Precision Health Seminar: Integrative Big Data Models for Precision Medicine

Ann Arbor MI 01-17-2019 01-17-2019 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. This talk will 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. Speaker: Veera Baladandayuthapani, professor of Biostatistics at the University of Michigan School of Public Health Learn more: https://precisionhealth.umich.edu/events/january-2019-seminar/

A talk by Veera Baladandayuthapani

January 17, 2019
2:00 pm - 3:00 pm
Palmer Commons, 4th floor, Forum Hall
Contact Information: Clarissa Piatek, clariggy@med.umich.edu

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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. This talk will 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. Speaker: Veera Baladandayuthapani, professor of Biostatistics at the University of Michigan School of Public Health Learn more: https://precisionhealth.umich.edu/events/january-2019-seminar/