Title: CPredictor 4.0: effectively detecting protein complexes in weighted dynamic PPI networks

Authors: Yunjia Shi; Heng Yao; Jihong Guan; Shuigeng Zhou

Addresses: Department of Computer Science and Technology, Tongji University, Shanghai, China ' Department of Computer Science and Technology, Tongji University, Shanghai, China ' Department of Computer Science and Technology, Tongji University, Shanghai, China ' Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China

Abstract: The identification of protein complexes is significant to understand the mechanisms of cellular processes. Up to present, many methods on protein complex detection have been developed in static PPI networks. However, static PPI networks cannot accurately describe the behaviours of proteins in the different stages of life cycle of a cell. In this paper, we combine different data sets including gene expression data, GO terms and high-throughput PPI data to reconstruct weighted dynamic PPI networks, on which a new method called CPredictor4.0 are proposed. Specifically, we first calculate protein active probability and protein functional similarity to construct weighted dynamic PPI networks, then define a high-order topological overlap measure of similarity to extract protein complexes based on the core-attachment model. In our experiments, four PPI datasets are used to detect protein complexes. Experimental results indicate that CPredictor4.0 is superior to the existing methods in overall.

Keywords: protein to protein interactions; protein complexes; protein active probability; functional similarity.

DOI: 10.1504/IJDMB.2018.094888

International Journal of Data Mining and Bioinformatics, 2018 Vol.20 No.4, pp.303 - 319

Received: 12 Jul 2018
Accepted: 22 Jul 2018
Published online: 25 Sep 2018 *

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