Perturbation-based Network Inference

Perturbation-Based Network Inference is a network inference methodology that identifies regulatory interactions by analyzing system-wide responses to targeted perturbations, distinguishing itself from correlation-based or observational methods through its reliance on directed, intervention-induced changes in network dynamics.

17

Publications

1.2K

Citations

76

Authors

49

Institutions

Publications per year

2017–2026

10

Authors

76

  1. AT
    Andreas Tjärnberg

    Science for Life Laboratory, Sweden

    2 publications · 34 citations

    2

    h-index

  2. ED
    Eduardo D. Sontag

    Rutgers, The State University of New Jersey, United States

    2 publications · 118 citations

    2

    h-index

  3. EL
    Erik L. L. Sonnhammer

    Stockholm University, Sweden

    2 publications · 34 citations

    2

    h-index

  4. TE
    Torbjörn E. M. Nordling

    National Cheng Kung University, Taiwan

    2 publications · 34 citations

    2

    h-index

  5. YS
    Yishai Shimoni

    Hebrew University of Jerusalem, Israel

    1 publications · 353 citations

    1

    h-index

1–5 of 76

Venues

  1. Scientific Reports

    2 publications · 34 citations

    2

    h-index

  2. Neural Networks

    1 publications · 23 citations

    1

    h-index

  3. Experimental Cell Research

    ECR · 1 publications · 66 citations

    1

    h-index

  4. Nonlinear Dynamics

    1 publications · 21 citations

    1

    h-index

  5. Biosystems

    1 publications · 28 citations

    1

    h-index

1–5