Microbe-bridged Disease-metabolite Associations prediction by Heterogeneous Graph Fusion

Introduction

DiMiMe model uses microbial information as a bridge to fuse disease and metabolite information through bipartite graph attention network, and finally realizes the association prediction of the disease-metabolite.

Usage

'Dataset' directory

All node features and edge data of bipartite graph. Positive samples and negative samples of all kinds.

'Analysis_results' directory

Result data used in experimental case analysis.

'Sampling' directory

K-means method used in negative sampling.

'model' and 'utiles'

Model code, initialization function and evaluation metrics function involved.

Requirements

The model is tested to work under python3.6. The required dependencies versions are as follows:

torch==1.4.0+cu100
torch-cluster==1.5.2
torch-geometric==1.4.1
torch-scatter==2.0.3
torch-sparse==0.5.1
torch-spline-conv==1.2.0
torchvision==0.5.0+cu100
scikit-learn==0.21.3
pandas==1.1.5
numpy==1.16.0