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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.\nHere is the original [MOGONET paper](https://www.nature.com/articles/s41467-021-23774-w) et [GitHub repository](https://github.com/txWang/MOGONET). \n\nIt 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.\n\n# Files \n\n```\nmogonet/\n├── README.md                     # Project documentation\n├── MOGONET_tutorial_colab.ipynb # Jupyter notebook tutorial (Google colab)\n├── licence.md                    # License information\n├── requirements.txt              # List of dependencies\n├── setup.py                     # Configuration for packaging\n├── mogonet/                     # Main package directory\n│   ├── __init__.py              # Package initialization\n│   ├── _version.py              # Version information\n│   ├── feat_importance.py       # Feature importance functions\n│   ├── models.py                # Neural network models\n│   ├── train_test.py            # Training and testing functions\n│   └── utils.py                 # Utility functions\n├── scripts/                     # Example scripts\n│   ├── MOGONET.py               # Data preparation script\n│   ├── main_biomarker.py        # Biomarker identification example\n│   └── main_mogonet.py          # Classification example\n└── .github/                     # GitHub Actions configuration\n    └── workflows/\n        └── python-package.yml    # CI/CD workflow\n```\n\n# Installation \nTo install MOGONET directly from the source code, follow these steps:\n```\ngit clone https://github.com/LamineTourelab/MOGONET.git\ncd MOGONET/\npip install .\n# If all required dependencies are not installed run the following\npip install -r requirements.txt\n```\n\nSee the [google colab noetbook](https://github.com/LamineTourelab/MOGONET/blob/main/MOGONET_tutorial_colab.ipynb) for examples.\n\n# License\nMOGONET is released under the MIT License. See the [LICENSE](https://github.com/LamineTourelab/MOGONET/blob/main/licence.md) file for more details.\n\n# Acknowledgments\nThis 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)..\n\nIf you use MOGONET in your research, please cite the original article:\n```\n@article{wang2021mogonet,\n  title={MOGONET integrates multi-omics data using graph convolutional networks for biomedical data classification},\n  author={Wang, Tianxiang and others},\n  journal={Nature Communications},\n  year={2021}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flaminetourelab%2Fmogonet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flaminetourelab%2Fmogonet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flaminetourelab%2Fmogonet/lists"}