Home > Events > 2011 Seminars & Colloquia > HYONHO CHUN - Purdue University

HYONHO CHUN - Purdue University

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"Gene regulation network inference with joint Gaussian graphical models and pathway enrichment analysis”
When
01 December 2011 from 4:00 PM to 5:00 PM
Where
111 Tyson Bldg.
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Revealing networks of biological components is one of the key questions in systems biology, and it has potential applications in understanding disease physiology and drug discovery in the area of network medicine. With epitomized microarrays, we now measure multiple genes’ expression simultaneously, and, thereby, we are able to statistically infer gene regulation networks from data. Gaussian graphical models (GGMs) have proven to be useful for this purpose when gene expressions are measured from multiple samples from a single tissue/condition by modeling Markov dependence among genes; however, in many recent studies, gene expressions have been measured from multiple tissues/conditions. Therefore, in the present study, we employ a GGM estimation procedure with joint sparsity to efficiently utilize data from multiple sources. To impose joint sparsity, we use a class of nonconvex penalty functions, which is capable of regularizing both group- and individual-level regulations as well as has sparsistency in identifying regulations that do not occur under any condition. We demonstrate the performance of our approach by simulation and then apply it to pathway enrichment analysis with a gene expression dataset from the GenCord project, which reveals pathways that are enriched by active gene regulations in umbilical cord tissues.

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