I will introduce a series of work on the development and application of Bayesian graphical models. The type of graphical models are designed to estimate the conditional independence structure of a set of random variables. We focus on the application of muliti-omics
cancer data, in which measurements of different genomics an proteomics features are obtained on a large number of tumor samples. Biologically, these features, such as DNA copy number, methylation, RNA and protein expression, are intricately related. Therefore,
we expect that the measurements of these features, treated as random quantities, should inform intra- and inter-genic networks. Those graphs provide deeper understanding of interactions of these molecules in the biological system, thereby helping with prognosis
of cancer patients. We show how the graphical models are deployed via massive computational effort, which leads to an online database called Zodiac (www.compgenome.org/zodiac2). Future ongoing work will also be discussed.