NSAP:A neighborhood subgraph aggregation method for drug-disease association prediction

This is our Pytorch implementation for the paper: Qiqi Jiao,Yu Jiang, Yang Zhang, Yadong Wang, and Junyi Li(2022).NSAP:A neighborhood subgraph aggregation method for drug-disease association prediction.

Table of Contents

• Environment Requirement
• Dataset
• Usage
• Training

Environment requirement

The code has been tested running uder python 3.7.4. The required packages are as follows:

• dgl==0.3.1
• numpy==1.18.1
• pandas==0.25.1
• scipy==1.5.2
• torch==1.9.0

It can be installed by the following command.

pip install -r requirement.txt

Dataset

Relations(A-B)	Number of A	Number of B	Number of A-B
Drug-disease	1482	793	11540
Drug-protein	1482	2077	11407
Disease-gene	793	6365	18844

Usage

1.Create checkpoint/ and dataset/preprocess_NSAP directories.

2.run the file /dataset/preprocess_NSAP_NEW.ipynb, and generate all the files we need.

3.Execute the following command from the home directory:

python nsap.py 

Training

The instruction of commands has been clearly stated in the codes (see the parser function in utils/parser.py).

Some important hyper-parameters are listed here.

• samples : It specifies the number of sampled neighboor.
• num_heads : It specifies the number of the attention heads.