# PDDGCN

**Repository Path**: Tomhappy/PDDGCN

## Basic Information

- **Project Name**: PDDGCN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-25
- **Last Updated**: 2024-04-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PDDGCN
### PDDGCN: A parasitic disease-drug association predictor based on multi-view fusion graph convolutional networks


![image](https://github.com/wangxiaosong96/PDDGCN/blob/main/flow%20chart.png)

## Tutorial
1. Split data for cross validation and indenpendent test experiment via the script split_data.py: python split_data.py fold_number DATANAME seed_indent seed_cross

2. To perform cross validation for finding the optimal hyperparameters by running the script command_optimal.py (if you don't want to finetune the hyperparameters, just skip this step):

3. python command_optimal.py --dataName DATANAME --exp_name mid_dim/num_layer/alp_beta --seed_cross seed_cross --seed_indent seed_indent

To get the experiment results by running the script command_optimal.py

## Citation
Xiaosong Wang, Guojun Chen, Hang Hu, Min Zhang, and Zhenyu Yue*, PDDGCN: A parasitic disease-drug association predictor based on multi-view fusion graph convolutional network, 2023, Submitted.

## Requirements
numpy 1.18.0

pandas 1.1.0

scipy 1.4.1

scikit-learn 0.22

tensorflow 1.15.0

pytorch 1.6.0

python 3.7.1

## Contact

Please feel free to contact us if you need any help: zhenyuyue@ahau.edu.cn