报告时间：2018年9月28日 14：30-15：30
报告地点：明德主楼1016会议室
报告主题：Network-based feature screening with applications to Genome data
报告摘要：Modern biological techniques have led to various types of data, which are often used to identify important biomarkers for certain diseases with appropriate statistical methods, such as feature screening. Model-free feature screening has been extensively studied in the literature, and it is effective to select useful predictors for ultra-high dimensional data. These existing screening procedures are conducted based on certain marginal correlations between predictors and a response variable, therefore network structures connecting the predictors are usually ignored. Google’s PageRank algorithm has achieved remarkable success. We adopt its spirit to adjust original screening approaches by incorporating the network information. We can then significantly improve the performance of those screening methods in choosing useful biomarkers, which is demonstrated in an intensive simulation study. A couple of real genome datasets along with a biological network are further analyzed by comparing results on both accuracy of predicting responses and stability of identifying biomarkers.
个人简介：吴梦云，上海财经大学统计与管理学院副教授。2013年获得中山大学概率论与数理统计博士学位，并与2016年8月至2018年7月在耶鲁大学生物统计系进行博士后研究。主要研究方向为高维数据变量选择、网络模型及整合分析等。目前，已在The Annals of Applied Statistics、Statistics in Medicine、Briefings in Bioinformatics、Genetic Epidemiology、Genomics等期刊发表多篇学术论文。国际统计学会当选会员（Elected Member, International Statistical Institute ISI），主持并完成了国家自然科学青年基金项目。