Integration of multi-omicsdata 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-omicsdata 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 anempiricalbiological 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.