Kernel-based Network Inference

Kernel-Based Network Inference is a computational method within regulatory network inference that employs kernel functions to model nonlinear dependencies among biological entities, distinguishing itself by leveraging reproducing kernel Hilbert spaces to infer regulatory interactions from high-dimensional genomics data.

24

Publications

1.1K

Citations

76

Authors

48

Institutions

Publications per year

2017–2026

8

Authors

76

  1. FD
    Florence d’Alché–Buc

    Informatique, BioInformatique, Systèmes Complexes, France

    3 publications · 120 citations

    3

    h-index

  2. KT
    Koji Tsuda

    The University of Tokyo, Japan

    2 publications · 187 citations

    2

    h-index

  3. PG
    Pierre Geurts

    University of Liège, Belgium

    2 publications · 81 citations

    2

    h-index

  4. JV
    Jean‐Philippe Vert

    École Nationale Supérieure des Mines de Paris, France

    2 publications · 241 citations

    2

    h-index

  5. EN
    Eugene Novikov

    Institut Curie, France

    1 publications · 17 citations

    1

    h-index

1–5 of 76

Venues

  1. Bioinformatics

    3 publications · 355 citations

    3

    h-index

  2. BMC Bioinformatics

    3 publications · 112 citations

    3

    h-index

  3. 1

    h-index

  4. Mathematics

    1 publications · 23 citations

    1

    h-index

  5. BMC Systems Biology

    1 publications · 17 citations

    1

    h-index

1–5