Home > Events > 2011 Seminars & Colloquia > Michael Newton - University of Wisconsin Madison

Michael Newton - University of Wisconsin Madison

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A problem in statistical genomics is to examine the points of contact between genomic data generated experimentally and exogenous information about gene function. The purpose of such data integration may be to summarize extensive gene-level data into manageable units, or it may be to enhance the signal to noise ratio through set-level averaging. In either case there are unique statistical problems with such data integration. I will review several statistical approaches and examine them in examples from cancer virology and flu replication genomics. Included will be a discussion of a new ``role model'', which aims to address pleiotropy and the spurious association of functional categories with changes in cellular state. When inferring non-null behavior of a functional category, role-model computations incorporate not only data on genes in that category, but also other functional attributes of these genes. Some difficult computational challenges emerge from this approach.