讲座题目：High-Dimensional Nonparametric ODE Models for Dynamic Gene Regulation Networks

主 讲 人：Tao Lu博士

讲座时间：2018年5月17日10:10

地 点：理学院钱伟长楼201会议室

欢迎有兴趣的师生前来聆听！

        理学院

         2018年5月15日

讲座内容简介：The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. High-dimensional linear ODE models have been proposed to identify GRNs, but with a limitation of the linear regulation effect assumption. In this talk, I present a nonparametric ODE models, coupled with the two-stage smoothing-based ODE estimation methods and adaptive group LASSO techniques, to model dynamic GRNs that could flexibly deal with nonlinear regulation effects. The method has sound theoretical properties and some benefits in computational efficiency and estimation accuracy. An example for identifying the nonlinear dynamic GRN of T-cell activation is used to illustrate the application of this model.

主讲人简介：Tao Lu博士，毕业于美国罗彻斯特大学统计专业，先后任职于美国纽约州立大学，内华达大学数学统计系助理教授，副教授，博士生导师。主要研究工作是发展基于微分方程的统计模型方法及其应用。至今已发表SCI论文40余篇，包括统计学顶级期刊Journal of the American Statistical Associatio，统计学权威期刊Biometrics, Stat. Med., Statistical Methods in Medical Research, Annals of Applied Statistics等，主持承担了200万人民币的科研项目。Tao Lu博士是当今国际上活跃的青年学者，受邀在多个国际会议上作邀请报告。担任统计学期刊Journal of Biopharmaceutical Statistics 和 Biometrical Journal的副主编以及PLOS ONE和PEERJ的主编, 担任美国宇航局宇航员健康研究项目统计评审，以及众多学术期刊的审稿人。