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.
This work is supported in part by the DOE Early Career Principal Investigator Award No. DE-FG02-04ER25611 and NSF CAREER Grant IIS-0347662.
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Ucar, D., Asur, S., Catalyurek, U., Parthasarathy, S. (2006). Improving Functional Modularity in Protein-Protein Interactions Graphs Using Hub-Induced Subgraphs. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds) Knowledge Discovery in Databases: PKDD 2006. PKDD 2006. Lecture Notes in Computer Science(), vol 4213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11871637_36
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DOI: https://doi.org/10.1007/11871637_36
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