INcorporating TRiplet Error for Predicting Protein-Protein Interactions using Deep Learning
NEW: Now published in Briefings in Bioinformatics (DOI: 10.1093/bib/bbae405)
INTREPPPID is a deep learning model for predicting protein interactions. It's especially good at making prediction on species other than those it was trained on (cross-species prediction).
You can find more information from our pre-print.
Here are some quick highlights, but be sure to read the documentation for more details!
The easiest way to install PPI Origami is to use pip to retrieve the PPI Origami release from PyPI.
pip install intrepppid
Alternatively, clone the repository and use poetry to install the dependencies
git clone https://github.com/jszym/intrepppid
cd intreppid
poetry install
To train INTREPPPID, simply use the train e2e_rnn_triplet
command like so:
intrepppid train e2e_rnn_triplet DATASET.h5 spm.model 3 100 80 --seed 3927704 --vocab_size 250 --trunc_len 1500 --embedding_size 64 --rnn_num_layers 2 --rnn_dropout_rate 0.3 --variational_dropout false --bi_reduce last --workers 4 --embedding_droprate 0.3 --do_rate 0.3 --log_path logs/e2e_rnn_triplet --beta_classifier 2 --use_projection false --optimizer_type ranger21_xx --lr 1e-2
Be sure to read the documentation for more details.
If you use or refer to INTREPPPID, kindly cite our pre-print:
Joseph Szymborski, Amin Emad, INTREPPPID—an orthologue-informed quintuplet network for cross-species prediction of protein–protein interaction, Briefings in Bioinformatics, Volume 25, Issue 5, September 2024, bbae405, https://doi.org/10.1093/bib/bbae405
also available in BibTex format:
@article{intrepppid,
title={INTREPPPID—an orthologue-informed quintuplet network for cross-species prediction of protein–protein interaction},
volume={25},
ISSN={1477-4054},
DOI={10.1093/bib/bbae405},
number={5},
journal={Briefings in Bioinformatics},
author={Szymborski, Joseph and Emad, Amin},
year={2024},
month=sep,
pages={bbae405}
}
INTREPPPID
INcorporating TRiplet Error for Predicting Protein-Protein Interactions using Deep Learning
Copyright (C) 2023 Joseph Szymborski
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/.