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

Research output: Working paperPreprint

Abstract / Description of output

Heterogeneity in human diseases presents challenges in diagnosis and treatments due to the broad range of manifestations and symptoms. With the rapid development of labelled multi-omic data, integrative machine learning methods have achieved breakthroughs in treatments by redefining these diseases at a more granular level. These approaches often have limitations in scalability, oversimplification, and handling of missing data. In this study, we introduce Multi-Omic Graph Diagnosis (MOGDx), a flexible command line tool for the integration of multi-omic data to perform classification tasks for heterogeneous diseases. MOGDx is a network integrative method that combines patient similarity networks with a reduced vector representation of genomic data. The reduced vector is derived from the latent embeddings of an auto-encoder and the combined network is fed into a graph convolutional network for classification. MOGDx was evaluated on three datasets from the cancer genome atlas for breast invasive carcinoma, kidney cancer, and low grade glioma. MOGDx demonstrated state-of-the-art performance and an ability to identify relevant multi-omic markers in each task. It did so while integrating more genomic measures with greater patient coverage compared to other network integrative methods. MOGDx is available to download from https://github.com/biomedicalinformaticsgroup/MOGDx. Overall, MOGDx is a promising tool for integrating multi-omic data, classifying heterogeneous diseases, and interpreting genomic markers.
Original languageEnglish
PublishermedRxiv
Pages1-11
Number of pages11
DOIs
Publication statusPublished - 9 Jul 2023

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