tailieunhanh - Báo cáo sinh học: "A novel functional module detection algorithm for protein-protein interaction networks"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: A novel functional module detection algorithm for protein-protein interaction networks. | Algorithms for Molecular Biology BioMed Central Research A novel functional module detection algorithm for protein-protein interaction networks Woochang Hwang 1 Young-Rae Cho1 Aidong Zhang1 and Murali Ramanathan2 Open Access Address Department of Computer Science and Engineering State University of New York at Buffalo USA and - Department of Pharmaceutical Sciences State University of New York at Buffalo USA Email Woochang Hwang - whwang2@ Young-Rae Cho - ycho8@ Aidong Zhang - azhang@ Murali Ramanathan - murali@ Corresponding author Published 05 December 2006 Received 24 July 2006 Algorithms for Molecular Biology 2006 1 24 doi 1748-7188-1 -24 Accepted 05 December 2006 This article is available from http content 1 1 24 2006 Hwang et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks termed STM. Results STM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique quasi-clique minimum cut betweeness cut and Markov Clustering MCL algorithms. The clusters obtained by each technique are compared for enrichment of .