Functional Enrichment Analysis

Functional Enrichment Analysis is a bioinformatics statistical method that, given a set of genes (typically derived from high-throughput gene expression experiments), tests for and quantifies the over- or under-representation of predefined functional categories (such as Gene Ontology terms or pathways) relative to an appropriate background set in order to infer underlying biological processes, functions, or mechanisms.

807

Publications

271.5K

Citations

3.8K

Authors

1.2K

Institutions

Publications per year

2017–2026

331

Authors

3.8K

  1. AM
    Avi Ma’ayan

    Icahn School of Medicine at Mount Sinai, United States

    13 publications · 23.4K citations

    13

    h-index

  2. JD
    Joaquı́n Dopazo

    Centro de Investigacion Principe Felipe, Spain

    11 publications · 1.9K citations

    10

    h-index

  3. MA
    Maureen A. Sartor

    University of Michigan, United States

    8 publications · 515 citations

    8

    h-index

  4. FA
    Fátima Al‐Shahrour

    Spanish National Cancer Research Centre, Spain

    7 publications · 1.7K citations

    7

    h-index

  5. NH
    Nigam H. Shah

    Stanford University, United States

    7 publications · 396 citations

    7

    h-index

1–5 of 3.8K

Venues

  1. Nucleic Acids Research

    84 publications · 65.8K citations

    60

    h-index

  2. Bioinformatics

    108 publications · 35.8K citations

    52

    h-index

  3. BMC Bioinformatics

    96 publications · 37.2K citations

    40

    h-index

  4. PLoS ONE

    41 publications · 11.3K citations

    26

    h-index

  5. BMC Genomics

    31 publications · 1.4K citations

    19

    h-index

1–5