主 题：Incorporating genome-wide co-expression information to improve differential expression analysis
主讲人：Yinglei Lai副教授
主持人：林华珍教授
时 间：2015年4月21日下午2：00-3：00
地 点：通博楼B212学术会议室
主办单位：统计研究中心 统计学院 科研处
主讲人简介：
Yinglei Lai received his B.S degrees in Information & Computation Sciences and Business Administration in 1999 from the University of Science of Technology of China, and his Ph.D degree in Applied Mathematics in 2003 from the University of Southern California. After his postdoctoral training during 2003-2004 at Yale University School of Medicine, he joined The George Washington University as a faculty member in the Department of Statistics. His research interest is to develop statistical and computational methods in bioinformatics, computational biology and biostatistics.
内容提要：
The control of false positives in differential expression analysis remains a major challenge although many statistical methods have been proposed for its improvement. Since genes interact with each other during cellular and molecular processes, an efficient incorporation of genome-wide co-expression information can significantly improve the detection of differential expression. We will address our recent research progress in this direction.
