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

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  • Additional Information
    • Source:
      Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
    • Publication Information:
      Original Publication: [London] : Nature Pub. Group
    • Subject Terms:
      Algorithms* ; Genomics*; Biomarkers/*analysis; Alzheimer Disease/genetics ; Breast Neoplasms/genetics ; Databases, Genetic ; Female ; Humans
    • 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.
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    • Grant Information:
      R01 EB025018 United States EB NIBIB NIH HHS; U54 AG065181 United States AG NIA NIH HHS
    • Accession Number:
      0 (Biomarkers)
    • Publication Date:
      Date Created: 20210609 Date Completed: 20210615 Latest Revision: 20240216
    • Publication Date:
      20240216
    • Accession Number:
      PMC8187432
    • Accession Number:
      10.1038/s41467-021-23774-w
    • Accession Number:
      34103512