Sparse Network Learning

Sparse Network Learning is a machine learning framework that belongs to the class of network inference methods, distinguished by its incorporation of sparsity-inducing constraints or priors to identify minimal sets of significant regulatory interactions from high-dimensional biological data.

222

Publications

13.3K

Citations

972

Authors

365

Institutions

Publications per year

2017–2026

146

Authors

972

  1. MP
    Mykola Pechenizkiy

    Eindhoven University of Technology, Netherlands

    6 publications · 126 citations

    6

    h-index

  2. DC
    Decebal Constantin Mocanu

    Eindhoven University of Technology, Netherlands

    6 publications · 126 citations

    6

    h-index

  3. TC
    Tianlong Chen

    The University of Texas at Austin, United States

    4 publications · 146 citations

    4

    h-index

  4. UE
    Utku Evci

    Google (United States), United States

    4 publications · 287 citations

    4

    h-index

  5. ZW
    Zhangyang Wang

    The University of Texas at Austin, United States

    4 publications · 146 citations

    4

    h-index

1–5 of 972

Venues

  1. arXiv (Cornell University)

    Repository · 42 publications · 2.5K citations

    24

    h-index

  2. Neurocomputing

    7 publications · 228 citations

    6

    h-index

  3. Expert Systems with Applications

    5 publications · 259 citations

    5

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 181 citations

    4

    h-index

  5. Bioinformatics

    3 publications · 154 citations

    3

    h-index

1–5