Uses probabilistic integration of cancer genomics data for combined evaluation of RNA-seq gene expression and 450K array DNA methylation measurements of promoters as well as gene bodies. The method learns the specific relationships between the data types and exploits these for biomarker discovery and classification of new samples. It also explicitly models the uncertainty of both the count-based NGS data and the continuous array data measurements. This approach is specifically tailored for cancer studies where much heterogeneity is observed among tumours. The method combines graphical model formalism with non-parametric specification of probability distributions to capture the highly context-specific relationships between methylation patterns and gene expression.