MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification

TLDR

Novel computational methods are needed to integrate multiple omics data for a comprehensive understanding of human diseases. The study introduces MOGONET, a multi‑omics integrative method for biomedical classification. MOGONET uses graph convolutional networks to jointly learn omics‑specific representations and cross‑omics correlations for classification. MOGONET outperforms state‑of‑the‑art methods across multiple omics datasets and identifies key biomarkers relevant to the studied biomedical problems.

Abstract

Abstract To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.