header_image

Pattern mining across massive biological networks for functional discovery

Dr. Haiyan Hu
Friday, April 11, 2008
3:00PM ~ 4:30PM, HPA 110

Abstract


The rapid accumulation of biological network data translates into an urgent need for computational methods for graph pattern mining. One important problem is to identify recurrent patterns across multiple networks to discover biological modules. However, existing algorithms for frequent pattern mining become very costly in time and space as the pattern sizes and network numbers increase. Currently, no efficient algorithm is available for mining recurrent patterns across large collections of genome-wide networks.

In this talk, I will first describe a novel algorithm, CODENSE, to efficiently mine frequent coherent dense subgraphs across large numbers of massive graphs. In order to further analyze the dynamics of the biological networks, I will introduce another efficient algorithm NETBICLUSTER to identify condition specific activation of network modules. I will also discuss the possible applications of the algorithms.

Short Bio


Dr. Haiyan Hu got his Ph.D. degree in Computer Science from the University of Southern California in 2006 and is currently a Visiting Research Assistant Professor at Indiana University.

FEEDBACK | Webmaster | EECS | FSI | CECS | UCF
University Of Central Florida | Orlando, Florida 32816-2362 Phone: 407-823-2341