Module-based Network Inference

Module-Based Network Inference is a network inference framework that identifies and leverages modular structures—functionally coherent subnetworks of highly interconnected nodes—to improve the accuracy and interpretability of inferred biological or complex networks by integrating module-level connectivity patterns as prior knowledge or modeling constraints.

23

Publications

2.2K

Citations

80

Authors

52

Institutions

Publications per year

2017–2026

7

Authors

80

  1. TM
    Tom Michoel

    University of Edinburgh, United Kingdom

    3 publications · 184 citations

    3

    h-index

  2. AO
    Alfred O. Hero

    University of Michigan, United States

    2 publications · 58 citations

    2

    h-index

  3. AR
    Arvind Rao

    The University of Texas MD Anderson Cancer Center, United States

    2 publications · 58 citations

    2

    h-index

  4. ÉB
    Éric Bonnet

    Ghent University, Belgium

    2 publications · 169 citations

    2

    h-index

  5. YB
    Yutong Bai

    Johns Hopkins University, United States

    2 publications · 126 citations

    2

    h-index

1–5 of 80

Venues

  1. Automatica

    2 publications · 241 citations

    2

    h-index

  2. BMC Bioinformatics

    2 publications · 63 citations

    2

    h-index

  3. BMC Genomics

    2 publications · 53 citations

    2

    h-index

  4. Evolutionary Biology

    1 publications · 16 citations

    1

    h-index

  5. Physical Review Letters

    PRL · 1 publications · 247 citations

    1

    h-index

1–5