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PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation
Li, LP (Li, Li-Ping)[ 1 ]; Wang, YB (Wang, Yan-Bin)[ 2 ]; You, ZH (You, Zhu-Hong)[ 1 ]; Li, Y (Li, Yang)[ 1 ]; An, JY (An, Ji-Yong)[ 3 ]
2018
Source PublicationINTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
ISSN1422-0067
Volume19Issue:4Pages:1-13
Abstract

Protein-protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular biology. As researchers have become aware of the importance of computational methods in predicting PPIs, many techniques have been developed for performing this task computationally. However, there are few technologies that really meet the needs of their users. In this paper, we develop a novel and efficient sequence-based method for predicting PPIs. The evolutionary features are extracted from the position-specific scoring matrix (PSSM) of protein. The features are then fed into a robust relevance vector machine (RVM) classifier to distinguish between the interacting and non-interacting protein pairs. In order to verify the performance of our method, five-fold cross-validation tests are performed on the Saccharomyces cerevisiae dataset. A high accuracy of 94.56%, with 94.79% sensitivity at 94.36% precision, was obtained. The experimental results illustrated that the proposed approach can extract the most significant features from each protein sequence and can be a bright and meaningful tool for the research of proteomics.

KeywordProtein-protein Interactions (Ppi) Low Rank Protein Sequence Relevance Vector Machine (Rvm) Evolutionary Information
DOI10.3390/ijms19041029
Indexed BySCI
WOS IDWOS:000434978700108
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5614
Collection多语种信息技术研究室
Corresponding AuthorYou, ZH (You, Zhu-Hong)[ 1 ]
Affiliation1.Xijing Univ, Dept Informat Engn, Xian 710123, Shaanxi, Peoples R China
2.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
3.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 21116, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Li, LP ,Wang, YB ,You, ZH ,et al. PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation[J]. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,2018,19(4):1-13.
APA Li, LP ,Wang, YB ,You, ZH ,Li, Y ,&An, JY .(2018).PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,19(4),1-13.
MLA Li, LP ,et al."PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation".INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 19.4(2018):1-13.
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