题目：A steganalysis approach to genome-wide motif identification and biological applications
报告人：章伟雄
地点：东五楼二楼210学术报告厅
时间：6月26上午9点

报告摘要
　　Systematic identification of cis-regulatory elements (motifs) in a genome scale is a challenging, big data analysis problem. We developed a novel, effective, steganalysis-based approach for genome-wide motif finding, called WordSpy. The key idea underlying our approach is to view the promoter regions of the genes of interest as a stegoscript with motifs embedded in 'background' sequences, which is implemented in an recursive Expectation-Maximixation (EM) algorithm. We applied WordSpy to the promoters of cell-cycle-related genes of Saccharomyces cerevisiae and Arabidopsis thaliana, identifying all known cell-cycle motifs with high ranking. WordSpy can discover a complete set of cis-elements and facilitate the systematic study of regulatory networks. In this talk, I will also present the results on regulation of genes involved in Alzheimer’s disease and on transcriptional regulation of microRNA genes in mammalian species (e.g., human and mouse) and plants (e.g., rice and Arabidopsis).

报告人简介
　　Dr. Weixiong Zhang is a full professor of Computer Science and of Genetics at Washington University in St. Louis, MO, USA. He received his BS and MS in Computer Engineering from Tsinghua University and his MS and PhD in Computer Science from UCLA. His main research interests include computational biology and artificial intelligence. He has published more than 130 research papers in journals and peer-reviewed conferences and one research monograph in these areas. In Artificial Intelligence, he has made significant contributions to heuristic search and combinatorial optimization; most of his results in this area have been published in more than 10 papers in Artificial Intelligence, the premier journal of the field of AI. He developed the fastest algorithm, named as Zhang Algorithm, for the asymmetric Traveling Salesman Problem. He won the Outstanding Paper Award of National Conference on AI (AAAI-2010), a leading conference in AI, in 2010, for his work in planning. In recent years, he has been focusing on developing methods and tools for analyzing large scale biological data for transcriptome modeling, analyzing noncoding small RNA gene regulation, understanding genome-wide genotype-phenotype associations, as well as their applications to complex human diseases, such as Alzheimer’s disease and psoriasis, and plant stress tolerance in rice, cassava, soybean and Arabidopsis. His research has been supported by NIH, NSF, USDA, DARPA, the Alzheimer’s Association and Monsanto Company. He is currently a Deputy Editor of PLoS Computational Biology and an Associate Editor of Artificial Intelligence. More information of his research can be found at http://www.cse.wustl.edu/~zhang.