报告摘要： The problem of modeling the dynamical regulation process within a gene network has been of great interest for a long time. We propose to model this dynamical system with a large number of nonlinear ordinary differential equations (ODEs), in which the regulation function is estimated directly from data without any parametric assumption. Most current research assumes the gene regulation network is static, but in reality, the connection and regulation function of the network may change with time or environment. This change is reflected in our dynamical model by allowing the regulation function varying with the gene expression and forcing this regulation function to be zero if no regulation happens. We introduce a statistical method called functional SCAD to estimate a time-varying sparse and directed gene regulation network, and, simultaneously, to provide a smooth estimation of the regulation function and identify the interval in which no regulation effect exists. The finite sample performance of the proposed method is investigated in a Monte Carlo simulation study. Our method is demonstrated by estimating a time-varying directed gene regulation network of 20 genes involved in muscle development during the embryonic stage of Drosophila melanogaster.
报告人简介： Dr. Jiguo Cao is the Canada Research Chair in Data Science at the Department of Statistics and Actuarial Science, Simon Fraser University (SFU). He is also the Director of Pacific Blue Cross Health Informatics Laboratory and the Associate Faculty Member at School of Computing Science, SFU. He is serving as the Associate Editor of Biometrics, Canadian Journal Statistics, Journal of Agricultural, Biological, and Environmental Statistics, and Statistics and Probability Letter. He obtained his PhD in statistics at McGill University in 2006 and worked as postdoctoral associate in statistics genetics at Yale University in 2006-2007 before joining SFU with a faculty member in 2007. Dr. Cao's research interests include functional data analysis and estimating differential equation models.