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bioRxiv · 10.1101/2020.07.02.184705

MORONET: Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification

Wang, T.·Shao, W.·Huang, Z.·Tang, H.·Ding, Z.·Huang, K.
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Abstract

ABSTRACTTo fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis for multiple types of omics data. We present a novel multi-omics integrative method named Multi-Omics gRaph cOnvolutional NETworks (MORONET) for biomedical classification. MORONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MORONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from a wide range of biomedical classification applications using mRNA expression data, DNA methylation data, and miRNA expression data. Furthermore, MORONET is able to identify important biomarkers from different omics data types that are related with the investigated diseases.Competing Interest StatementThe authors have declared no competing interest.View Full Text

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Wang, T., Shao, W., Huang, Z., Tang, H., Ding, Z., Huang, K.. 2020-07-03. MORONET: Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification. https://doi.org/10.1101/2020.07.02.184705

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