Gap Penalty Learning

Gap Penalty Learning is a computational method in sequence alignment algorithms that systematically infers optimal gap penalty parameters by learning from biological or artificial sequence data to distinguish homologous from non-homologous alignments.

12

Publications

836

Citations

28

Authors

19

Institutions

Publications per year

2017–2026

2

Authors

28

  1. SF
    Stephen F. Altschul

    National Institutes of Health, United States

    3 publications · 436 citations

    3

    h-index

  2. ML
    Michael L. Fredman

    University of California San Diego, United States

    2 publications · 71 citations

    2

    h-index

  3. JK
    John Kececioglu

    University of Arizona, United States

    2 publications · 38 citations

    2

    h-index

  4. SW
    Sven Warris

    Wageningen University & Research, Netherlands

    1 publications · 18 citations

    1

    h-index

  5. HG
    Hasan Güneş

    Istanbul Technical University, Türkiye

    1 publications · 156 citations

    1

    h-index

1–5 of 28

Venues

  1. Bulletin of Mathematical Biology

    4 publications · 367 citations

    4

    h-index

  2. 2

    h-index

  3. Computer Methods and Programs in Biomedicine

    1 publications · 55 citations

    1

    h-index

  4. Journal of Theoretical Biology

    1 publications · 140 citations

    1

    h-index

  5. PLoS ONE

    1 publications · 18 citations

    1

    h-index

1–5