Title: Mining coherent dense subgraphs across multiple biological networks (joint work with H. Hu, Y. Huang, J. Han and X. Zhou) Slide set [PDF] 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. We developed a novel algorithm, CODENSE, to efficiently mine frequent coherent dense subgraphs across large numbers of massive graphs. Applying CODENSE to 39 co-expression networks derived from microarray datasets, we discovered a large number of functionally homogeneous clusters and made functional predictions for 169 uncharacterized yeast genes. Availability: http://zhoulab.usc.edu/CODENSE/ Biography: Mr. Xifeng Yan is a fifth-year Ph.D. student in the Department of Computer Science at the University of Illinois. His research interests include data mining, structural/graph pattern mining, and their applications in database systems and bioinformatics.
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