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Abstract:
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At the heart of Precision Medicine is connecting the right drug/therapy to the right patient. The extensive acquisition of high-throughput molecular and drug profiling data across diverse model systems have made precision medicine efforts a realistic possibility. Modern precision medicine endeavors are at an inflexion point – facing the fundamental challenge of assimilating, organizing, analyzing and interpreting multi-domain data types to make individualized health decisions. From an analytic viewpoint, 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). Integrative analyses of these multi-domain data, combined with patients’ clinical outcomes, can help us quantify and interpret the complex biological processes that characterize a disease. This talk will cover probabilistic Bayesian statistical and computational frameworks that acknowledge and exploit these inherent complex structural relationships, for both biomarker discovery, and clinical prediction, to aid evidence-based translational and individualized medicine.
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