Partial Correlation Network Inference

Partial Correlation Network Inference is a statistical method for constructing regulatory networks by estimating pairwise conditional dependencies between variables, where the genus is network inference methods and the differentia is its use of partial correlation coefficients to identify direct interactions while controlling for the influence of other variables.

37

Publications

2.8K

Citations

144

Authors

98

Institutions

Publications per year

2017–2026

16

Authors

144

  1. EI
    Eiko I. Fried

    Leiden University, Netherlands

    4 publications · 540 citations

    4

    h-index

  2. SM
    S. M. Minhaz Ud-Dean

    Institute for Biomedical Engineering, Switzerland

    2 publications · 282 citations

    2

    h-index

  3. BL
    Brigitte Lueger‐Schuster

    University of Vienna, Austria

    2 publications · 137 citations

    2

    h-index

  4. KH
    Kristof Hoorelbeke

    Ghent University, Belgium

    2 publications · 36 citations

    2

    h-index

  5. NP
    Nan Papili Gao

    ETH Zurich, Switzerland

    2 publications · 282 citations

    2

    h-index

1–5 of 144

Venues

  1. PLoS ONE

    3 publications · 157 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 35 citations

    2

    h-index

  3. Bioinformatics

    2 publications · 815 citations

    2

    h-index

  4. Briefings in Bioinformatics

    1 publications · 38 citations

    1

    h-index

  5. 1

    h-index

1–5