Probabilistic Annotation Models
Probabilistic Annotation Models is a computational framework in functional annotation integration that uses probabilistic methods to quantify uncertainty and infer the most likely functional labels for biological entities by modeling the annotation process as a stochastic phenomenon.
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4.7K
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79
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2017–2026
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Repetitive Elements May Comprise Over Two-Thirds of the Human Genome
A. P. Jason de Koning, Wanjun Gu, Todd A. Castoe et al. · PLoS Genetics · 2011 · 1.1K citations · Full text
Vikas C. Raykar, Shipeng Yu, Linda Zhao et al. · Journal of Machine Learning Research · 2010 · 1K citations
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Columbia University
New York, United States
3 h-index
6 publications
261 citations
Northeastern University
Boston, United States
3 h-index
6 publications
448 citations

Institute for Systems Biology
Seattle, United States
3 h-index
15 publications
562 citations

University of Glasgow
Glasgow, United Kingdom
2 h-index
7 publications
449 citations
Singapore-MIT Alliance for Research and Technology
Singapore, Singapore
2 h-index
2 publications
205 citations

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

University of Coimbra
Coimbra, Portugal
2 h-index
4 publications
410 citations
IBM (United States)
Armonk, United States
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4 publications
324 citations
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3 publications · 60 citations
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Repository · 2 publications · 35 citations
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