A Novel Core-Attachment Based Greedy Search Method for Mining Functional Modules in Protein Interaction Networks

Abstract

As advances in the technologies of predicting protein interactions, huge data sets portrayed as networks have been available. Therefore, computational methods are required to analyze the interaction data in order to effectively detect functional modules from such networks. However, these analysis mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. A greedy search method (GSM) based on core-attachment structure is proposed in this paper, which detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The proposed algorithm is applied to the protein interaction network of S.cerevisiae and many significant functional modules are detected, most of which match the known complexes. The comparison results show that our algorithm outperforms several other competing algorithms.