Multi-Omic Graph Diagnosis (MOGDx): a data integration tool to perform classification tasks for heterogeneous diseases
Year: 2024
Type: article
Abstract: MOTIVATION: 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. RESULTS: 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 has a network taxonomy. It fuses patient similarity networks, augments this integrated network with a reduced vector representation of genomic data and performs classification using a graph convolutional network. 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 integrated more genomic measures with greater patient coverage compared to other network integrative methods. Overall, MOGDx is a promising tool for integrating multi-omic data, classifying heterogeneous diseases, and aiding interpretation of genomic marker data. AVAILABILITY AND IMPLEMENTATION: MOGDx source code is available from https://github.com/biomedicalinformaticsgroup/MOGDx.
Source: Bioinformatics
Authors: Barry Ryan, Riccardo E Marioni, T Ian Simpson
Institutions: University of Edinburgh, Institute of Genetics and Cancer
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $4,188
Awards: G3186036607, G3565013306
OpenAlex ID: W4401798479