題目：Statistical Methods for Integrative Analysis of Biomedical Big Data
主講人：張敏 教授（普渡大學(xué)）
時(shí)間：2016年12月16日上午10:30
地點(diǎn)：主樓216
主講人介紹：
Dr. Min Zhang is a professor of Statistics at Purdue University. She received her MD from Hebei Medical Unviersity, PhD in Neurobiology from Beijing Medical Univeristy, and PhD in Biometry from Cornell University. Her current research focuses on developing statistical methods that can extract information from biomedical big data more efficiently and effectively, including methods for quantitative trait loci mapping and genome-wide association studies. Recently she is working on integrative analysis that can applied to systems biology and precision medicine.
內(nèi)容介紹：
We developed a variable selection framework to integrate pathway information for genome-wide association analysis. Unlike Bayesian variable selection methods that rely on computation-intensive Markov chain Monte Carlo algorithms, we proposed an iterated conditional modes/medians algorithm to implement an empirical Bayes variable selection. Iterated conditional modes are first utilized to optimize values of the hyper-parameters and to implement the empirical Bayes method, and then iterated conditional medians are used to estimate the model parameters and therefore implement the variable selection function. In addition to the advantages of Bayesian inference, the proposed method enjoys efficient computation, increased statistical power of the analysis, and improved estimation of the model parameters. Extensive computer simulation studies show the superior performance of our proposed approach, and the method has been applied to real data from genome-wide association studies.
（承辦：管理科學(xué)與物流系，科研與學(xué)術(shù)交流中心）
