BiFusion: Bipartite Graph Convolutional Networks for In Silico Drug Repurposing

Authors: Zichen Wang, Mu Zhou and Corey Arnold

Introduction

This repository is the Pytorch implementation of our ISMB 2020 paper 'Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing'.

BiFusion is a bipartite graph convolution network model for drug repurposing through heterogeneous information fusion. Our approach combines insights of multi-scale pharmaceutical information by constructing a multi-relational graph of drug–protein, disease-protein and protein–protein interactions.

Usage

'dataloader' directory

Contains the code for dataloader.

'layer' directory

Contains the code for model components.

'model' directory

Contains the code for BiFusion model

Run the code as following:

$ python main.py

Requirements

BiFusion is tested to work under Python 3.6. The required dependencies are:

PyTorch==1.2.0  
PyTorch-Geometric==1.4.1  
numpy==1.16.0  
scikit-learn==0.21.3

Citing

If this repository is useful for your research, please consider citing this paper:

@article{wang2020toward,
  title={Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing},
  author={Wang, Zichen and Zhou, Mu and Arnold, Corey},
  journal={Bioinformatics},
  volume={36},
  number={Supplement\_1},
  pages={i525--i533},
  year={2020},
  publisher={Oxford University Press}
}

Questions

Please send any questions you might have about this repository to zcwang0702@ucla.edu