Network Deconvolution

Network Deconvolution is a computational method that disentangles direct regulatory interactions from indirect correlations in inferred regulatory networks by mathematically removing transitive edge effects propagated through network paths.

50

Publications

12.7K

Citations

210

Authors

83

Institutions

Publications per year

2017–2026

31

Authors

210

  1. AB
    Adith Boloor

    Washington University in St. Louis, United States

    2 publications · 88 citations

    2

    h-index

  2. BH
    Bohyung Han

    Pohang University of Science and Technology, South Korea

    2 publications · 4.7K citations

    2

    h-index

  3. TC
    Tom C. Freeman

    University of Edinburgh, United Kingdom

    2 publications · 298 citations

    2

    h-index

  4. SH
    Seunghoon Hong

    Pohang University of Science and Technology, South Korea

    2 publications · 4.7K citations

    2

    h-index

  5. WQ
    Wei Qin

    Shanghai Jiao Tong University, China

    2 publications · 58 citations

    2

    h-index

1–5 of 210

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 707 citations

    5

    h-index

  2. IEEE Access

    3 publications · 166 citations

    3

    h-index

  3. Scientific Reports

    2 publications · 88 citations

    2

    h-index

  4. Journal of Intelligent Manufacturing

    2 publications · 58 citations

    2

    h-index

  5. Nature Methods

    2 publications · 57 citations

    2

    h-index

1–5