Multi-omics Data Fusion

Multi-Omics Data Fusion is a computational and statistical methodology that integrates heterogeneous biological data types generated from multiple omics layers—such as genomics, transcriptomics, proteomics, and metabolomics—into a unified framework to reveal systemic biological insights that cannot be discerned from individual omics analyses alone.

1.5K

Publications

131.5K

Citations

13.5K

Authors

2.9K

Institutions

Publications per year

2017–2026

1.1K

Authors

13.5K

  1. KL
    Kim‐Anh Lê Cao

    The University of Queensland, Australia

    13 publications · 7.2K citations

    13

    h-index

  2. MS
    M Snyder

    Stanford University, United States

    13 publications · 1.9K citations

    12

    h-index

  3. AC
    Ana Conesa

    Centro de Investigacion Principe Felipe, Spain

    10 publications · 743 citations

    10

    h-index

  4. YB
    Yīmíng Bào

    Beijing Institute of Genomics, China

    16 publications · 6.3K citations

    9

    h-index

  5. JS
    Julio Sáez-Rodríguez

    Heidelberg University, Germany

    9 publications · 1.1K citations

    9

    h-index

1–5 of 13.5K

Venues

  1. Bioinformatics

    77 publications · 7.2K citations

    40

    h-index

  2. Nucleic Acids Research

    57 publications · 15.3K citations

    36

    h-index

  3. Nature Communications

    35 publications · 3.8K citations

    31

    h-index

  4. Scientific Reports

    53 publications · 2.6K citations

    30

    h-index

  5. BMC Bioinformatics

    47 publications · 3.4K citations

    30

    h-index

1–5