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Sequence-based Prediction of Protein-Protein Interactions Using Gray Wolf Optimizer-Based Relevance Vector Machine
An, JY (An, Ji-Yong)[ 1,2 ]; You, ZH (You, Zhu-Hong)[ 3 ]; Zhou, Y (Zhou, Yong)[ 1,2 ]; Wang, DF (Wang, Da-Fu)[ 1,2 ]
2019
Source PublicationEVOLUTIONARY BIOINFORMATICS
ISSN1176-9343
Volume15Issue:5Pages:1-10
Abstract

Protein-protein interactions (PPIs) are essential to a number of biological processes. The PPIs generated by biological experiment are both time-consuming and expensive. Therefore, many computational methods have been proposed to identify PPIs. However, most of these methods are limited as they are difficult to compute and rely on a large number of homologous proteins. Accordingly, it is urgent to develop effective computational methods to detect PPIs using only protein sequence information. The kernel parameter of relevance vector machine (RVM) is set by experience, which may not obtain the optimal solution, affecting the prediction performance of RVM. In this work, we presented a novel computational approach called GWORVM-BIG, which used Bi-gram (BIG) to represent protein sequences on a position-specific scoring matrix (PSSM) and GWORVM classifier to perform classification for predicting PPIs. More specifically, the proposed GWORVM model can obtain the optimum solution of kernel parameters using gray wolf optimizer approach, which has the advantages of less control parameters, strong global optimization ability, and ease of implementation compared with other optimization algorithms. The experimental results on yeast and human data sets demonstrated the good accuracy and efficiency of the proposed GWORVM-BIG method. The results showed that the proposed GWORVM classifier can significantly improve the prediction performance compared with the RVM model using other optimizer algorithms including grid search (GS), genetic algorithm (GA), and particle swarm optimization (PSO). In addition, the proposed method is also compared with other existing algorithms, and the experimental results further indicated that the proposed GWORVM-BIG model yields excellent prediction performance. For facilitating extensive studies for future proteomics research, the GWORVMBIG server is freely available for academic use at

KeywordRVM gray wolf optimizer BIG PSSM
DOI10.1177/1176934319844522
Indexed BySCI
WOS IDWOS:000467164100001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5768
Collection多语种信息技术研究室
Corresponding AuthorYou, ZH (You, Zhu-Hong)[ 3 ]
Affiliation1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou, Jiangsu, Peoples R China
2.Minstry Educ Peoples Republ China, Mine Digitizat Engn Res Ctr, Beijing, Peoples R China
3.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
Recommended Citation
GB/T 7714
An, JY ,You, ZH ,Zhou, Y ,et al. Sequence-based Prediction of Protein-Protein Interactions Using Gray Wolf Optimizer-Based Relevance Vector Machine[J]. EVOLUTIONARY BIOINFORMATICS,2019,15(5):1-10.
APA An, JY ,You, ZH ,Zhou, Y ,&Wang, DF .(2019).Sequence-based Prediction of Protein-Protein Interactions Using Gray Wolf Optimizer-Based Relevance Vector Machine.EVOLUTIONARY BIOINFORMATICS,15(5),1-10.
MLA An, JY ,et al."Sequence-based Prediction of Protein-Protein Interactions Using Gray Wolf Optimizer-Based Relevance Vector Machine".EVOLUTIONARY BIOINFORMATICS 15.5(2019):1-10.
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