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 We present IntOMICS, an efficient integrative framework based on Bayesian networks. IntOMICS systematically analyses gene expression, DNA methylation, copy number variation and biological prior knowledge to infer regulatory networks. IntOMICS complements the missing biological prior knowledge by so-called empirical biological knowledge, estimated from the available experimental data. Regulatory networks derived from IntOMICS provide deeper insights into the complex flow of genetic information on top of the increasing accuracy trend compared to a published algorithm designed exclusively for gene expression data. The ability to capture relevant crosstalks between multi-omics modalities is verified using known associations in microsatellite stable colon cancer samples. IntOMICS is also applied to detect potential predictive biomarkers in microsatellite stable stage III colon cancer samples.
 IntOMICS is a powerful resource for exploratory systems biology and can provide valuable insights into the complex mechanisms of biological processes that has a vital role in personalised medicine.
 
-IntOMICS takes as input (i) gene expression matrix GE with m samples and n1 genes, (ii) the associated copy number variation matrix CNV (m x n2), (iii) the associated DNA methylation matrix of beta-values METH (m x n3) sampled from the same individuals, and (iv) the biological prior knowledge with information on known interactions among molecular features. An automatically tuned MCMC algorithm (Yang and Rosenthal, 2009) estimates parameters and empirical biological knowledge. Conventional MCMC algorithm with additional Markov blanket resampling step is used to infer resulting regulatory network structure consisting of three types of nodes: GE nodes refer to gene expression levels, CNV nodes refer to copy number variations, and METH nodes refer to DNA methylation. Edge weight wi represents the empirical frequency of given edge over samples of network structures.
+IntOMICS takes as input (i) gene expression matrix GE with m samples and n1 genes, (ii) the associated copy number variation matrix CNV (m x n2), (iii) the associated DNA methylation matrix of beta-values METH (m x n3) sampled from the same individuals, and (iv) the biological prior knowledge with information on known interactions among molecular features. An automatically tuned MCMC algorithm (Yang and Rosenthal, 2009) estimates parameters and empirical biological knowledge. Conventional MCMC algorithm with additional Markov blanket resampling step (Su and Borsuk, 2016) is used to infer resulting regulatory network structure consisting of three types of nodes: GE nodes refer to gene expression levels, CNV nodes refer to copy number variations, and METH nodes refer to DNA methylation. Edge weight wi represents the empirical frequency of given edge over samples of network structures.
 
 Follow the instructions in IntOMICS_workflow.Rmd.