Improving functional modularity in protein-protein interactions graphs using hub-induced subgraphs

Authors:
Duygu Ucar;Sitaram Asur;Umit Catalyurek;Srinivasan Parthasarathy
Affiliations:
Department of Computer Science and Engineering, The Ohio State University;Department of Computer Science and Engineering, The Ohio State University;Department of Biomedical Informatics, The Ohio State University;Department of Computer Science and Engineering, The Ohio State University
Venue:
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Year:
2006

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Abstract

Dense subgraphs of Protein-Protein Interaction (PPI) graphs are believed to be potential functional modules and play an important role in inferring the functional behavior of proteins. PPI graphs are known to exhibit the scale-free property in which a few nodes (hubs) are highly connected. This scale-free topology of PPI graphs makes it hard to isolate dense subgraphs effectively. In this paper, we propose a novel refinement method based on neighborhoods and the biological importance of hub proteins. We show that this refinement improves the functional modularity of the PPI graph and leads to effective clustering into dense components. A detailed comparison of these dense components with the ones obtained from the original PPI graph reveal three major benefits of the refinement: i) Enhancement of existing functional groupings; ii) Isolation of new functional groupings; and iii) Soft clustering of multifunctional hub proteins to multiple functional groupings.