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From Function to Interaction: A New Paradigm for Accurately Predicting Protein Complexes Based on Protein-to-Protein Interaction Networks
PrePrint
ISSN: 1545-5963
Identification of protein complexes is critical to understand complex formation and protein functions. Recent advances in highthroughput experiments have provided large datasets of protein-protein interactions (PPIs). Many approaches, based on the assumption that complexes are dense subgraphs of PPI networks (PINs in short), have been proposed to predict complexes using graph clustering methods. In this paper, we introduce a novel from-function-to-interaction paradigm for protein complex detection. As proteins perform biological functions by forming complexes, we first cluster proteins using Biology Process (BP) annotations from Gene Ontology (GO). Then, we map the resulting proteins clusters onto a PPI network (PIN in short), extract connected subgraphs consisting of clustered proteins as nodes from the PPI network and expand each connected subgraph with protein nodes that have rich links to the proteins in the subgraph. Such expanded subgraphs are taken as predicted complexes. We apply the proposed method (called CPredictor) to two PPI datasets of S. cerevisiae for predicting protein complexes. Experimental results show that CPredictor outperforms the existing methods. The outstanding precision of CPredictor proves that the from-function-to-interaction paradigm provides a new and effective way to computational detection of protein complexes.
Citation:
Jihong Guan, "From Function to Interaction: A New Paradigm for Accurately Predicting Protein Complexes Based on Protein-to-Protein Interaction Networks," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 28 Feb. 2014. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TCBB.2014.2306825>
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