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

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

SUBMITTER: Wang T 

PROVIDER: S-EPMC8187432 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature