A Graph-Theoretic Method for Mining Functional Modules in Large Sparse Protein Interaction Networks

Authors:
Shihua Zhang;Hong-Wei Liu;Xue-Mei Ning;Xiang-Sun Zhang
Affiliations:
Chinese Academy of Sciences, Beijing 100080, China;Renmin University of China, Beijing 100872, China;Graduate University of Chinese Academy of Sciences, Beijing 100049, China;Chinese Academy of Sciences, Beijing 100080, China
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

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Abstract

With ever increasing amount of available data on protein-protein interaction (PPI) networks, understanding the topology of the networks and then biochemical processes in cells has become a key problem. Modular architecture which encompasses groups of genes/proteins involved in elementary biological functional units is a basic form of the organization of interacting proteins. Here we propose a method that combines the line graph transformation and clique percolation clustering algorithm to detect network modules which may overlap each other in large sparse protein-protein interaction (PPI) networks. The resulting modules by the present method show a high coverage among yeast, fly, and worm PPI networks respectively. Our analysis of the yeast PPI network suggests that most of these modules have well biological significance in context of protein localization, function annotation, and protein complexes.