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
Parents
113
1–5 of 113
463
4
h-index
4
h-index
3
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199
Texas A&M University
College Station, United States
5 h-index
10 publications
642 citations
Princeton University
Princeton, United States
4 h-index
16 publications
2.1K citations
Duke University
Durham, United States
4 h-index
9 publications
784 citations
University of Cambridge
Cambridge, United Kingdom
3 h-index
14 publications
2.4K citations

Imperial College London
London, United Kingdom
3 h-index
3 publications
629 citations

Johns Hopkins University
Baltimore, United States
3 h-index
4 publications
190 citations

University of British Columbia
Vancouver, Canada
3 h-index
6 publications
444 citations

Pacific Northwest National Laboratory
Richland, United States
3 h-index
15 publications
206 citations
1–8 of 199
11 publications · 955 citations
11
h-index
5 publications · 3.5K citations
5
h-index
5 publications · 262 citations
4
h-index
4 publications · 219 citations
4
h-index
3 publications · 643 citations
3
h-index
1–5