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.

56

Publications

4.7K

Citations

178

Authors

79

Institutions

Publications per year

2017–2026

13

Authors

178

  1. YY
    Yan Yan

    University of Chinese Academy of Sciences, China

    3 publications · 224 citations

    3

    h-index

  2. JD
    Jennifer Dy

    Northeastern University, United States

    3 publications · 224 citations

    3

    h-index

  3. RR
    Rómer Rosales

    LinkedIn (United States), United States

    3 publications · 224 citations

    3

    h-index

  4. GF
    Glenn Fung

    University of Wisconsin–Madison, United States

    3 publications · 224 citations

    3

    h-index

  5. RB
    Rainer Breitling

    University of Groningen, Netherlands

    2 publications · 94 citations

    2

    h-index

1–5 of 178

Venues

  1. BMC Bioinformatics

    3 publications · 60 citations

    3

    h-index

  2. Bioinformatics

    3 publications · 142 citations

    3

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 35 citations

    2

    h-index

  4. 1

    h-index

  5. Neurocomputing

    1 publications · 35 citations

    1

    h-index

1–5