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

Authors

237

  1. OS
    Oliver Serang

    Harvard University, United States

    3 publications · 85 citations

    3

    h-index

  2. VX
    Vincent Xue

    Hunter College, United States

    2 publications · 490 citations

    2

    h-index

  3. JI
    John Ingraham

    Harvard University, United States

    2 publications · 490 citations

    2

    h-index

  4. JL
    Justin L. MacCallum

    University of Calgary, Canada

    2 publications · 296 citations

    2

    h-index

  5. AS
    Anders S. Christensen

    University of Copenhagen, Denmark

    2 publications · 57 citations

    2

    h-index

1–5 of 237

Venues

  1. Journal of Proteome Research

    JPR · 4 publications · 322 citations

    4

    h-index

  2. Bioinformatics

    4 publications · 298 citations

    4

    h-index

  3. Proteins Structure Function and Bioinformatics

    3 publications · 187 citations

    3

    h-index

  4. Molecular & Cellular Proteomics

    MCP · 3 publications · 98 citations

    3

    h-index

  5. Proceedings of the National Academy of Sciences

    PNAS · 2 publications · 296 citations

    2

    h-index

1–5