Sequence Scoring

Sequence Scoring is a computational method within the framework of Profile Hidden Markov Models that quantifies the likelihood of a given biological sequence being generated by the model, thereby distinguishing it from arbitrary sequences through position-specific emission and transition probabilities.

57

Publications

6.5K

Citations

132

Authors

86

Institutions

Publications per year

2017–2026

1

Authors

132

  1. SK
    Samuel Karlin

    Stanford University, United States

    6 publications · 2.2K citations

    6

    h-index

  2. RH
    Richard Hughey

    University of California, Santa Cruz, United States

    3 publications · 172 citations

    3

    h-index

  3. AD
    Amir Dembo

    Stanford University, United States

    3 publications · 269 citations

    3

    h-index

  4. RM
    Richard Mott

    Centre for Human Genetics, United Kingdom

    2 publications · 186 citations

    2

    h-index

  5. SL
    Steven L. Salzberg

    Johns Hopkins University, United States

    2 publications · 135 citations

    2

    h-index

1–5 of 132

Venues

  1. Bioinformatics

    11 publications · 733 citations

    11

    h-index

  2. BMC Bioinformatics

    6 publications · 295 citations

    6

    h-index

  3. Journal of Computational Biology

    4 publications · 425 citations

    4

    h-index

  4. Nucleic Acids Research

    3 publications · 948 citations

    3

    h-index

  5. Genome Research

    GR · 3 publications · 392 citations

    3

    h-index

1–5