Probabilistic Sequencing Models

Probabilistic Sequencing Models is a computational framework that belongs to the class of de novo peptide sequencing methods and specifically employs statistical inference and probability distributions to determine the most likely amino acid sequence from tandem mass spectrometry data, distinguishing itself by quantifying uncertainty and incorporating probabilistic dependencies among spectral events and sequence hypotheses.

5

Publications

84

Citations

16

Authors

11

Institutions

Publications per year

2017–2026

2

Authors

16

  1. ØB
    Øyvind Bleka

    University of Oslo, Norway

    1 publications · 21 citations

    1

    h-index

  2. VP
    Vera Pawlowsky‐Glahn

    Universitat de Girona, Spain

    1 publications · 17 citations

    1

    h-index

  3. SG
    Simon Godsill

    University of Cambridge, United Kingdom

    1 publications · 18 citations

    1

    h-index

  4. JG
    Jan Graffelman

    Universitat Politècnica de Catalunya, Spain

    1 publications · 17 citations

    1

    h-index

  5. MI
    M. I. Ortego

    Universitat Politècnica de Catalunya, Spain

    1 publications · 17 citations

    1

    h-index

1–5 of 16

Venues

  1. 1

    h-index

  2. Forensic Science International Genetics

    FSI · 1 publications · 21 citations

    1

    h-index

  3. BMJ

    1 publications · 12 citations

    1

    h-index

  4. NAR Genomics and Bioinformatics

    1 publications · 17 citations

    1

    h-index

  5. BMC Bioinformatics

    1 publications · 16 citations

    1

    h-index

1–5