Abstract
Markov random field modeling has been increasingly applied to capture functional, spatial, and temporal dependence in the analysis of genomics data. In this presentation, the speaker will discuss the use of Markov random field models to prioritize genes for follow-up studies from genome wide association studies and to identify differentially expressed genes from spatially and temporally collected gene expression data. Using data from Crohn's disease, Parkinson's disease, and brain tissue expression data as examples, the speaker will show that this framework leads to more replicable and biologically meaningful results.
About the speaker
Prof Hongyu Zhao received his PhD in Statistics from the University of California at Berkeley in 1995. He is currently the Ira V. Hiscock Professor of Biostatistics and Professor of Statistics and Genetics at Yale University.
Prof Zhao's research interests are the applications of statistical methods in molecular biology, genetics, drug developments, and personalized medicine. He has published over 300 articles in statistics, human genetics, bioinformatics, and proteomics, and edited two books on human genetics analysis and statistical genomics. He is a Co-Editor of Statistics in Biosciences, and serves on the editorial boards of several leading statistical and genetics journals.
Prof Zhao is a Fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics. He was the recipient of the Mortimer Spiegelman Award for a top statistician in health statistics under the age of 40 awarded by the American Public Health Association.
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