Barry Ryan , Riccardo Marioni and T. Ian Simpson

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 incorporates a network taxonomy for data integration and utilises a graph neural network architecture for classification. Networks con be easily integrated, can readily handle missing data, and have been used in a wide variety of biomedical applications in the unsupervised setting. Graph Neural Networks (GNN) have shown powerful classification performance on several benchmark network datasets. The use of GNN's in a supervised setting for disease classification is a promising avenue to redefine heterogenous diseases.
The performance of MOGDx was benchmarked on three distinct datasets from The Cancer Genome Atlas ([TCGA]( 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 [Github](

For more information find the preprint to our paper online here