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 et GitHub repository.

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 for examples.

License

MOGONET is released under the MIT License. See the LICENSE file for more details.

Acknowledgments

This implementation is inspired by the original MOGONET paper et GitHub repository..

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}
}