Gap Penalty Optimization

Gap Penalty Optimization is a computational method in sequence alignment that determines the optimal values for gap penalties by balancing the trade-off between alignment accuracy and biological plausibility, distinguishing it from other alignment strategies through its focus on systematically refining gap cost parameters to enhance global or local homology inference.

39

Publications

4.5K

Citations

88

Authors

53

Institutions

Publications per year

2017–2026

4

Authors

88

  1. SF
    Stephen F. Altschul

    National Institutes of Health, United States

    3 publications · 436 citations

    3

    h-index

  2. HZ
    Haowen Zhang

    Georgia Institute of Technology, United States

    2 publications · 52 citations

    2

    h-index

  3. OG
    Osamu Gotoh

    Saitama Cancer Center, Japan

    2 publications · 171 citations

    2

    h-index

  4. ML
    Michael L. Fredman

    University of California San Diego, United States

    2 publications · 71 citations

    2

    h-index

  5. YG
    Yu Gao

    Georgia Institute of Technology, United States

    2 publications · 52 citations

    2

    h-index

1–5 of 88

Venues

  1. Bulletin of Mathematical Biology

    6 publications · 538 citations

    6

    h-index

  2. Protein Engineering Design and Selection

    PEDS · 3 publications · 206 citations

    3

    h-index

  3. Journal of Theoretical Biology

    3 publications · 173 citations

    3

    h-index

  4. Journal of Molecular Biology

    JMB · 2 publications · 1.3K citations

    2

    h-index

  5. Molecular Biology and Evolution

    MBE · 2 publications · 198 citations

    2

    h-index

1–5