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
Parents
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132
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86
Stanford University
Stanford, United States
7 h-index
12 publications
4.3K citations
University of California, Santa Cruz
Santa Cruz, United States
5 h-index
11 publications
1.3K citations

Centre National de la Recherche Scientifique
Paris, France
4 h-index
8 publications
367 citations
National Center for Biotechnology Information
Bethesda, United States
2 h-index
2 publications
1.9K citations

Johns Hopkins University
Baltimore, United States
2 h-index
2 publications
135 citations

Karolinska Institutet
Stockholm, Sweden
2 h-index
4 publications
204 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
2 h-index
5 publications
284 citations

Uppsala University
Uppsala, Sweden
2 h-index
6 publications
357 citations
1–8 of 86
11 publications · 733 citations
11
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6 publications · 295 citations
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4 publications · 425 citations
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3 publications · 948 citations
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GR · 3 publications · 392 citations
3
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1–5