MOGONET

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MOGONET (Multi-Omics Graph cOnvolutional NETworks) is multi-omics data integrative analysis framework for classification tasks in biomedical applications.

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# MOGONET : Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification

MOGONET integrates multi-omics data using graph convolutional networks
## Fig: MOGONET architecture.
![image](https://user-images.githubusercontent.com/93058160/214865396-c19cc08b-8396-4cec-b2f4-ce02b3f933bc.png)

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}
}
```

MOGONET

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