1axin/MML-MGNN

The implementation of the [A Multi-Channel Graph Neural Network based on Multi-Similarity Modality Hypergraph Contrastive Learning for Predicting Unknown Types of Cancer Biomarkers]

MML-MGNN

MML-MGNN is an implementation of the [A Multi-Channel Graph Neural Network based on Multi-Similarity Modality Hypergraph Contrastive Learning for Predicting Unknown Types of Cancer Biomarkers]

The project includes data processing, experimental result presentation, and model training.

Table of Contents

• Features
• Directory Structure
• Installation
• Usage
• Data
• Output
• Experimental Results
• mcgae
• Contributing
• License
• References

Features

-Implementation of multi-similarity modal hypergraph contrastive learning (MMLMGN)

-Data processing and experimental workflow

-Showing experimental results in Jupyter Notebooks

-Using Python package tools for dependency management

-Implementation of prediction cases (mcgae)

Directory Structure

MML-MGNN/
│
├── data/                  # Example input files needed for running the code
│
├── experimental_results/  # All experimental results and associated Notebooks
│
├── mmlmgn/                # Contains all scripts and main.py for feature engineering
│
├── output/                # Output files generated by running main.py
│
├── mcgae/                 # Example script to implement prediction
│
├── requirements.txt       # Project dependencies
│
└── setup.py               # Package configuration file
│
└── README.md              # This document

Installation

To install the required dependencies in your environment, follow these steps:

1. Clone the repository:

   git clone https://github.com/axin/MML-MGNN.git
   cd MML-MGNN

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  

3. Install the dependencies:

   pip install -r requirements.txt

Usage

To run the main program, use the following command:

python mmlmgm/main.py

Command-Line Arguments

You can pass command-line arguments when running main.py. Please refer to the code comments in param.py for details on the available arguments.

We are pleased to announce that the MMLMGN method for feature engineering has been packaged and uploaded to pypi.

You can install this package directly:

pip install MMLMGN

For detailed usage of MMLMGN, please refer to the readme description in pypi.

Data

In the data/ folder, you can find example input files required for the code to run.

• File NodeGraph.csv is the graph input, used to construct the kernel similarity modal hyperedge of the node

• Files NodeAWalker.csv and NodeBWalker.csv are the feature inputs of two nodes, with a size of N*M, n is the number of nodes, and M is the feature dimension, used to construct the nearest neighbor modal hyperedge

• Files NodeASt.csv and NodeBSt.csv are the feature inputs of two nodes, with a size of N*M, n is the number of nodes, and M is the feature dimension, used to construct the structural topology modal hyperedge

Users can refer to data/ to customize input to use MMLMGN

Output

In the output/ folder, you can find the three modality embedding files generated by the feature engineering MMLMGN for each node.

• NodeAGraphEmb.csv, NodeBGraphEmb.csv are the kernel similarity modal embeddings of nodes A and B respectively.

• NodeAWalkerEmb.csv, NodeBWalkerEmb.csv are the nearest neighbor similarity modal embeddings of nodes A and B respectively.

• NodeAStEmb.csv, NodeBStEmb.csv are the structural topology similarity modal embeddings of nodes A and B respectively.

Experimental Results

In the experimental_results/ folder, you can find detailed reports of all experimental results along with Jupyter Notebooks used for reproducing the figures. You can browse through these Notebooks to understand the model’s performance and effectiveness.

The experimental_results/ folder contains five subfolders, corresponding to the five comparative experiments in the results section of the paper.

Cancer biomarker prediction/

• Contains the results of the benchmark data test (five-fold cross validation) for three cancer markers in the paper.
• CCA, LCA, MCA correspond to the circRNA, lncRNA, and miRNA biomarker prediction tasks for cancer, respectively.
• pre/test represents the model prediction score/data label.
• The numbers 0-4 represent the number of folds in the five-fold cross validation.
• Visualization.ipynb Implemented the analysis of all the results in Cancer biomarker prediction and the reproduction of the charts in the paper.
• Visualization.ipynb is also applicable to the data in different aggregation layer/, different channel fusion/, and Robustness Testing/. You only need to change the path of the data.

different aggregation layer/

• Results of different convolutional layer combinations on three benchmark tasks.
• Performance calculation and visualization are based on reference Visualization.ipynb(the file directory needs to be changed).
• The figures in the paper were drawn using Origin software after calculation in Visualization.ipynb.

different channel fusion/

• Results of different channel fusion methods on three benchmark tasks.
• Performance calculation and visualization are based on reference Visualization.ipynb(the file directory needs to be changed).
• The figures in the paper were drawn using Origin software after calculation in Visualization.ipynb.

Robustness Testing/

• Results of robustness tests with different interference factors on three benchmark tasks.
• Performance calculation and visualization are based on reference Visualization.ipynb(the file directory needs to be changed).
• The figures in the paper were drawn using Origin software after calculation in Visualization.ipynb.

Sota model comparison/

• Sota model comparison/ records the results of the SOTA model on three benchmark data sets (9905/, 9589/, CCA/)

• Three notebooks (9905_SOTA.ipynb, 9589_SOTA.ipynb, CCA_SOTA.ipynb) reproduce the figures comparing all models on the benchmark data.

mcgae

• mcgae provides an implementation of the multi-channel graph autoencoder used in the paper for downstream prediction tasks

• mcgae aims to use these features to make predictions after feature engineering MMLMGN

• mcgae shows how the npy file used to record the results is recorded

Usage

To run the mcgae program, use the following command:

python mcgae/MCGAE.py

Contributing

Contributions are welcome! If you would like to contribute to this project:

1. Fork this repository
2. Create your feature branch (git checkout -b feature/YourFeature)
3. Commit your changes (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature/YourFeature)
5. Open a pull request

License

This project is licensed under the MIT License. For more details, please see the LICENSE file.

Explanation

Technical support provided by axin (xinfei106@gmail.com)