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This package is deprecated. It will probably be removed from Bioconductor. Please refer to the package end-of-life guidelines for more information.

This package is for version 3.19 of Bioconductor. This package has been removed from Bioconductor. For the last stable, up-to-date release version, see .

Integrative analysis of multi-omics data to infer regulatory networks

Bioconductor version: Release (3.19)

IntOMICSr is an efficient integrative framework based on Bayesian networks. IntOMICSr systematically analyses gene expression (GE), DNA methylation (METH), copy number variation (CNV) and biological prior knowledge (B) to infer regulatory networks. IntOMICSr complements the missing biological prior knowledge by so-called empirical biological knowledge (empB), estimated from the available experimental data. An automatically tuned MCMC algorithm (Yang and Rosenthal, 2017) estimates model parameters and the empirical biological knowledge. Conventional MCMC algorithm with additional Markov blanket resampling (MBR) step (Su and Borsuk, 2016) infers resulting regulatory network structure consisting of three types of nodes: GE nodes refer to gene expression levels, CNV nodes refer to associated copy number variations, and METH nodes refer to associated DNA methylation probe(s).

Author: Pacinkova Anna [cre, aut]

Maintainer: Pacinkova Anna <ana.pacinkova at>

Citation (from within R, enter citation("IntOMICS")):


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Reference Manual PDF


biocViews Bayesian, CopyNumberVariation, DNAMethylation, GeneExpression, GeneRegulation, Network, Software, SystemsBiology
Version 1.4.0
In Bioconductor since BioC 3.17 (R-4.3) (1 year)
License GPL-3
Imports bnlearn, bnstruct, matrixStats, RColorBrewer, bestNormalize, igraph, gplots, stats, utils, graphics, numbers, SummarizedExperiment, ggplot2, ggraph, methods, cowplot, grid, rlang
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