Modern scientific endeavors generate high-throughput, multi-model datasets of different sizes, formats, and structures at a single subject-level. In the context of biomedicine, such data include multi-platform genomics, proteomics and imaging; and each of these distinct data types provides a different, partly independent and complementary, high-resolution view of various biological processes. Modeling and inference in such studies is challenging, not only due to high dimensionality, but also due to presence of rich structured dependencies such as serial, spatial, graphical, functional and shape-based correlations. This talk will cover probabilistic frameworks that acknowledge and exploit these inherent complex structural relationships to develop regression and clustering models, that extract maximal information from such data. These approaches will be illustrated using several biomedical case examples especially in oncology.