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Home | Seminars and Symposia | Past seminars/symposia: Friday, April 24, 2009

Pathway and Gene Target Identification via Weighted Gene Co-Expression Network Analysis

by

Bin Zhang
Genetics Department
Merck and Co., Inc.

Friday, April 24, 2009
12:00 Lunch
12:15 Seminar

401/402 Walter Library

The unprecedented volumes of large-scale genomic and genetic data being generated today, combined with the poor understanding of the genetics underlying complex biological systems, demand a systems biology approach to identify a global landscape of interactomes that contribute to a variety of clinical endpoints like tumor progression and obesity. Recent progress in this direction has uncovered gene modules or subnetworks associated with a number of diseases and enables further prioritization of gene targets. One of the key approaches in these studies is weighted gene co-expression network analysis. Different from the traditional un-weighted network approach, the weighted network analysis preserves the continuous nature of interactions and is robust to parameter selection, and thus enables establishing higher-order relationships among the nodes in the network. In this talk, I will review the analysis procedure and highlight several key applications.

 

Dr. Bin Zhang is a Research Fellow in the Genetics Department at Merck & Co. His expertise lies in bioinformatics and computational biology, pattern recognition and data mining. Dr. Zhang developed a theory for weighted gene co-expression network analysis which has been extensively employed to identify pathways and gene targets involved in a variety of diseases such as cancer, atherosclerosis, Alzheimer's, obesity and diabetes etc. One of such applications was selected as the second most influential brain tumor publication in the year 2006 by the brain tumor research portal at www.braintumour.net. His recent research along this direction has been highlighted by Nature (http://www.nature.com/news/2008/080314/full/news.2008.669.html). Dr. Zhang's early research on image pattern recognition significantly contributed to several large-scale pattern recognition systems of national interest including U.S. Handwritten Address Identification System, United Kingdom Handwritten Address Identification System and Handwritten Document Comparison System. Dr. Zhang has published over 20 journal papers, including a number of high profile papers in Nature, PNAS, and Nature Genetics, 1 book chapter, 23 peer reviewed conference papers and 12 abstracts. Dr. Zhang is the recipient of the Best Paper Award of ICDAR 2003 — the Seventh International Conference on Document Analysis and Recognition. Dr. Zhang joined Merck & Co. as a senior research scientist in 2005 and then was promoted as Research Fellow in 2007. Prior to joining Merck & Co., Dr. Zhang was a post-doctoral fellow from 2003 to 2004 and then a Research Faculty and Senior Biostatistician at David Geffen Medical School of University of California at Los Angeles. Dr. Zhang holds a Ph.D. and a master degree in Computer Science from the State University of New York at Buffalo, a master degree in electronic engineering from Tsinghua University, Beijing, China, and a bachelor's degree in electrical engineering from Tongji University, Shanghai, China.