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
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836
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28
Authors
19
Institutions
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2017–2026
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Gappy data: To Krig or not to Krig?
Hasan Güneş, Sirod Sirisup, George Em Karniadakis · Journal of Computational Physics · 2005 · 156 citations
Gap costs for multiple sequence alignment
Stephen F. Altschul · Journal of Theoretical Biology · 1989 · 140 citations
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University of Arizona
Tucson, United States
2 h-index
4 publications
76 citations
University of California San Diego
San Diego, United States
2 h-index
2 publications
71 citations
Department of Genomes & Genetics
Paris, France
1 h-index
1 publications
39 citations
Mininglamp (China)
Beijing, China
1 h-index
1 publications
23 citations

Hanze University of Applied Sciences
Groningen, The Netherlands
1 h-index
4 publications
72 citations
Wageningen University & Research
Wageningen, The Netherlands
1 h-index
3 publications
54 citations
Massachusetts Institute of Technology
Cambridge, United States
1 h-index
1 publications
232 citations
Institut Pasteur
Paris, France
1 h-index
1 publications
39 citations
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4 publications · 367 citations
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2 publications · 38 citations
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1 publications · 55 citations
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1 publications · 140 citations
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1 publications · 18 citations
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