Bayesian Data Integration

Bayesian Data Integration is a statistical framework that uses Bayesian inference to jointly model and infer biological states or parameters from multiple, heterogeneous omics data sources by explicitly quantifying uncertainty and dependencies among data types through probabilistic graphical models.

113

Publications

10.8K

Citations

463

Authors

199

Institutions

Publications per year

2017–2026

34

Authors

463

  1. OG
    Olga G. Troyanskaya

    Princeton University, United States

    4 publications · 446 citations

    4

    h-index

  2. BK
    Bani K. Mallick

    Texas A&M University, United States

    4 publications · 225 citations

    4

    h-index

  3. AF
    A. F. M. Smith

    Imperial College London, United Kingdom

    3 publications · 664 citations

    3

    h-index

  4. KM
    Kerrie Mengersen

    Queensland University of Technology, Australia

    3 publications · 96 citations

    3

    h-index

  5. AK
    Arnab K. Maity

    Pfizer (United States), United States

    2 publications · 34 citations

    2

    h-index

1–5 of 463

Venues

  1. Bioinformatics

    11 publications · 955 citations

    11

    h-index

  2. Nucleic Acids Research

    5 publications · 3.5K citations

    5

    h-index

  3. PLoS Computational Biology

    5 publications · 262 citations

    4

    h-index

  4. IEEE Transactions on Image Processing

    4 publications · 219 citations

    4

    h-index

  5. 3

    h-index

1–5