# MCHNN

**Repository Path**: Tomhappy/MCHNN

## Basic Information

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

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-12-09
- **Last Updated**: 2023-12-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MCHNN

The code for paper " Multi-view Contrastive Learning Hypergraph Neural Network for Drug-Microbe-Disease Association Prediction (IJCAI 2023)''.

![image-20230110161157410](./figure/model.png) 

## 1.Overview

The repository is organized as follows:

- `Data/` contains the datasets used in the paper;
- `Utils/` contains the processing functions and tools (e.g., negative sampling strategy functions....);
- `main_cv_hyper_graph.py` contains the training and testing code of 5-CV  experiments on datasets;
- `main_indep_hyper_graph.py` contains the training and testing code of independent test experiments on datasets;
- `model_hyper_graph.py` contains the three body parts of the model, i.e., BioEncoder, HgnnEncoder, and Decoder.

## 2.Dependencies

- python == 3.7.2
- numpy == 1.20.2
- torch == 1.8.0
- torch-geometric == 2.0.1
- networks == 2.5.1
- sklearn == 1.0.2
- rdkit == 2020.09.1.0
- deepchem == 2.5.0

## 3.Example

Here we provide two examples of using MCHNN to run 5-CV experiments and independent test experiments, execute the following command:

`python main_cv_hyper_graph.py --hypergraph_loss_ratio 0.8 --lr 0.005`

`python main_indep_hyper_graph.py --hypergraph_loss_ration 0.8 --lr 0.005`