https://github.com/laminetourelab/mogonet
MOGONET (Multi-Omics Graph cOnvolutional NETworks) is multi-omics data integrative analysis framework for classification tasks in biomedical applications.
https://github.com/laminetourelab/mogonet
deep-learning graph-convolutional-networks machine-learning mogonet multi-omics multi-omics-integration multimodal-deep-learning pytorch
Last synced: 3 months ago
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MOGONET (Multi-Omics Graph cOnvolutional NETworks) is multi-omics data integrative analysis framework for classification tasks in biomedical applications.
- Host: GitHub
- URL: https://github.com/laminetourelab/mogonet
- Owner: LamineTourelab
- License: mit
- Created: 2022-12-08T15:38:45.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-03-27T14:17:13.000Z (about 1 year ago)
- Last Synced: 2025-04-19T17:53:45.872Z (about 1 year ago)
- Topics: deep-learning, graph-convolutional-networks, machine-learning, mogonet, multi-omics, multi-omics-integration, multimodal-deep-learning, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 56.6 MB
- Stars: 14
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# MOGONET : Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification
MOGONET integrates multi-omics data using graph convolutional networks
## Fig: MOGONET architecture.
MOGONET combines GCN for multi-omics-specific learning and VCDN for multi-omics integration. For clear and concise illustration, an example of one sample is chosen to demonstrate the VCDN component for multi-omics integration. Preprocessing is first performed on each omics data type to remove noise and redundant features. Each omics-specific GCN is trained to perform class prediction using omics features and the corresponding sample similarity network generated from the omics data. The cross-omics discovery tensor is calculated from the initial predictions of omics-specific GCNs and forwarded to VCDN for final prediction. MOGONET is an end-to-end model and all networks are trained jointly.
Here is the original [MOGONET paper](https://www.nature.com/articles/s41467-021-23774-w) et [GitHub repository](https://github.com/txWang/MOGONET).
It provides tools for biomedical data classification and biomarker identification. MOGONET can handle binary and multi-class classification tasks, making it suitable for a wide range of applications in bioinformatics and computational biology.
# Files
```
mogonet/
├── README.md # Project documentation
├── MOGONET_tutorial_colab.ipynb # Jupyter notebook tutorial (Google colab)
├── licence.md # License information
├── requirements.txt # List of dependencies
├── setup.py # Configuration for packaging
├── mogonet/ # Main package directory
│ ├── __init__.py # Package initialization
│ ├── _version.py # Version information
│ ├── feat_importance.py # Feature importance functions
│ ├── models.py # Neural network models
│ ├── train_test.py # Training and testing functions
│ └── utils.py # Utility functions
├── scripts/ # Example scripts
│ ├── MOGONET.py # Data preparation script
│ ├── main_biomarker.py # Biomarker identification example
│ └── main_mogonet.py # Classification example
└── .github/ # GitHub Actions configuration
└── workflows/
└── python-package.yml # CI/CD workflow
```
# Installation
To install MOGONET directly from the source code, follow these steps:
```
git clone https://github.com/LamineTourelab/MOGONET.git
cd MOGONET/
pip install .
# If all required dependencies are not installed run the following
pip install -r requirements.txt
```
See the [google colab noetbook](https://github.com/LamineTourelab/MOGONET/blob/main/MOGONET_tutorial_colab.ipynb) for examples.
# License
MOGONET is released under the MIT License. See the [LICENSE](https://github.com/LamineTourelab/MOGONET/blob/main/licence.md) file for more details.
# Acknowledgments
This implementation is inspired by the original [MOGONET paper](https://www.nature.com/articles/s41467-021-23774-w) et [GitHub repository](https://github.com/txWang/MOGONET)..
If you use MOGONET in your research, please cite the original article:
```
@article{wang2021mogonet,
title={MOGONET integrates multi-omics data using graph convolutional networks for biomedical data classification},
author={Wang, Tianxiang and others},
journal={Nature Communications},
year={2021}
}
```