Bayesian Protein Inference
Bayesian Protein Inference is a computational framework within the field of proteomics that applies Bayesian statistical methods to probabilistically infer the presence and abundance of proteins from mass spectrometry data, distinguishing itself by explicitly modeling uncertainty and incorporating prior knowledge to resolve ambiguities arising from shared peptides among proteins.
50
Publications
4K
Citations
237
Authors
112
Institutions
Publications per year
2017–2026
10
Parents
50
Illuminating protein space with a programmable generative model
John Ingraham, Max Baranov, Zak Costello et al. · Nature · 2023 · 403 citations · Full text
The ProDom database of protein domain families: more emphasis on 3D
C. Bru · Nucleic Acids Research · 2004 · 358 citations · Full text
Phylogeny and evolution of the major intrinsic protein family
Rafael Zardoya · Biology of the Cell · 2005 · 285 citations
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112

Howard Hughes Medical Institute
Chevy Chase, United States
3 h-index
9 publications
1.6K citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
3 publications
458 citations
Harvard University
Cambridge, United States
3 h-index
6 publications
325 citations
University of Michigan
Ann Arbor, United States
2 h-index
5 publications
669 citations
Stony Brook University
Stony Brook, United States
2 h-index
4 publications
592 citations
Indiana University Bloomington
Bloomington, United States
2 h-index
7 publications
596 citations
Goethe University Frankfurt
Frankfurt am Main, Germany
2 h-index
2 publications
121 citations
Intarcia Therapeutics (United States)
Boston, United States
2 h-index
37 publications
11.4K citations
1–8 of 112
JPR · 4 publications · 322 citations
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4 publications · 298 citations
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3 publications · 187 citations
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MCP · 3 publications · 98 citations
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PNAS · 2 publications · 296 citations
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