This article is part of the supplement: Highlights from the Fourth International Society for Computational Biology (ISCB) Student Council Symposium .

Oral presentation

Functional module detection by functional flow pattern mining in protein interaction networks

Young-Rae Cho , Lei Shi and Aidong Zhang

Department of Computer Science, State University of New York at Buffalo, NY 14260, USA

author email corresponding author email

from Fourth International Society for Computational Biology (ISCB) Student Council Symposium
Toronto, Canada. 18 July 2008

BMC Bioinformatics 2008, 9(Suppl 10):O1doi:10.1186/1471-2105-9-S10-O1

Published:	30 October 2008

First paragraph (this article has no abstract)

A functional module has been defined as a group of molecules that participate in the same functional activities. Various graph-theoretic or data-mining techniques have been applied to discover functional modules from protein interaction networks [1]. However, their performance has been compromised by false-positive and false-negative interaction data and complex connectivity of the interaction networks. In our earlier study [2], we have introduced the functional flow-based approach to efficiently identify overlapping modules, which are generally large-sized, from interaction networks. In this abstract, we extend this approach by mining functional flow patterns for the purpose of detecting small-sized modules for specific functions.