IntOMICS: A Bayesian Framework for Reconstructing Regulatory Networks Using Multi-Omics Data
Language English Country United States Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
36961919
PubMed Central
PMC10178929
DOI
10.1089/cmb.2022.0149
Knihovny.cz E-resources
- Keywords
- Bayesian networks, integrative analysis, multi-omics, regulatory network,
- MeSH
- Bayes Theorem MeSH
- Markov Chains MeSH
- Multiomics * MeSH
- Systems Biology MeSH
- DNA Copy Number Variations * MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components. We present a new comprehensive R/Bioconductor-package, IntOMICS, which implements a Bayesian framework for multi-omics data integration. IntOMICS adopts a Markov Chain Monte Carlo sampling scheme to systematically analyze gene expression, copy number variation, DNA methylation, and biological prior knowledge to infer regulatory networks. The unique feature of IntOMICS is an empirical biological knowledge estimation from the available experimental data, which complements the missing biological prior knowledge. IntOMICS has the potential to be a powerful resource for exploratory systems biology.
Faculty of Informatics Masaryk University Brno Czech Republic
RECETOX Faculty of Science Masaryk University Brno Czech Republic
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