IntOMICS: A Bayesian Framework for Reconstructing Regulatory Networks Using Multi-Omics Data.

Anna Pačínková, Vlad Popovici
Author Information
  1. Anna Pačínková: RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic. ORCID
  2. Vlad Popovici: RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic.

Abstract

Integration of multi- 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- 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 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.

Keywords

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MeSH Term

Multiomics
Bayes Theorem
DNA Copy Number Variations
Systems Biology
Markov Chains

Word Cloud

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