                    },
+                    {
+                        "name": "Osumi-Sutherland D."
+                    },
+                    {
+                        "name": "Parkinson H."
+                    },
+                    {
+                        "name": "Pilgrim C."
+                    }
+                ],
+                "date": "2023-01-26T00:00:00Z",
+                "journal": "Frontiers in Physiology",
+                "title": "Virtual Fly Brain—An interactive atlas of the Drosophila nervous system"
+            },
+            "pmcid": "PMC9908962",
+            "pmid": "36776967"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Literature and language",
+            "uri": "http://edamontology.org/topic_3068"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/virusrecom/virusrecom.biotools.json b/data/virusrecom/virusrecom.biotools.json
new file mode 100644
index 0000000000000..35087c82f2894
--- /dev/null
+++ b/data/virusrecom/virusrecom.biotools.json
@@ -0,0 +1,124 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T12:33:41.438321Z",
+    "biotoolsCURIE": "biotools:virusrecom",
+    "biotoolsID": "virusrecom",
+    "collectionID": [
+        "COVID-19"
+    ],
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "xyge@hnu.edu.cn",
+            "name": "Xing-Yi Ge",
+            "orcidid": "https://orcid.org/0000-0001-9182-1952",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "qiuye@hnu.edu.cn",
+            "name": "Ye Qiu",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Chen-Hui Yang"
+        },
+        {
+            "name": "Sheng-Bao Ye"
+        },
+        {
+            "name": "Xiao-Wei Yu"
+        },
+        {
+            "name": "Zhi-Jian Zhou"
+        }
+    ],
+    "description": "An information-theory-based method for recombination detection of viral lineages and its application on SARS-CoV-2.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Query and retrieval",
+                    "uri": "http://edamontology.org/operation_0224"
+                },
+                {
+                    "term": "Recombination detection",
+                    "uri": "http://edamontology.org/operation_0451"
+                },
+                {
+                    "term": "Standardisation and normalisation",
+                    "uri": "http://edamontology.org/operation_3435"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ZhijianZhou01/virusrecom",
+    "language": [
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-10T12:33:41.440910Z",
+    "license": "LGPL-2.1",
+    "name": "VirusRecom",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC513",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Genomic recombination is an important driving force for viral evolution, and recombination events have been reported for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the Coronavirus Disease 2019 pandemic, which significantly alter viral infectivity and transmissibility. However, it is difficult to identify viral recombination, especially for low-divergence viruses such as SARS-CoV-2, since it is hard to distinguish recombination from in situ mutation. Herein, we applied information theory to viral recombination analysis and developed VirusRecom, a program for efficiently screening recombination events on viral genome. In principle, we considered a recombination event as a transmission process of ``information'' and introduced weighted information content (WIC) to quantify the contribution of recombination to a certain region on viral genome; then, we identified the recombination regions by comparing WICs of different regions. In the benchmark using simulated data, VirusRecom showed a good balance between precision and recall compared to two competing tools, RDP5 and 3SEQ. In the detection of SARS-CoV-2 XE, XD and XF recombinants, VirusRecom providing more accurate positions of recombination regions than RDP5 and 3SEQ. In addition, we encapsulated the VirusRecom program into a command-line-interface software for convenient operation by users. In summary, we developed a novel approach based on information theory to identify viral recombination within highly similar sequences, providing a useful tool for monitoring viral evolution and epidemic control.",
+                "authors": [
+                    {
+                        "name": "Ge X.-Y."
+                    },
+                    {
+                        "name": "Qiu Y."
+                    },
+                    {
+                        "name": "Yang C.-H."
+                    },
+                    {
+                        "name": "Ye S.-B."
+                    },
+                    {
+                        "name": "Yu X.-W."
+                    },
+                    {
+                        "name": "Zhou Z.-J."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "VirusRecom: an information-theory-based method for recombination detection of viral lineages and its application on SARS-CoV-2"
+            },
+            "pmid": "36567622"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "DNA replication and recombination",
+            "uri": "http://edamontology.org/topic_3127"
+        },
+        {
+            "term": "Evolutionary biology",
+            "uri": "http://edamontology.org/topic_3299"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        }
+    ]
+}
diff --git a/data/visiomode/visiomode.biotools.json b/data/visiomode/visiomode.biotools.json
new file mode 100644
index 0000000000000..11095f9758496
--- /dev/null
+++ b/data/visiomode/visiomode.biotools.json
@@ -0,0 +1,95 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T12:28:32.499446Z",
+    "biotoolsCURIE": "biotools:visiomode",
+    "biotoolsID": "visiomode",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Constantinos Eleftheriou"
+        },
+        {
+            "name": "Ian Duguid"
+        },
+        {
+            "name": "Marie Zechner"
+        },
+        {
+            "name": "Thomas Clarke"
+        },
+        {
+            "name": "V. Poon"
+        }
+    ],
+    "description": "An open-source platform for building rodent touchscreen-based behavioral assays.",
+    "editPermission": {
+        "type": "private"
+    },
+    "homepage": "http://www.visiomode.org",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-10T12:28:32.502155Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/DuguidLab/visiomode"
+        }
+    ],
+    "name": "Visiomode",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/j.jneumeth.2022.109779",
+            "metadata": {
+                "abstract": "© 2023 The AuthorsBackground: Touchscreen-based behavioral assays provide a robust method for assessing cognitive behavior in rodents, offering great flexibility and translational potential. The development of touchscreen assays presents a significant programming and mechanical engineering challenge, where commercial solutions can be prohibitively expensive and open-source solutions are underdeveloped, with limited adaptability. New method: Here, we present Visiomode (www.visiomode.org), an open-source platform for building rodent touchscreen-based behavioral tasks. Visiomode leverages the inherent flexibility of touchscreens to offer a simple yet adaptable software and hardware platform. The platform is built on the Raspberry Pi computer combining a web-based interface and powerful plug-in system with an operant chamber that can be adapted to generate a wide range of behavioral tasks. Results: As a proof of concept, we use Visiomode to build both simple stimulus-response and more complex visual discrimination tasks, showing that mice display rapid sensorimotor learning including switching between different motor responses (i.e., nose poke versus reaching). Comparison with existing methods: Commercial solutions are the ‘go to’ for rodent touchscreen behaviors, but the associated costs can be prohibitive, limiting their uptake by the wider neuroscience community. While several open-source solutions have been developed, efforts so far have focused on reducing the cost, rather than promoting ease of use and adaptability. Visiomode addresses these unmet needs providing a low-cost, extensible platform for creating touchscreen tasks. Conclusions: Developing an open-source, rapidly scalable and low-cost platform for building touchscreen-based behavioral assays should increase uptake across the science community and accelerate the investigation of cognition, decision-making and sensorimotor behaviors both in health and disease.",
+                "authors": [
+                    {
+                        "name": "Clarke T."
+                    },
+                    {
+                        "name": "Duguid I."
+                    },
+                    {
+                        "name": "Eleftheriou C."
+                    },
+                    {
+                        "name": "Poon V."
+                    },
+                    {
+                        "name": "Zechner M."
+                    }
+                ],
+                "date": "2023-02-15T00:00:00Z",
+                "journal": "Journal of Neuroscience Methods",
+                "title": "Visiomode: An open-source platform for building rodent touchscreen-based behavioral assays"
+            },
+            "pmid": "36621552"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Medical informatics",
+            "uri": "http://edamontology.org/topic_3063"
+        },
+        {
+            "term": "Neurobiology",
+            "uri": "http://edamontology.org/topic_3304"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        }
+    ]
+}
diff --git a/data/visit/visit.biotools.json b/data/visit/visit.biotools.json
deleted file mode 100644
index 457b4acefa730..0000000000000
--- a/data/visit/visit.biotools.json
+++ /dev/null
@@ -1,29 +0,0 @@
-{
-    "additionDate": "2021-05-27T09:24:40Z",
-    "biotoolsCURIE": "biotools:visit",
-    "biotoolsID": "visit",
-    "description": "VisIt is an Open Source, interactive, scalable, visualization, animation and analysis tool.",
-    "editPermission": {
-        "type": "private"
-    },
-    "function": [
-        {
-            "operation": [
-                {
-                    "term": "Visualisation",
-                    "uri": "http://edamontology.org/operation_0337"
-                }
-            ]
-        }
-    ],
-    "homepage": "https://wci.llnl.gov/simulation/computer-codes/visit",
-    "lastUpdate": "2022-12-09T23:52:25.998587Z",
-    "name": "VisIt",
-    "owner": "Kigaard",
-    "topic": [
-        {
-            "term": "Zoology",
-            "uri": "http://edamontology.org/topic_3500"
-        }
-    ]
-}
diff --git a/data/vital_sqi/vital_sqi.biotools.json b/data/vital_sqi/vital_sqi.biotools.json
new file mode 100644
index 0000000000000..a8749b79f8c79
--- /dev/null
+++ b/data/vital_sqi/vital_sqi.biotools.json
@@ -0,0 +1,141 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-24T15:41:50.410913Z",
+    "biotoolsCURIE": "biotools:vital_sqi",
+    "biotoolsID": "vital_sqi",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "khoaldv@oucru.org",
+            "name": "Van-Khoa D. Le",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "David Clifton"
+        },
+        {
+            "name": "Hai Bich Ho"
+        },
+        {
+            "name": "Pantelis Georgiou"
+        }
+    ],
+    "description": "Signal quality control pipeline for electrocardiogram and photoplethysmogram.",
+    "documentation": [
+        {
+            "type": [
+                "General",
+                "User manual"
+            ],
+            "url": "https://vitalsqi.readthedocs.io/en/latest/?badge=latest"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                },
+                {
+                    "term": "Splitting",
+                    "uri": "http://edamontology.org/operation_3359"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://pypi.org/project/vital-sqi/",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-24T15:41:50.413468Z",
+    "license": "MIT",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/meta00/vital_sqi"
+        }
+    ],
+    "name": "vital_sqi",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FPHYS.2022.1020458",
+            "metadata": {
+                "abstract": "Copyright © 2022 Le, Ho, Karolcik, Hernandez, Greeff, Nguyen, Phan, Le, Thwaites, Georgiou and Clifton.Electrocardiogram (ECG) and photoplethysmogram (PPG) are commonly used to determine the vital signs of heart rate, respiratory rate, and oxygen saturation in patient monitoring. In addition to simple observation of those summarized indexes, waveform signals can be analyzed to provide deeper insights into disease pathophysiology and support clinical decisions. Such data, generated from continuous patient monitoring from both conventional bedside and low-cost wearable monitors, are increasingly accessible. However, the recorded waveforms suffer from considerable noise and artifacts and, hence, are not necessarily used prior to certain quality control (QC) measures, especially by those with limited programming experience. Various signal quality indices (SQIs) have been proposed to indicate signal quality. To facilitate and harmonize a wider usage of SQIs in practice, we present a Python package, named vital_sqi, which provides a unified interface to the state-of-the-art SQIs for ECG and PPG signals. The vital_sqi package provides with seven different peak detectors and access to more than 70 SQIs by using different settings. The vital_sqi package is designed with pipelines and graphical user interfaces to enable users of various programming fluency to use the package. Multiple SQI extraction pipelines can take the PPG and ECG waveforms and generate a bespoke SQI table. As these SQI scores represent the signal features, they can be input in any quality classifier. The package provides functions to build simple rule-based decision systems for signal segment quality classification using user-defined SQI thresholds. An experiment with a carefully annotated PPG dataset suggests thresholds for relevant PPG SQIs.",
+                "authors": [
+                    {
+                        "name": "Clifton D."
+                    },
+                    {
+                        "name": "Georgiou P."
+                    },
+                    {
+                        "name": "Greeff H."
+                    },
+                    {
+                        "name": "Hernandez B."
+                    },
+                    {
+                        "name": "Ho H.B."
+                    },
+                    {
+                        "name": "Karolcik S."
+                    },
+                    {
+                        "name": "Le T.P."
+                    },
+                    {
+                        "name": "Le V.-K.D."
+                    },
+                    {
+                        "name": "Nguyen V.H."
+                    },
+                    {
+                        "name": "Phan N.Q.K."
+                    },
+                    {
+                        "name": "Thwaites L."
+                    }
+                ],
+                "date": "2022-11-11T00:00:00Z",
+                "journal": "Frontiers in Physiology",
+                "title": "vital_sqi: A Python package for physiological signal quality control"
+            },
+            "pmcid": "PMC9692103",
+            "pmid": "36439252"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/vitviz/vitviz.biotools.json b/data/vitviz/vitviz.biotools.json
new file mode 100644
index 0000000000000..8c4441f0288bf
--- /dev/null
+++ b/data/vitviz/vitviz.biotools.json
@@ -0,0 +1,109 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-24T15:35:14.493636Z",
+    "biotoolsCURIE": "biotools:vitviz",
+    "biotoolsID": "vitviz",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "stefania.savoi@unito.it",
+            "name": "Stefania Savoi",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "tomas.matus@uv.es",
+            "name": "José Tomás Matus",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Antonio Santiago"
+        },
+        {
+            "name": "Luis Orduña"
+        }
+    ],
+    "description": "Transcriptomic and metabolomic integration as a resource in grapevine to study fruit metabolite quality traits.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Deposition",
+                    "uri": "http://edamontology.org/operation_3431"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Visualisation",
+                    "uri": "http://edamontology.org/operation_0337"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://tomsbiolab.com/vitviz",
+    "lastUpdate": "2023-01-24T15:35:14.496536Z",
+    "name": "VitViz",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.3389/FPLS.2022.937927",
+            "metadata": {
+                "abstract": "Copyright © 2022 Savoi, Santiago, Orduña and Matus.Transcriptomics and metabolomics are methodologies being increasingly chosen to perform molecular studies in grapevine (Vitis vinifera L.), focusing either on plant and fruit development or on interaction with abiotic or biotic factors. Currently, the integration of these approaches has become of utmost relevance when studying key plant physiological and metabolic processes. The results from these analyses can undoubtedly be incorporated in breeding programs whereby genes associated with better fruit quality (e.g., those enhancing the accumulation of health-promoting compounds) or with stress resistance (e.g., those regulating beneficial responses to environmental transition) can be used as selection markers in crop improvement programs. Despite the vast amount of data being generated, integrative transcriptome/metabolome meta-analyses (i.e., the joint analysis of several studies) have not yet been fully accomplished in this species, mainly due to particular specificities of metabolomic studies, such as differences in data acquisition (i.e., different compounds being investigated), unappropriated and unstandardized metadata, or simply no deposition of data in public repositories. These meta-analyses require a high computational capacity for data mining a priori, but they also need appropriate tools to explore and visualize the integrated results. This perspective article explores the universe of omics studies conducted in V. vinifera, focusing on fruit-transcriptome and metabolome analyses as leading approaches to understand berry physiology, secondary metabolism, and quality. Moreover, we show how omics data can be integrated in a simple format and offered to the research community as a web resource, giving the chance to inspect potential gene-to-gene and gene-to-metabolite relationships that can later be tested in hypothesis-driven research. In the frame of the activities promoted by the COST Action CA17111 INTEGRAPE, we present the first grapevine transcriptomic and metabolomic integrated database (TransMetaDb) developed within the Vitis Visualization (VitViz) platform (https://tomsbiolab.com/vitviz). This tool also enables the user to conduct and explore meta-analyses utilizing different experiments, therefore hopefully motivating the community to generate Findable, Accessible, Interoperable and Reusable (F.A.I.R.) data to be included in the future.",
+                "authors": [
+                    {
+                        "name": "Matus J.T."
+                    },
+                    {
+                        "name": "Orduna L."
+                    },
+                    {
+                        "name": "Santiago A."
+                    },
+                    {
+                        "name": "Savoi S."
+                    }
+                ],
+                "date": "2022-10-20T00:00:00Z",
+                "journal": "Frontiers in Plant Science",
+                "title": "Transcriptomic and metabolomic integration as a resource in grapevine to study fruit metabolite quality traits"
+            },
+            "pmcid": "PMC9630917",
+            "pmid": "36340350"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Metabolomics",
+            "uri": "http://edamontology.org/topic_3172"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Small molecules",
+            "uri": "http://edamontology.org/topic_0154"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/vpatho/vpatho.biotools.json b/data/vpatho/vpatho.biotools.json
new file mode 100644
index 0000000000000..487176af7f1d8
--- /dev/null
+++ b/data/vpatho/vpatho.biotools.json
@@ -0,0 +1,146 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T12:16:23.410247Z",
+    "biotoolsCURIE": "biotools:vpatho",
+    "biotoolsID": "vpatho",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "jiangning.song@monash.edu",
+            "name": "Jiangning Song",
+            "orcidid": "https://orcid.org/0000-0001-8031-9086",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "njyudj@njust.edu.cn",
+            "name": "Dong-Jun Yu",
+            "orcidid": "https://orcid.org/0000-0002-6786-8053",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Fang Ge"
+        },
+        {
+            "name": "Chen Li",
+            "orcidid": "https://orcid.org/0000-0002-1847-754X"
+        }
+    ],
+    "description": "A deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Identifier",
+                        "uri": "http://edamontology.org/data_0842"
+                    },
+                    "format": [
+                        {
+                            "term": "VCF",
+                            "uri": "http://edamontology.org/format_3016"
+                        }
+                    ]
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Variant effect prediction",
+                    "uri": "http://edamontology.org/operation_0331"
+                },
+                {
+                    "term": "Variant prioritisation",
+                    "uri": "http://edamontology.org/operation_3226"
+                },
+                {
+                    "term": "Virulence prediction",
+                    "uri": "http://edamontology.org/operation_3461"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://csbio.njust.edu.cn/bioinf/vpatho/",
+    "lastUpdate": "2023-02-10T12:16:23.412924Z",
+    "name": "VPatho",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIB/BBAC535",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Determining the pathogenicity and functional impact (i.e. gain-of-function; GOF or loss-of-function; LOF) of a variant is vital for unraveling the genetic level mechanisms of human diseases. To provide a 'one-stop' framework for the accurate identification of pathogenicity and functional impact of variants, we developed a two-stage deep-learning-based computational solution, termed VPatho, which was trained using a total of 9619 pathogenic GOF/LOF and 138 026 neutral variants curated from various databases. A total number of 138 variant-level, 262 protein-level and 103 genome-level features were extracted for constructing the models of VPatho. The development of VPatho consists of two stages: (i) a random under-sampling multi-scale residual neural network (ResNet) with a newly defined weighted-loss function (RUS-Wg-MSResNet) was proposed to predict variants' pathogenicity on the gnomAD_NV + GOF/LOF dataset; and (ii) an XGBOD model was constructed to predict the functional impact of the given variants. Benchmarking experiments demonstrated that RUS-Wg-MSResNet achieved the highest prediction performance with the weights calculated based on the ratios of neutral versus pathogenic variants. Independent tests showed that both RUS-Wg-MSResNet and XGBOD achieved outstanding performance. Moreover, assessed using variants from the CAGI6 competition, RUS-Wg-MSResNet achieved superior performance compared to state-of-the-art predictors. The fine-trained XGBOD models were further used to blind test the whole LOF data downloaded from gnomAD and accordingly, we identified 31 nonLOF variants that were previously labeled as LOF/uncertain variants. As an implementation of the developed approach, a webserver of VPatho is made publicly available at http://csbio.njust.edu.cn/bioinf/vpatho/ to facilitate community-wide efforts for profiling and prioritizing the query variants with respect to their pathogenicity and functional impact.",
+                "authors": [
+                    {
+                        "name": "Ge F."
+                    },
+                    {
+                        "name": "Iqbal S."
+                    },
+                    {
+                        "name": "Li C."
+                    },
+                    {
+                        "name": "Li F."
+                    },
+                    {
+                        "name": "Muhammad A."
+                    },
+                    {
+                        "name": "Song J."
+                    },
+                    {
+                        "name": "Thafar M.A."
+                    },
+                    {
+                        "name": "Worachartcheewan A."
+                    },
+                    {
+                        "name": "Xu X."
+                    },
+                    {
+                        "name": "Yan Z."
+                    },
+                    {
+                        "name": "Yu D.-J."
+                    }
+                ],
+                "date": "2023-01-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants"
+            },
+            "pmid": "36528806"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Exome sequencing",
+            "uri": "http://edamontology.org/topic_3676"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Infectious disease",
+            "uri": "http://edamontology.org/topic_3324"
+        },
+        {
+            "term": "Machine learning",
+            "uri": "http://edamontology.org/topic_3474"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        }
+    ]
+}
diff --git a/data/vsl2/vsl2.biotools.json b/data/vsl2/vsl2.biotools.json
deleted file mode 100644
index a8e402d34edce..0000000000000
--- a/data/vsl2/vsl2.biotools.json
+++ /dev/null
@@ -1,73 +0,0 @@
-{
-    "accessibility": "Open access",
-    "additionDate": "2022-08-31T10:04:03.014683Z",
-    "biotoolsCURIE": "biotools:vsl2",
-    "biotoolsID": "vsl2",
-    "cost": "Free of charge",
-    "credit": [
-        {
-            "email": "zoran@ist.temple.edu",
-            "name": "Zoran Obradovic"
-        }
-    ],
-    "description": "VSL2 is a predictor model that is applicable to disordered regions of any length and can accurately identify the short disordered regions that are often misclassified by other disorder predictors.",
-    "editPermission": {
-        "authors": [
-            "damiano.piovesan",
-            "tlazar"
-        ],
-        "type": "group"
-    },
-    "function": [
-        {
-            "operation": [
-                {
-                    "term": "Protein disorder prediction",
-                    "uri": "http://edamontology.org/operation_3904"
-                }
-            ]
-        }
-    ],
-    "homepage": "http://www.ist.temple.edu/disprot/predictorVSL2.php",
-    "lastUpdate": "2022-12-09T23:53:44.772424Z",
-    "name": "VSL2",
-    "operatingSystem": [
-        "Linux"
-    ],
-    "owner": "daniela.mereuta",
-    "publication": [
-        {
-            "doi": "10.1186/1471-2105-7-208",
-            "metadata": {
-                "abstract": "Background: Due to the functional importance of intrinsically disordered proteins or protein regions, prediction of intrinsic protein disorder from amino acid sequence has become an area of active research as witnessed in the 6th experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Since the initial work by Romero et al. (Identifying disordered regions in proteins from amino acid sequences, IEEE Int. Conf. Neural Netw., 1997), our group has developed several predictors optimized for long disordered regions (>30 residues) with prediction accuracy exceeding 85%. However, these predictors are less successful on short disordered regions (≤30 residues). A probable cause is a length-dependent amino acid compositions and sequence properties of disordered regions. Results: We proposed two new predictor models, VSL2-M1 and VSL2-M2, to address this length-dependency problem in prediction of intrinsic protein disorder. These two predictors are similar to the original VSL1 predictor used in the CASP6 experiment. In both models, two specialized predictors were first built and optimized for short (≤30 residues) and long disordered regions (>30 residues), respectively. A meta predictor was then trained to integrate the specialized predictors into the final predictor model. As the 10-fold cross-validation results showed, the VSL2 predictors achieved well-balanced prediction accuracies of 81% on both short and long disordered regions. Comparisons over the VSL2 training dataset via 10-fold cross-validation and a blind-test set of unrelated recent PDB chains indicated that VSL2 predictors were significantly more accurate than several existing predictors of intrinsic protein disorder. Conclusion: The VSL2 predictors are applicable to disordered regions of any length and can accurately identify the short disordered regions that are often misclassified by our previous disorder predictors. The success of the VSL2 predictors further confirmed the previously observed differences in amino acid compositions and sequence properties between short and long disordered regions, and justified our approaches for modelling short and long disordered regions separately. The VSL2 predictors are freely accessible for non-commercial use at http://www.ist.temple.edu/disprot/predictorVSL2.php. © 2006 Peng et al; licensee BioMed Central Ltd.",
-                "authors": [
-                    {
-                        "name": "Dunker A.K."
-                    },
-                    {
-                        "name": "Obradovic Z."
-                    },
-                    {
-                        "name": "Peng K."
-                    },
-                    {
-                        "name": "Radivojac P."
-                    },
-                    {
-                        "name": "Vucetic S."
-                    }
-                ],
-                "citationCount": 675,
-                "date": "2006-04-17T00:00:00Z",
-                "journal": "BMC Bioinformatics",
-                "title": "Length-dependent prediction of protein in intrinsic disorder"
-            },
-            "type": [
-                "Primary"
-            ]
-        }
-    ],
-    "toolType": [
-        "Command-line tool"
-    ]
-}
diff --git a/data/vsrnafinder/vsrnafinder.biotools.json b/data/vsrnafinder/vsrnafinder.biotools.json
new file mode 100644
index 0000000000000..aa7cbf2f93dc3
--- /dev/null
+++ b/data/vsrnafinder/vsrnafinder.biotools.json
@@ -0,0 +1,128 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-26T09:42:23.566893Z",
+    "biotoolsCURIE": "biotools:vsrnafinder",
+    "biotoolsID": "vsrnafinder",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "pys2013@hnu.edu.cn",
+            "name": "Yousong Peng",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Haizhen Zhu"
+        },
+        {
+            "name": "Ping Fu"
+        },
+        {
+            "name": "Zena Cai"
+        }
+    ],
+    "description": "A novel method for identifying high-confidence viral small RNAs from small RNA-Seq data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Peak calling",
+                    "uri": "http://edamontology.org/operation_3222"
+                },
+                {
+                    "term": "Quantification",
+                    "uri": "http://edamontology.org/operation_3799"
+                },
+                {
+                    "term": "Sequence trimming",
+                    "uri": "http://edamontology.org/operation_3192"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/ZenaCai/vsRNAfinder",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-02-26T09:42:23.569429Z",
+    "license": "Not licensed",
+    "name": "vsRNAfinder",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bib/bbac496",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Virus-encoded small RNAs (vsRNA) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of an effective method for vsRNA identification. Herein, we presented vsRNAfinder, a de novo method for identifying high-confidence vsRNAs from small RNA-Seq (sRNA-Seq) data based on peak calling and Poisson distribution and is publicly available at https://github.com/ZenaCai/vsRNAfinder. vsRNAfinder outperformed two widely used methods namely miRDeep2 and ShortStack in identifying viral miRNAs with a significantly improved sensitivity. It can also be used to identify sRNAs in animals and plants with similar performance to miRDeep2 and ShortStack. vsRNAfinder would greatly facilitate effective identification of vsRNAs from sRNA-Seq data.",
+                "authors": [
+                    {
+                        "name": "Cai Z."
+                    },
+                    {
+                        "name": "Fu P."
+                    },
+                    {
+                        "name": "Ge X.-Y."
+                    },
+                    {
+                        "name": "Jiang T."
+                    },
+                    {
+                        "name": "Peng Y."
+                    },
+                    {
+                        "name": "Qiu Y."
+                    },
+                    {
+                        "name": "Wang Y."
+                    },
+                    {
+                        "name": "Wu A."
+                    },
+                    {
+                        "name": "Zhang G."
+                    },
+                    {
+                        "name": "Zhu H."
+                    }
+                ],
+                "date": "2022-11-19T00:00:00Z",
+                "journal": "Briefings in bioinformatics",
+                "title": "vsRNAfinder: a novel method for identifying high-confidence viral small RNAs from small RNA-Seq data"
+            },
+            "pmid": "36377755"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Functional, regulatory and non-coding RNA",
+            "uri": "http://edamontology.org/topic_0659"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Plant biology",
+            "uri": "http://edamontology.org/topic_0780"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Zoology",
+            "uri": "http://edamontology.org/topic_3500"
+        }
+    ]
+}
diff --git a/data/vulcan_mapper/vulcan_mapper.biotools.json b/data/vulcan_mapper/vulcan_mapper.biotools.json
index 5195e597a3832..c71808c0c63c4 100644
--- a/data/vulcan_mapper/vulcan_mapper.biotools.json
+++ b/data/vulcan_mapper/vulcan_mapper.biotools.json
@@ -1,7 +1,10 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2021-08-23T17:59:56Z",
     "biotoolsCURIE": "biotools:vulcan_mapper",
     "biotoolsID": "vulcan_mapper",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "Improved long-read mapping and structural variant calling via dual-mode alignment",
     "editPermission": {
         "type": "private"
@@ -39,9 +42,21 @@
         }
     ],
     "homepage": "https://gitlab.com/treangenlab/vulcan",
-    "lastUpdate": "2022-12-09T23:56:53.813295Z",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-10T13:55:45.761138Z",
+    "license": "MIT",
     "name": "Vulcan",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "yilei_fu",
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "Mapping",
diff --git a/data/watchdog/watchdog.biotools.json b/data/watchdog/watchdog.biotools.json
index 0af3296152733..19f53106ce21e 100644
--- a/data/watchdog/watchdog.biotools.json
+++ b/data/watchdog/watchdog.biotools.json
@@ -1,15 +1,58 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2020-04-24T15:51:24Z",
     "biotoolsCURIE": "biotools:watchdog",
     "biotoolsID": "watchdog",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "Workflow management system for the automated and distributed analysis of large-scale experimental data",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "https://klugem.github.io/watchdog/Watchdog-manual.html"
+        }
+    ],
     "editPermission": {
         "type": "private"
     },
     "homepage": "https://www.bio.ifi.lmu.de/watchdog",
-    "lastUpdate": "2022-12-09T23:58:20.392967Z",
+    "language": [
+        "Java"
+    ],
+    "lastUpdate": "2023-01-10T13:45:54.299616Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://anaconda.org/bioconda/watchdog-wms"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/klugem/watchdog"
+        },
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://hub.docker.com/r/klugem/watchdog-wms/"
+        }
+    ],
     "name": "Watchdog",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "CarolineFriedel",
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "Workflows",
diff --git a/data/weka/weka.biotools.json b/data/weka/weka.biotools.json
index 969bb38cc4e65..4a36317955414 100644
--- a/data/weka/weka.biotools.json
+++ b/data/weka/weka.biotools.json
@@ -3,6 +3,9 @@
     "additionDate": "2022-05-16T13:45:19.595307Z",
     "biotoolsCURIE": "biotools:weka",
     "biotoolsID": "weka",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
     "confidence_flag": "tool",
     "cost": "Free of charge",
     "credit": [
@@ -12,7 +15,10 @@
     ],
     "description": "Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.",
     "editPermission": {
-        "type": "private"
+        "authors": [
+            "iacs-biocomputacion"
+        ],
+        "type": "group"
     },
     "function": [
         {
@@ -33,7 +39,7 @@
         }
     ],
     "homepage": "https://www.cs.waikato.ac.nz/ml/weka/",
-    "lastUpdate": "2022-05-16T13:45:19.597510Z",
+    "lastUpdate": "2023-02-01T12:59:16.256272Z",
     "name": "Weka",
     "operatingSystem": [
         "Linux",
@@ -68,6 +74,7 @@
             "uri": "http://edamontology.org/topic_3500"
         }
     ],
+    "validated": 1,
     "version": [
         "3"
     ]
diff --git a/data/wenda_gpu/wenda_gpu.biotools.json b/data/wenda_gpu/wenda_gpu.biotools.json
new file mode 100644
index 0000000000000..5d107ca47d631
--- /dev/null
+++ b/data/wenda_gpu/wenda_gpu.biotools.json
@@ -0,0 +1,117 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-20T01:28:37.565416Z",
+    "biotoolsCURIE": "biotools:wenda_gpu",
+    "biotoolsID": "wenda_gpu",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Jacob R. Gardner"
+        },
+        {
+            "name": "Ariel A. Hippen",
+            "orcidid": "http://orcid.org/0000-0001-9336-6543"
+        },
+        {
+            "name": "Casey S. Greene",
+            "orcidid": "http://orcid.org/0000-0001-8713-9213"
+        },
+        {
+            "name": "Jake Crawford",
+            "orcidid": "http://orcid.org/0000-0001-6207-0782"
+        }
+    ],
+    "description": "Fast domain adaptation method for building prediction models on genomic data.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Clustering",
+                    "uri": "http://edamontology.org/operation_3432"
+                },
+                {
+                    "term": "Fold recognition",
+                    "uri": "http://edamontology.org/operation_0303"
+                },
+                {
+                    "term": "PTM site prediction",
+                    "uri": "http://edamontology.org/operation_0417"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/greenelab/wenda_gpu/",
+    "language": [
+        "Python",
+        "Shell"
+    ],
+    "lastUpdate": "2023-01-20T01:28:37.567973Z",
+    "license": "BSD-3-Clause",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://github.com/greenelab/wenda_gpu_paper/"
+        }
+    ],
+    "name": "wenda_gpu",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/bioinformatics/btac663",
+            "metadata": {
+                "abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: Domain adaptation allows for the development of predictive models even in cases with limited sample data. Weighted elastic net domain adaptation specifically leverages features of genomic data to maximize transferability but the method is too computationally demanding to apply to many genome-sized datasets. RESULTS: We developed wenda_gpu, which uses GPyTorch to train models on genomic data within hours on a single GPU-enabled machine. We show that wenda_gpu returns comparable results to the original wenda implementation, and that it can be used for improved prediction of cancer mutation status on small sample sizes than regular elastic net. AVAILABILITY AND IMPLEMENTATION: wenda_gpu is available on GitHub at https://github.com/greenelab/wenda_gpu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Crawford J."
+                    },
+                    {
+                        "name": "Gardner J.R."
+                    },
+                    {
+                        "name": "Greene C.S."
+                    },
+                    {
+                        "name": "Hippen A.A."
+                    }
+                ],
+                "date": "2022-11-15T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "wenda_gpu: fast domain adaptation for genomic data"
+            },
+            "pmcid": "PMC9665854",
+            "pmid": "36193991"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Bioinformatics",
+            "uri": "http://edamontology.org/topic_0091"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/wgdtree/wgdtree.biotools.json b/data/wgdtree/wgdtree.biotools.json
new file mode 100644
index 0000000000000..17fae7c0c6ea0
--- /dev/null
+++ b/data/wgdtree/wgdtree.biotools.json
@@ -0,0 +1,117 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-24T15:27:22.780978Z",
+    "biotoolsCURIE": "biotools:wgdtree",
+    "biotoolsID": "wgdtree",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "daliberles@temple.edu",
+            "name": "David A. Liberles",
+            "orcidid": "https://orcid.org/0000-0003-3487-8826",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "C. Nicholas Henry"
+        },
+        {
+            "name": "Claire S. Probst"
+        },
+        {
+            "name": "Yuying Rong"
+        }
+    ],
+    "description": "A phylogenetic software tool to examine conditional probabilities of retention following whole genome duplication events.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene tree construction",
+                    "uri": "http://edamontology.org/operation_0553"
+                },
+                {
+                    "term": "Phylogenetic tree reconciliation",
+                    "uri": "http://edamontology.org/operation_3947"
+                },
+                {
+                    "term": "Species tree construction",
+                    "uri": "http://edamontology.org/operation_0544"
+                },
+                {
+                    "term": "Tree dating",
+                    "uri": "http://edamontology.org/operation_3942"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/cnickh/wgdtree",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-24T15:27:22.783680Z",
+    "license": "MIT",
+    "name": "WGDTree",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12859-022-05042-W",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: Multiple processes impact the probability of retention of individual genes following whole genome duplication (WGD) events. In analyzing two consecutive whole genome duplication events that occurred in the lineage leading to Atlantic salmon, a new phylogenetic statistical analysis was developed to examine the contingency of retention in one event based upon retention in a previous event. This analysis is intended to evaluate mechanisms of duplicate gene retention and to provide software to generate the test statistic for any genome with pairs of WGDs in its history. Results: Here a software package written in Python, ‘WGDTree’ for the analysis of duplicate gene retention following whole genome duplication events is presented. Using gene tree-species tree reconciliation to label gene duplicate nodes and differentiate between WGD and SSD duplicates, the tool calculates a statistic based upon the conditional probability of a gene duplicate being retained after a second whole genome duplication dependent upon the retention status after the first event. The package also contains methods for the simulation of gene trees with WGD events. After running simulations, the accuracy of the placement of events has been determined to be high. The conditional probability statistic has been calculated for Phalaenopsis equestris on a monocot species tree with a pair of consecutive WGD events on its lineage, showing the applicability of the method. Conclusions: A new software tool has been created for the analysis of duplicate genes in examination of retention mechanisms. The software tool has been made available on the Python package index and the source code can be found on GitHub here: https://github.com/cnickh/wgdtree.",
+                "authors": [
+                    {
+                        "name": "Henry C.N."
+                    },
+                    {
+                        "name": "Liberles D.A."
+                    },
+                    {
+                        "name": "Miraszek J.L."
+                    },
+                    {
+                        "name": "Piper K."
+                    },
+                    {
+                        "name": "Probst C.S."
+                    },
+                    {
+                        "name": "Rong Y."
+                    },
+                    {
+                        "name": "Wilson A.E."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Bioinformatics",
+                "title": "WGDTree: a phylogenetic software tool to examine conditional probabilities of retention following whole genome duplication events"
+            },
+            "pmcid": "PMC9701042",
+            "pmid": "36434497"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Genomics",
+            "uri": "http://edamontology.org/topic_0622"
+        },
+        {
+            "term": "Phylogenetics",
+            "uri": "http://edamontology.org/topic_3293"
+        },
+        {
+            "term": "Statistics and probability",
+            "uri": "http://edamontology.org/topic_2269"
+        }
+    ]
+}
diff --git a/data/wgrn/wgrn.biotools.json b/data/wgrn/wgrn.biotools.json
new file mode 100644
index 0000000000000..a2162967b1f06
--- /dev/null
+++ b/data/wgrn/wgrn.biotools.json
@@ -0,0 +1,84 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T12:11:49.705155Z",
+    "biotoolsCURIE": "biotools:wgrn",
+    "biotoolsID": "wgrn",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Close Weilong Guo"
+        },
+        {
+            "name": "Huiru Peng"
+        },
+        {
+            "name": "Yiwen Guo"
+        },
+        {
+            "name": "Yongming Chen"
+        }
+    ],
+    "description": "A wheat integrative regulatory network from large-scale complementary functional datasets enables trait-associated gene discovery for crop improvement.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene ID",
+                        "uri": "http://edamontology.org/data_2295"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Gene functional annotation",
+                    "uri": "http://edamontology.org/operation_3672"
+                },
+                {
+                    "term": "Gene regulatory network analysis",
+                    "uri": "http://edamontology.org/operation_1781"
+                },
+                {
+                    "term": "Gene regulatory network prediction",
+                    "uri": "http://edamontology.org/operation_2437"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://wheat.cau.edu.cn/wGRN",
+    "lastUpdate": "2023-02-10T12:11:49.707862Z",
+    "name": "wGRN",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.MOLP.2022.12.019",
+            "pmid": "36575796"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene regulation",
+            "uri": "http://edamontology.org/topic_0204"
+        },
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        }
+    ]
+}
diff --git a/data/wgsim/wgsim.biotools.json b/data/wgsim/wgsim.biotools.json
index 8b65b595ae275..b02a2424fe686 100644
--- a/data/wgsim/wgsim.biotools.json
+++ b/data/wgsim/wgsim.biotools.json
@@ -1,15 +1,26 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2021-04-22T01:04:54Z",
     "biotoolsCURIE": "biotools:wgsim",
     "biotoolsID": "wgsim",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "Wgsim is a small tool for simulating sequence reads from a reference genome.\nIt is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL)\npolymorphisms, and simulate reads with uniform substitution sequencing errors.\nIt does not generate INDEL sequencing errors, but this can be partly\ncompensated by simulating INDEL polymorphisms.\n\nWgsim outputs the simulated polymorphisms, and writes the true read coordinates\nas well as the number of polymorphisms and sequencing errors in read names.\nOne can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl\nthat comes with the package.",
     "editPermission": {
         "type": "private"
     },
     "homepage": "https://github.com/lh3/wgsim",
-    "lastUpdate": "2022-12-09T23:59:21.316937Z",
+    "language": [
+        "C",
+        "Perl"
+    ],
+    "lastUpdate": "2023-01-10T13:41:28.362051Z",
+    "license": "Not licensed",
     "name": "wgsim",
     "owner": "leipzig",
+    "toolType": [
+        "Command-line tool"
+    ],
     "topic": [
         {
             "term": "DNA polymorphism",
diff --git a/data/whatismygene/whatismygene.biotools.json b/data/whatismygene/whatismygene.biotools.json
index e5c66686558bc..129e7853d5869 100644
--- a/data/whatismygene/whatismygene.biotools.json
+++ b/data/whatismygene/whatismygene.biotools.json
@@ -1,15 +1,26 @@
 {
+    "accessibility": "Open access",
     "additionDate": "2022-07-26T10:48:58.836208Z",
     "biotoolsCURIE": "biotools:whatismygene",
     "biotoolsID": "whatismygene",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
     "description": "Web-based gene enrichment tools based on a huge underlying database of transcriptomic, proteomic (and other-omic) studies.",
     "editPermission": {
         "type": "private"
     },
     "homepage": "https://whatismygene.com",
-    "lastUpdate": "2022-12-10T00:01:07.198150Z",
+    "lastUpdate": "2023-01-10T13:39:03.987777Z",
     "name": "Whatismygene",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
     "owner": "whatismygene",
+    "toolType": [
+        "Database portal"
+    ],
     "topic": [
         {
             "term": "Proteomics",
diff --git a/data/white_rabbit/white_rabbit.biotools.json b/data/white_rabbit/white_rabbit.biotools.json
new file mode 100644
index 0000000000000..127b7f0f1a8d8
--- /dev/null
+++ b/data/white_rabbit/white_rabbit.biotools.json
@@ -0,0 +1,53 @@
+{
+    "additionDate": "2023-01-25T11:01:19.528168Z",
+    "biotoolsCURIE": "biotools:white_rabbit",
+    "biotoolsID": "white_rabbit",
+    "collectionID": [
+        "IMPaCT-Data"
+    ],
+    "credit": [
+        {
+            "name": "Observational Health Data Sciences and Informatics OHDSI",
+            "typeEntity": "Consortium"
+        }
+    ],
+    "description": "WhiteRabbit is a software tool to help prepare for ETLs (Extraction, Transformation, Loading) of longitudinal healthcare databases into the OMOP Common Data Model (CDM). The source data can be in comma-separated text files, or in a database (MySQL, SQL Server, ORACLE, PostgreSQL); the CDM will be in a database (MySQL, SQL Server, PostgreSQL).\n\nThe main function of WhiteRabbit is to perform a scan of the source data, providing detailed information on the tables, fields, and values that appear in a field. This scan will generate a report that can be used as a reference when designing the ETL, for instance when using the Rabbit-In-a-Hat tool. Rabbit-In-a-Hat uses the scan document and displays source data information through a graphical user interface to allow a user to connect source data structure to the CDM data structure. The function of Rabbit-In-a-Hat is to generate documentation for the ETL process, not generate code to create an ETL.",
+    "download": [
+        {
+            "type": "Downloads page",
+            "url": "https://github.com/OHDSI/WhiteRabbit"
+        }
+    ],
+    "editPermission": {
+        "authors": [
+            "iacs-biocomputacion"
+        ],
+        "type": "public"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data handling",
+                    "uri": "http://edamontology.org/operation_2409"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://ohdsi.github.io/WhiteRabbit/",
+    "lastUpdate": "2023-02-01T12:05:20.887615Z",
+    "license": "Apache-2.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://www.ohdsi.org/analytic-tools/whiterabbit-for-etl-design/"
+        }
+    ],
+    "name": "White Rabbit",
+    "owner": "iacs-biocomputacion",
+    "version": [
+        "v0.10.7"
+    ]
+}
diff --git a/data/wmds.net/wmds.net.biotools.json b/data/wmds.net/wmds.net.biotools.json
new file mode 100644
index 0000000000000..814cd25cf348b
--- /dev/null
+++ b/data/wmds.net/wmds.net.biotools.json
@@ -0,0 +1,134 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-03T22:37:10.922634Z",
+    "biotoolsCURIE": "biotools:wmds.net",
+    "biotoolsID": "wmds.net",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "pyliu@zju.edu.cn",
+            "name": "Pengyuan Liu",
+            "orcidid": "https://orcid.org/0000-0002-8386-8416",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "yanlu76@zju.edu.cn",
+            "name": "Yan Lu",
+            "orcidid": "https://orcid.org/0000-0002-5004-4656",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Md Amanullah"
+        },
+        {
+            "name": "Xiang Cheng"
+        }
+    ],
+    "description": "A network control framework for identifying key players in transcriptome programs.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Expression correlation analysis",
+                    "uri": "http://edamontology.org/operation_3463"
+                },
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Network analysis",
+                    "uri": "http://edamontology.org/operation_3927"
+                },
+                {
+                    "term": "Validation",
+                    "uri": "http://edamontology.org/operation_2428"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/chaofen123/WMDS.net",
+    "language": [
+        "MATLAB"
+    ],
+    "lastUpdate": "2023-03-03T22:37:10.925164Z",
+    "license": "Not licensed",
+    "name": "WMDS.net",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1093/BIOINFORMATICS/BTAD071",
+            "metadata": {
+                "abstract": "MOTIVATION: Mammalian cells can be transcriptionally reprogramed to other cellular phenotypes. Controllability of such complex transitions in transcriptional networks underlying cellular phenotypes is an inherent biological characteristic. This network controllability can be interpreted by operating a few key regulators to guide the transcriptional program from one state to another. Finding the key regulators in the transcriptional program can provide key insights into the network state transition underlying cellular phenotypes. RESULTS: To address this challenge, here, we proposed to identify the key regulators in the transcriptional co-expression network as a minimum dominating set (MDS) of driver nodes that can fully control the network state transition. Based on the theory of structural controllability, we developed a weighted MDS network model (WMDS.net) to find the driver nodes of differential gene co-expression networks. The weight of WMDS.net integrates the degree of nodes in the network and the significance of gene co-expression difference between two physiological states into the measurement of node controllability of the transcriptional network. To confirm its validity, we applied WMDS.net to the discovery of cancer driver genes in RNA-seq datasets from The Cancer Genome Atlas. WMDS.net is powerful among various cancer datasets and outperformed the other top-tier tools with a better balance between precision and recall. AVAILABILITY AND IMPLEMENTATION: https://github.com/chaofen123/WMDS.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
+                "authors": [
+                    {
+                        "name": "Amanullah M."
+                    },
+                    {
+                        "name": "Cheng X."
+                    },
+                    {
+                        "name": "Liu P."
+                    },
+                    {
+                        "name": "Liu W."
+                    },
+                    {
+                        "name": "Liu Y."
+                    },
+                    {
+                        "name": "Lu Y."
+                    },
+                    {
+                        "name": "Pan X."
+                    },
+                    {
+                        "name": "Xu H."
+                    },
+                    {
+                        "name": "Zhang H."
+                    }
+                ],
+                "date": "2023-02-14T00:00:00Z",
+                "journal": "Bioinformatics (Oxford, England)",
+                "title": "WMDS.net: a network control framework for identifying key players in transcriptome programs"
+            },
+            "pmcid": "PMC9925106",
+            "pmid": "36727489"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Molecular interactions, pathways and networks",
+            "uri": "http://edamontology.org/topic_0602"
+        },
+        {
+            "term": "Oncology",
+            "uri": "http://edamontology.org/topic_2640"
+        },
+        {
+            "term": "Physiology",
+            "uri": "http://edamontology.org/topic_3300"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        }
+    ]
+}
diff --git a/data/wordom/wordom.biotools.json b/data/wordom/wordom.biotools.json
new file mode 100644
index 0000000000000..563d93425d79a
--- /dev/null
+++ b/data/wordom/wordom.biotools.json
@@ -0,0 +1,125 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-03T22:33:40.617712Z",
+    "biotoolsCURIE": "biotools:wordom",
+    "biotoolsID": "wordom",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "angelo.felline@unimore.it",
+            "name": "Angelo Felline",
+            "orcidid": "https://orcid.org/0000-0002-0417-8442",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "fanelli@unimo.it",
+            "name": "Francesca Fanelli",
+            "orcidid": "https://orcid.org/0000-0002-7620-6895",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "michele.seeber@unimore.it",
+            "name": "Michele Seeber",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Marco Cecchini",
+            "orcidid": "https://orcid.org/0000-0003-2671-1583"
+        },
+        {
+            "name": "Simone Conti",
+            "orcidid": "https://orcid.org/0000-0001-7986-567X"
+        }
+    ],
+    "description": "A user-friendly program for the analysis of molecular structures and conformational ensembles.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Essential dynamics",
+                    "uri": "http://edamontology.org/operation_3891"
+                },
+                {
+                    "term": "Molecular dynamics",
+                    "uri": "http://edamontology.org/operation_2476"
+                },
+                {
+                    "term": "Principal component visualisation",
+                    "uri": "http://edamontology.org/operation_2939"
+                }
+            ]
+        }
+    ],
+    "homepage": "http://wordom.sf.net",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-03-03T22:33:40.620313Z",
+    "license": "GPL-3.0",
+    "link": [
+        {
+            "type": [
+                "Repository"
+            ],
+            "url": "https://sourceforge.net/projects/wordom/"
+        }
+    ],
+    "name": "Wordom",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.CSBJ.2023.01.026",
+            "metadata": {
+                "abstract": "We present the second update of Wordom, a user-friendly and efficient program for manipulation and analysis of conformational ensembles from molecular simulations. The actual update expands some of the existing modules and adds 21 new modules to the update 1 published in 2011. The new adds can be divided into three sets that: 1) analyze atomic fluctuations and structural communication; 2) explore ion-channel conformational dynamics and ionic translocation; and 3) compute geometrical indices of structural deformation. Set 1 serves to compute correlations of motions, find geometrically stable domains, identify a dynamically invariant core, find changes in domain-domain separation and mutual orientation, perform wavelet analysis of large-scale simulations, process the output of principal component analysis of atomic fluctuations, perform functional mode analysis, infer regions of mechanical rigidity, analyze overall fluctuations, and perform the perturbation response scanning. Set 2 includes modules specific for ion channels, which serve to monitor the pore radius as well as water or ion fluxes, and measure functional collective motions like receptor twisting or tilting angles. Finally, set 3 includes tools to monitor structural deformations by computing angles, perimeter, area, volume, β-sheet curvature, radial distribution function, and center of mass. The ring perception module is also included, helpful to monitor supramolecular self-assemblies. This update places Wordom among the most suitable, complete, user-friendly, and efficient software for the analysis of biomolecular simulations. The source code of Wordom and the relative documentation are available under the GNU general public license at http://wordom.sf.net.",
+                "authors": [
+                    {
+                        "name": "Cecchini M."
+                    },
+                    {
+                        "name": "Conti S."
+                    },
+                    {
+                        "name": "Fanelli F."
+                    },
+                    {
+                        "name": "Felline A."
+                    },
+                    {
+                        "name": "Seeber M."
+                    }
+                ],
+                "date": "2023-01-01T00:00:00Z",
+                "journal": "Computational and Structural Biotechnology Journal",
+                "title": "Wordom update 2: A user-friendly program for the analysis of molecular structures and conformational ensembles"
+            },
+            "pmcid": "PMC9929209",
+            "pmid": "36817953"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Molecular dynamics",
+            "uri": "http://edamontology.org/topic_0176"
+        },
+        {
+            "term": "NMR",
+            "uri": "http://edamontology.org/topic_0593"
+        },
+        {
+            "term": "Structural variation",
+            "uri": "http://edamontology.org/topic_3175"
+        }
+    ]
+}
diff --git a/data/wrmxpress/wrmxpress.biotools.json b/data/wrmxpress/wrmxpress.biotools.json
new file mode 100644
index 0000000000000..2a0c6917fbfad
--- /dev/null
+++ b/data/wrmxpress/wrmxpress.biotools.json
@@ -0,0 +1,128 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-01-24T15:15:44.911811Z",
+    "biotoolsCURIE": "biotools:wrmxpress",
+    "biotoolsID": "wrmxpress",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "mzamanian@wisc.edu",
+            "name": "Mostafa Zamanian",
+            "orcidid": "https://orcid.org/0000-0001-9233-1760",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Kendra J. Gallo"
+        },
+        {
+            "name": "John D. Chan",
+            "orcidid": "https://orcid.org/0000-0003-4986-972X"
+        },
+        {
+            "name": "Nicolas J. Wheeler",
+            "orcidid": "https://orcid.org/0000-0002-5909-4190"
+        }
+    ],
+    "description": "A modular package for high-throughput image analysis of parasitic and free-living worms.",
+    "documentation": [
+        {
+            "type": [
+                "User manual"
+            ],
+            "url": "http://www.zamanianlab.org/ZamanianLabDocs/pipelines_wrmxpress/"
+        }
+    ],
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Editing",
+                    "uri": "http://edamontology.org/operation_3096"
+                },
+                {
+                    "term": "Image analysis",
+                    "uri": "http://edamontology.org/operation_3443"
+                },
+                {
+                    "term": "Parsing",
+                    "uri": "http://edamontology.org/operation_1812"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/zamanianlab/wrmXpress",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-24T15:15:44.914479Z",
+    "license": "MIT",
+    "name": "wrmXpress",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1371/JOURNAL.PNTD.0010937",
+            "metadata": {
+                "abstract": "© 2022 Wheeler et al.Advances in high-throughput and high-content imaging technologies require concomitant development of analytical software capable of handling large datasets and generating rele-vant phenotypic measurements. Several tools have been developed to analyze drug response phenotypes in parasitic and free-living worms, but these are siloed and often limited to specific instrumentation, worm species, and single phenotypes. No unified tool exists to analyze diverse high-content phenotypic imaging data of worms and provide a platform for future extensibility. We have developed wrmXpress, a unified framework for analyzing a variety of phenotypes matched to high-content experimental assays of free-living and parasitic nematodes and flatworms. We demonstrate its utility for analyzing a suite of pheno-types, including motility, development/size, fecundity, and feeding, and establish the package as a platform upon which to build future custom phenotypic modules. We show that wrmXpress can serve as an analytical workhorse for anthelmintic screening efforts across schistosomes, filarial nematodes, and free-living model nematodes and holds prom-ise for enabling collaboration among investigators with diverse interests.",
+                "authors": [
+                    {
+                        "name": "Chan J.D."
+                    },
+                    {
+                        "name": "Gallo K.J."
+                    },
+                    {
+                        "name": "Rehborg E.J.G."
+                    },
+                    {
+                        "name": "Ryan K.T."
+                    },
+                    {
+                        "name": "Wheeler N.J."
+                    },
+                    {
+                        "name": "Zamanian M."
+                    }
+                ],
+                "date": "2022-11-01T00:00:00Z",
+                "journal": "PLoS Neglected Tropical Diseases",
+                "title": "wrmXpress: A modular package for high-throughput image analysis of parasitic and free-living worms"
+            },
+            "pmcid": "PMC9718391",
+            "pmid": "36399491"
+        }
+    ],
+    "toolType": [
+        "Library"
+    ],
+    "topic": [
+        {
+            "term": "Genotype and phenotype",
+            "uri": "http://edamontology.org/topic_0625"
+        },
+        {
+            "term": "Imaging",
+            "uri": "http://edamontology.org/topic_3382"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Transcription factors and regulatory sites",
+            "uri": "http://edamontology.org/topic_0749"
+        },
+        {
+            "term": "Workflows",
+            "uri": "http://edamontology.org/topic_0769"
+        }
+    ]
+}
diff --git a/data/xcvatr/xcvatr.biotools.json b/data/xcvatr/xcvatr.biotools.json
new file mode 100644
index 0000000000000..51fce7c34a0e4
--- /dev/null
+++ b/data/xcvatr/xcvatr.biotools.json
@@ -0,0 +1,123 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T00:29:43.375245Z",
+    "biotoolsCURIE": "biotools:xcvatr",
+    "biotoolsID": "xcvatr",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "email": "arif.o.harmanci@uth.tmc.edu",
+            "name": "Arif Harmanci",
+            "orcidid": "https://orcid.org/0000-0002-9696-1118",
+            "typeEntity": "Person"
+        },
+        {
+            "email": "Akash.Patel@bcm.edu",
+            "name": "Akash J. Patel",
+            "typeEntity": "Person"
+        },
+        {
+            "name": "Akdes Serin Harmanci"
+        },
+        {
+            "name": "Tiemo J. Klisch"
+        }
+    ],
+    "description": "Detection and characterization of variant impact on the Embeddings of single -cell and bulk RNA-sequencing samples.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Gene expression profiling",
+                    "uri": "http://edamontology.org/operation_0314"
+                },
+                {
+                    "term": "Genotyping",
+                    "uri": "http://edamontology.org/operation_3196"
+                },
+                {
+                    "term": "Indel detection",
+                    "uri": "http://edamontology.org/operation_0452"
+                },
+                {
+                    "term": "RNA-Seq quantification",
+                    "uri": "http://edamontology.org/operation_3800"
+                },
+                {
+                    "term": "Variant filtering",
+                    "uri": "http://edamontology.org/operation_3675"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://github.com/harmancilab/XCVATR",
+    "language": [
+        "C++",
+        "Shell"
+    ],
+    "lastUpdate": "2023-02-10T00:29:43.378515Z",
+    "license": "Not licensed",
+    "name": "XCVATR",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1186/S12864-022-09004-7",
+            "metadata": {
+                "abstract": "© 2022, The Author(s).Background: RNA-sequencing has become a standard tool for analyzing gene activity in bulk samples and at the single-cell level. By increasing sample sizes and cell counts, this technique can uncover substantial information about cellular transcriptional states. Beyond quantification of gene expression, RNA-seq can be used for detecting variants, including single nucleotide polymorphisms, small insertions/deletions, and larger variants, such as copy number variants. Notably, joint analysis of variants with cellular transcriptional states may provide insights into the impact of mutations, especially for complex and heterogeneous samples. However, this analysis is often challenging due to a prohibitively high number of variants and cells, which are difficult to summarize and visualize. Further, there is a dearth of methods that assess and summarize the association between detected variants and cellular transcriptional states. Results: Here, we introduce XCVATR (eXpressed Clusters of Variant Alleles in Transcriptome pRofiles), a method that identifies variants and detects local enrichment of expressed variants within embedding of samples and cells in single-cell and bulk RNA-seq datasets. XCVATR visualizes local “clumps” of small and large-scale variants and searches for patterns of association between each variant and cellular states, as described by the coordinates of cell embedding, which can be computed independently using any type of distance metrics, such as principal component analysis or t-distributed stochastic neighbor embedding. Through simulations and analysis of real datasets, we demonstrate that XCVATR can detect enrichment of expressed variants and provide insight into the transcriptional states of cells and samples. We next sequenced 2 new single cell RNA-seq tumor samples and applied XCVATR. XCVATR revealed subtle differences in CNV impact on tumors. Conclusions: XCVATR is publicly available to download from https://github.com/harmancilab/XCVATR.",
+                "authors": [
+                    {
+                        "name": "Harmanci A."
+                    },
+                    {
+                        "name": "Harmanci A.S."
+                    },
+                    {
+                        "name": "Klisch T.J."
+                    },
+                    {
+                        "name": "Patel A.J."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "BMC Genomics",
+                "title": "XCVATR: detection and characterization of variant impact on the Embeddings of single -cell and bulk RNA-sequencing samples"
+            },
+            "pmcid": "PMC9764736",
+            "pmid": "36539717"
+        }
+    ],
+    "toolType": [
+        "Command-line tool"
+    ],
+    "topic": [
+        {
+            "term": "Cell biology",
+            "uri": "http://edamontology.org/topic_2229"
+        },
+        {
+            "term": "Gene transcripts",
+            "uri": "http://edamontology.org/topic_3512"
+        },
+        {
+            "term": "Genetic variation",
+            "uri": "http://edamontology.org/topic_0199"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/yalla/yalla.biotools.json b/data/yalla/yalla.biotools.json
index 5ff193169db42..300ef92d3dbe0 100644
--- a/data/yalla/yalla.biotools.json
+++ b/data/yalla/yalla.biotools.json
@@ -6,7 +6,19 @@
     "collectionID": [
         "PerMedCoE"
     ],
+    "confidence_flag": "tool",
     "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "James Sharpe"
+        },
+        {
+            "name": "Miquel Marin-Riera"
+        },
+        {
+            "name": "Philipp Germann"
+        }
+    ],
     "description": "GPU-Powered Spheroid Models for Mesenchyme and Epithelium",
     "download": [
         {
@@ -18,9 +30,16 @@
         "type": "public"
     },
     "homepage": "https://github.com/germannp/yalla",
-    "lastUpdate": "2022-04-28T14:15:52.083477Z",
+    "language": [
+        "Python"
+    ],
+    "lastUpdate": "2023-01-10T13:35:10.043473Z",
     "license": "MIT",
     "name": "yalla",
+    "operatingSystem": [
+        "Linux",
+        "Mac"
+    ],
     "owner": "tntiniak",
     "publication": [
         {
@@ -38,13 +57,17 @@
                         "name": "Sharpe J."
                     }
                 ],
-                "citationCount": 9,
+                "citationCount": 14,
                 "date": "2019-03-27T00:00:00Z",
                 "journal": "Cell Systems",
                 "title": "ya||a: GPU-Powered Spheroid Models for Mesenchyme and Epithelium"
-            }
+            },
+            "pmid": "30904379"
         }
     ],
+    "toolType": [
+        "Command-line tool"
+    ],
     "version": [
         "1.0"
     ]
diff --git a/data/yersiniomics/yersiniomics.biotools.json b/data/yersiniomics/yersiniomics.biotools.json
new file mode 100644
index 0000000000000..58c9517cc76cc
--- /dev/null
+++ b/data/yersiniomics/yersiniomics.biotools.json
@@ -0,0 +1,82 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-03-03T22:24:15.347943Z",
+    "biotoolsCURIE": "biotools:yersiniomics",
+    "biotoolsID": "yersiniomics",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Javier Pizarro-Cerdá"
+        },
+        {
+            "name": "Karen Druart"
+        },
+        {
+            "name": "Cyril Savin",
+            "orcidid": "https://orcid.org/0000-0003-2659-3433"
+        },
+        {
+            "name": "Olivier Dussurget",
+            "orcidid": "https://orcid.org/0000-0001-9924-0837"
+        },
+        {
+            "name": "Pierre Lê-Bury",
+            "orcidid": "https://orcid.org/0000-0002-6010-2952"
+        }
+    ],
+    "description": "Multi-Omics Interactive Database for Yersinia Species.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "operation": [
+                {
+                    "term": "Data retrieval",
+                    "uri": "http://edamontology.org/operation_2422"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://yersiniomics.pasteur.fr/#bacnet.Yersinia",
+    "lastUpdate": "2023-03-03T22:24:15.350467Z",
+    "name": "Yersiniomics",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1128/SPECTRUM.03826-22",
+            "pmid": "36847572"
+        }
+    ],
+    "toolType": [
+        "Database portal"
+    ],
+    "topic": [
+        {
+            "term": "Mapping",
+            "uri": "http://edamontology.org/topic_0102"
+        },
+        {
+            "term": "Proteomics",
+            "uri": "http://edamontology.org/topic_0121"
+        },
+        {
+            "term": "Proteomics experiment",
+            "uri": "http://edamontology.org/topic_3520"
+        },
+        {
+            "term": "RNA-Seq",
+            "uri": "http://edamontology.org/topic_3170"
+        },
+        {
+            "term": "Transcriptomics",
+            "uri": "http://edamontology.org/topic_3308"
+        }
+    ]
+}
diff --git a/data/ymla/ymla.biotools.json b/data/ymla/ymla.biotools.json
new file mode 100644
index 0000000000000..a83955451859b
--- /dev/null
+++ b/data/ymla/ymla.biotools.json
@@ -0,0 +1,106 @@
+{
+    "accessibility": "Open access",
+    "additionDate": "2023-02-10T00:24:48.476454Z",
+    "biotoolsCURIE": "biotools:ymla",
+    "biotoolsID": "ymla",
+    "confidence_flag": "tool",
+    "cost": "Free of charge",
+    "credit": [
+        {
+            "name": "Chia-Wei Hsu"
+        },
+        {
+            "name": "Wei-Sheng Wu"
+        },
+        {
+            "name": "Yan-Yuan Tseng"
+        },
+        {
+            "name": "Tzu-Hsien Yang",
+            "orcidid": "https://orcid.org/0000-0001-9420-196X"
+        }
+    ],
+    "description": "A comparative platform to carry out functional enrichment analysis for multiple gene lists in yeast.",
+    "editPermission": {
+        "type": "private"
+    },
+    "function": [
+        {
+            "input": [
+                {
+                    "data": {
+                        "term": "Gene name",
+                        "uri": "http://edamontology.org/data_2299"
+                    }
+                }
+            ],
+            "operation": [
+                {
+                    "term": "Enrichment analysis",
+                    "uri": "http://edamontology.org/operation_3501"
+                }
+            ]
+        }
+    ],
+    "homepage": "https://cosbi7.ee.ncku.edu.tw/YMLA/",
+    "lastUpdate": "2023-02-10T00:24:48.480299Z",
+    "name": "YMLA",
+    "operatingSystem": [
+        "Linux",
+        "Mac",
+        "Windows"
+    ],
+    "owner": "Jennifer",
+    "publication": [
+        {
+            "doi": "10.1016/J.COMPBIOMED.2022.106314",
+            "metadata": {
+                "abstract": "© 2022 Elsevier LtdComparative analysis among multiple gene lists on their functional features is now a routine task due to the advancement of high-throughput experiments. Several enrichment analysis tools were developed in the past. However, these tools mainly focus on one gene list and contain only gene ontology or interaction features. What makes it worse, comparative investigation and customized feature set reanalysis are still unavailable. Therefore, we constructed the YMLA (Yeast Multiple List Analyzer) platform in this research. YMLA includes 39 yeast features and facilitates comparative analysis among multiple gene lists via tabular views, heatmaps, and network plots. Moreover, the customized feature set reanalysis function was implemented in YMLA to help form mechanism hypotheses based on a selected enriched feature subset. We demonstrated the biological applicability of YMLA via example lists consisting of genes with top/bottom translation efficiency values. The analysis results provided by YMLA reveal novel facts consistent with previous experiments. YMLA is available at https://cosbi7.ee.ncku.edu.tw/YMLA/.",
+                "authors": [
+                    {
+                        "name": "Hsu C.-W."
+                    },
+                    {
+                        "name": "Rathod J."
+                    },
+                    {
+                        "name": "Tseng Y.-Y."
+                    },
+                    {
+                        "name": "Wang Y.-X."
+                    },
+                    {
+                        "name": "Wu W.-S."
+                    },
+                    {
+                        "name": "Yang T.-H."
+                    },
+                    {
+                        "name": "Yu C.-H."
+                    }
+                ],
+                "date": "2022-12-01T00:00:00Z",
+                "journal": "Computers in Biology and Medicine",
+                "title": "YMLA: A comparative platform to carry out functional enrichment analysis for multiple gene lists in yeast"
+            },
+            "pmid": "36455295"
+        }
+    ],
+    "toolType": [
+        "Web application"
+    ],
+    "topic": [
+        {
+            "term": "Gene expression",
+            "uri": "http://edamontology.org/topic_0203"
+        },
+        {
+            "term": "Model organisms",
+            "uri": "http://edamontology.org/topic_0621"
+        },
+        {
+            "term": "Ontology and terminology",
+            "uri": "http://edamontology.org/topic_0089"
+        }
+    ]
+}