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Molecules 2018, 23(6), 1460; https://doi.org/10.3390/molecules23061460

Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein–Protein Interaction Network

1
College of Information and Electronic Engineering, Hunan City University, Yiyang 413000, China
2
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
3
College of Mathematics and Computer Science, Hunan Normal University, Changsha 410081, China
4
School of Information Engineering, East China Jiaotong University, Nanchang 330013, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Xiangxiang Zeng, Alfonso Rodríguez-Patón and Quan Zou
Received: 21 May 2018 / Revised: 11 June 2018 / Accepted: 12 June 2018 / Published: 15 June 2018
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Abstract

High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein–protein interaction (PPI) networks. In this study, based on penalized matrix decomposition (PMD), a novel method of penalized matrix decomposition for the identification of protein complexes (i.e., PMDpc) was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMDpc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR). View Full-Text
Keywords: protein–protein interaction (PPI); clustering; protein complex; penalized matrix decomposition protein–protein interaction (PPI); clustering; protein complex; penalized matrix decomposition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Cao, B.; Deng, S.; Qin, H.; Ding, P.; Chen, S.; Li, G. Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein–Protein Interaction Network. Molecules 2018, 23, 1460.

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