Detecting overlapping protein complexes in weighted protein-protein interaction networks using pseudo-clique extension based on fuzzy relation

Abstract

Detecting overlapping protein complexes in protein-protein interaction (PPI) networks can provide insight into cellular functional organization and thus elucidate underlying cellular mechanisms. Recently, various algorithms for protein complex detection have been developed for PPI networks. However, the majority of algorithms primarily depend on network topological features and/or gene expression profile, failing to consider the inherent biological meanings between protein pairs. In this paper, we propose a method of pseudo-clique extension based on fuzzy relation (PCE-FR) that detects protein complexes from PPI networks weighted with the biological significance hidden in protein pairs. The proposed algorithm operates in three stages: it first forms the non-overlapping protein sub-structure based on fuzzy relation and then expands each sub-structure by adding neighbor proteins to maximize the cohesive score. Finally, highly overlapped candidate protein complexes are merged to form the final protein complex set. We apply PCE-FR to two yeast PPI networks and a human PPI network and validate our results by using CYC2008 and CHPC2012, respectively. Experimental results show that our method outperforms classical algorithms such as CFinder, ClusterONE, CMC, RRW, HC-PIN and ProRank+, and that it achieves ideal overall performance in terms of Precision, Accuracy, and Separation.