[CODE] Multi-Omic Graph Diagnosis (MOGDx) : A data integration tool to perform classification tasks for heterogeneous diseases

MOGDx v2.0.1 Release This release has been developed for the ISMB conference paper submission. Multi-omic Graph Diagnosis (MOGDx) is a tool for the integration of omic data and classification of heterogeneous diseases Key Features 1. Genomic Data Integration Integrate any number of modalities Flexibility in choice of omics integrated 2. Network Taxonomy Represents omic information using a patient similarity metric Uses Similarity Network Fusion to integrate multiple omics 3. Graph Convolutional Network with Multi Modal Encoder Reduces multiple modalities in a supervised manner using multi-modal encoder Performs patient classification using graph convolutional network framework How to Use See README.md for instructions of use Citation If you use MOGDx in your research, please cite our paper once it is published or our preprint on medRxiv Feedback and Contributions We welcome your feedback and contributions! Feel free to open an issue on our GitHub for bug reports, feature requests, or general inquiries. Thank you for choosing MOGDx for your multi omic integration, nees We look forward to your contributions and the advancement of genomics research.

Barry Ryan. (2024). Multi-Omic Graph Diagnosis (MOGDx) : A data integration tool to perform classification tasks for heterogeneous diseases (v2.0.1). Zenodo. https://doi.org/10.5281/zenodo.10546432