Finding cliques in protein interaction networks via transitive closure of a weighted graph

Authors:
Chris Ding;Xiaofeng He;Hanchuan Peng
Affiliations:
University of California, Berkeley, CA;University of California, Berkeley, CA;University of California, Berkeley, CA
Venue:
Proceedings of the 5th international workshop on Bioinformatics
Year:
2005

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Abstract

Finding protein functional modules in protein interaction networks amounts to finding densely connected subgraphs. Standard methods such as cliques and k-cores produce very small subgraphs due to highly sparse connections in most protein networks. Furthermore, standard methods are not applicable on weighted protein networks. We propose a method to identify cliques on weighted graphs. To overcome the sparsity problem, we introduce the concept of transitive closure on weighted graphs which is based on enforcing a transitive affinity inequality on the connection weights, and an algorithm to compute them. Using protein network from TAP-MS experiment on yeast, we discover a large number of cliques that are densely connected protein modules, with clear biological meanings as shown on Gene Ontology analysis.