vshy-dream/DRExplainer

DRExplainer

An official implement of Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network

Environment Setup

To set up the required environment, you can choose from the following options:

• Using conda: You can install the necessary Python dependencies from the environment.yml file using the following command:

  conda env create -f environment.yml

Contents

• /prediction - Contains all runnable code and data of prediction.

• /explanation - Contains all runnable code and data of explanation.

Usage Guide

1. Prediction

python /prediction/code/DGCN.py

2. Explanation

python /explanation/DRExplainer.py

Code

Here is a list of the code files in this repository:

• generate_feature.py - generates the feature of bio-entities and saves it to /prediction/data/node_representation.
• generate_negedge.py - generates the negative triples described in our paper and saves them to /prediction/data.
• DGCN.py - trains the prediction model and saves the model file to /prediction/data/weights.
• dgcn_eval.py - evaluates the model prediction performance in /prediction/data/weights.
• ground_truth.py - generates the ground truth benchmark dataset and save it to /explanation/data.
• DRExplainer.py - explaines the observable triples and save the results to /explanation/data.
• explain_eval.py - evaluates the model interpretability performance is /explanation/data.

Contact

If you need any help or are looking for cooperation feel free to contact us: haoyuan.shi@mail.ustc.edu.cn.