In the postgenome era, multiple types of molecular data for the same set of samples are often available and should be analyzed jointly in an integrative analysis in order to maximize the information gain. Bayesian methods are particularly well suited for integrating different biological data sources. In this article, we cover crucial tasks and corresponding methods with a focus on integrative analyses. We emphasize gene prioritization, model-based cluster approaches for subgroup identification, regression modeling, and prediction, as well as structure learning using network models. Our review introduces prior concepts for sparsity and variable selection and concludes with some aspects on validation and computation.


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