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Title: Integrative Bayesian models for precision oncology Authors:  Veerabhadran Baladandayuthapani - UT MD Anderson Cancer Center (United States) [presenting]
Jeff Morris - MD Anderson Cancer Center (United States)
Francesco Stingo - University of Florence (Italy)
Kim-Anh Do - University of Texas MD Anderson Cancer Center (United States)
Abstract: Modern biomedicine has generated unprecedented amounts of data. A combination of clinical, environmental and public health information, proliferation of associated genomic information, and increasingly complex digital information have created unique challenges in assimilating, organizing, analyzing and interpreting such structured as well as unstructured data. 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 structured dependencies (e.g. pathway/regulatory mechanisms, serial and spatial correlations etc.). Integrative analyses of these multi-domain data combined with patients clinical outcomes can help us understand the complex biological processes that characterize a disease, as well as how these processes relate to the eventual progression and development of a disease. The aim is to cover statistical and computational frameworks that acknowledge and exploit these inherent complex structural relationships for both biomarker discovery and clinical prediction to aid translational medicine. The approaches will be illustrated using several case examples in oncology.