Cluster-based Enrichment Analysis

Cluster-Based Enrichment Analysis is a functional enrichment analysis approach that identifies overrepresented biological functions within clusters of genes or proteins derived from high-throughput data, distinguished by its reliance on grouping entities based on similarity in expression, interaction, or other functional profiles prior to enrichment assessment.

24

Publications

39.7K

Citations

97

Authors

46

Institutions

Publications per year

2017–2026

8

Authors

97

  1. DH
    Daniel Hübschmann

    Heidelberg University, Germany

    2 publications · 334 citations

    2

    h-index

  2. ZG
    Zuguang Gu

    Heidelberg University, Germany

    2 publications · 334 citations

    2

    h-index

  3. KM
    Karen Messer

    University of California San Diego, United States

    1 publications · 17 citations

    1

    h-index

  4. GL
    Guangyao Li

    Ontario Institute for Cancer Research, Canada

    1 publications · 19 citations

    1

    h-index

  5. JM
    Joseph Mulvaney

    Cold Spring Harbor Laboratory, United States

    1 publications · 17 citations

    1

    h-index

1–5 of 97

Venues

  1. BMC Bioinformatics

    4 publications · 219 citations

    4

    h-index

  2. PLoS ONE

    3 publications · 71 citations

    3

    h-index

  3. Journal of Chemical Information and Modeling

    JCIM · 2 publications · 88 citations

    2

    h-index

  4. Bioinformatics

    2 publications · 100 citations

    2

    h-index

  5. BMC Neuroscience

    1 publications · 36 citations

    1

    h-index

1–5