Regulatory Network Inference

Regulatory Network Inference is a computational and statistical methodology in systems biology that reconstructs the structure and, often, the directionality and strength of interactions in gene or cellular regulatory networks from high-dimensional molecular measurements such as bulk or single-cell transcriptomic and epigenomic data.

1.7K

Publications

129.5K

Citations

5.6K

Authors

1.6K

Institutions

Publications per year

2017–2026

534

Authors

5.6K

  1. JQ
    John Quackenbush

    Dana-Farber Cancer Institute, United States

    19 publications · 737 citations

    17

    h-index

  2. SM
    Satoru Miyano

    The University of Tokyo, Japan

    20 publications · 1K citations

    17

    h-index

  3. FE
    Frank Emmert‐Streib

    Tampere University, Finland

    17 publications · 1K citations

    15

    h-index

  4. RB
    Richard Bonneau

    New York University, United States

    16 publications · 2.2K citations

    15

    h-index

  5. AC
    Andrea Califano

    Columbia University, United States

    16 publications · 5K citations

    14

    h-index

1–5 of 5.6K

Venues

  1. Bioinformatics

    210 publications · 22.2K citations

    65

    h-index

  2. BMC Bioinformatics

    112 publications · 8.3K citations

    40

    h-index

  3. PLoS Computational Biology

    69 publications · 4.8K citations

    38

    h-index

  4. PLoS ONE

    89 publications · 5.7K citations

    36

    h-index

  5. Nucleic Acids Research

    57 publications · 4.2K citations

    35

    h-index

1–5