[CODE] An Integrative Network Approach for Longitudinal Classification in Parkinson's Disease

MOGDx-PPMI v1.0.0 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. It is used to analyse the Progressive Parkinson's Marker Initiative (PPMI) dataset 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 needs. We look forward to your contributions and the advancement of genomics research. What's Changed PPMI by @Barry8197 in https://github.com/Barry8197/MOGDx-PPMI/pull/1 New Contributors @Barry8197 made their first contribution in https://github.com/Barry8197/MOGDx-PPMI/pull/1 Full Changelog: https://github.com/Barry8197/MOGDx-PPMI/commits/v1.0.0

Barry Ryan. (2024). An Integrative Network Approach for Longitudinal Classification in Parkinson's Disease (v1.0.1). Zenodo. https://doi.org/10.5281/zenodo.10546617