biotools-import on Sun May 21 01:17:30 UTC 2023

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"
+        }
+    ]
+}

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": "[email protected]",
+            "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"
+        }
+    ]
+}

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": "[email protected]",
+            "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"
+        }
+    ]
+}

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"
+        }
+    ]
+}