Improving Biological Significance of Gene Expression Biclusters with Key Missing Genes

Improving Biological Significance of Gene Expression Biclusters with Key Missing Genes

时间：2010年4月29日 地点：九龙湖校区计算机楼301室

报告简介：

In an era of increasingly complex biological datasets, one of the key steps in gene functional analysis comes from clustering genes based on co-expression. Biclustering algorithms can identify gene clusters with local co-expressed patterns, which are more likely to define genes functioning together than global clustering methods. However, these algorithms are not effective in uncovering gene regulatory networks because the mined biclusters lack genes that may be critical in the function but may not be co-expressed with the clustered genes. In this project, we introduce a biclustering method called SKeleton Biclustering (SKB), which builds high quality biclusters from microarray data, creates relationships among the biclustered genes based on Gene Ontology annotations, and identifies genes that are missing in the biclusters. SKB thus defines inter-bicluster and intra-bicluster functional relationships. The delineation of functional relationships and incorporation of such missing genes may help biologists to discover biological processes that are important in a given study and provides clues for how the processes may be functioning together. We experimented with the Yeast cell cycle and Arabidopsis cold-response microarray datasets. Results show that, with SKB, a clear structure of the inter- and intra-bicluster relationships is identified, and the biological significance of the biclusters is considerably improved.

报告人简介：

Dr. Jin Chen is an Assistant Professor in the MSU-DOE Plant Research Laboratory and the Department of Computer Science and Engineering at Michigan State University. He received his Bachelor degree in Computer Science from Southeast University in China in 1997. He received his Ph.D. in Computer Science from the National University of Singapore in 2007. Before joining MSU, he was a postdoctoral research associate in the bioinformatics lab of Dr. Seung Y. Rhee at the Carnegie Institution for Science at Stanford University from 2006 to 2009. His general interests are in data mining, machine learning and bioinformatics. He is particularly interested in developing data mining and graph mining algorithms for deciphering genomic data, especially algorithms for genome-level analysis of protein/gene functions and interactions.

计算机网络和信息集成教育部重点实验室（东南大学）