computesuda / sgad

Protein Interaction Network Reconstruction with a Structural Gated Attention Deep Model by Incorporating Network Structure Information

Contents for SGAD

• Overview
• Repo Contents
• System Requirements
• Installation Guide
  • Dependencies
  • Download
• Citation

Overview

Protein-protein interactions (PPIs) represent a delicate but universal mechanism for a wide range of biological processes in living cells. Many aberrant PPIs have been identified to be involved in various diseases, which greatly enlarge the therapeutic targets for cancers, infectious disease, neurodegenerative diseases, and so on. Nowadays, multiple descriptors based on sequence information have been employed in many deep learning predictors. However, these models are mostly lacking the sufficient description of the original structure of protein networks and incapable of capturing the highly nonlinear structure of protein networks. As a result, the predictive models suffer from generalizability and are not applicable to the unseen datasets. On the other word, most of the predictive models are not robust in dealing with the noise in the dataset, leading to the fragility in predictive models. Herein, we proposed a novel computational framework, Structural Gated Attention Deep (SGAD) Model, for PPIs network reconstruction.

Repo Contents

• 1-3Datasets: PPI data sets (1:3 ratio).
• 1-5Datasets: PPI data sets (1:5 ratio).
• Datasets: PPI data sets and fasta files of proteins.
• evaluation_indicators: evaluation index functions.
• fig: plotting ROC curves.
• src: model, data loading and feature extraction.

System Requirements

The SGAD package requires only a standard computer with enough RAM to support the operations defined by a user. All the experiments were run on CentOS 8, CUDA 10.1.243, CuDnn 7.0, Python 3.7, Keras 2.0, and PyTorch 1.3.0.

Installation Guide

Dependencies

The following dependencies are required to run SGAD properly:

• scikit-learn==0.23.0
• numpy==1.16.2
• torch==1.3.1
• keras==2.0.8
• networkx==2.4.0

Download

git clone https://github.com/ComputeSuda/SGAD.git

Citation

For usage of the package, please cite the following paper.

Fei Zhu, et al, Protein Interaction Network Reconstruction with a Structural Gated Attention Deep
Model by Incorporating Network Structure Information, J. Chem. Inf. Model., 2022, 62, 2, 258–273.

This repository is distributed under GNU General Public License v3.0.

sgad's Issues

Q for code

# 2
# Keep the proteins involved
proteinID1 = [Po_pairs[i][0] for i in range(len(Po_pairs))]
proteinID2 = [Po_pairs[i][1] for i in range(len(Po_pairs))]
proteinID3 = [Ne_pairs[i][0] for i in range(len(Ne_pairs))]
proteinID4 = [Ne_pairs[i][1] for i in range(len(Ne_pairs))]
proteinID = list(set(proteinID1 + proteinID2 + proteinID3 + proteinID4))
add_nodes = list(set(proteinID3 + proteinID4) - set(proteinID1 + proteinID1))  ##

In SGAD.py,line 477 (the last line above), "proteinID1 + proteinID1", Why use "proteinID1" twice, and use "proteinID1" once is also the same result. Would you please take the time to answer my questions