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An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences
Wang, L (Wang, Lei); You, ZH (You, Zhu-Hong); Chen, X (Chen, Xing); Li, JQ (Li, Jian-Qiang); Yan, X (Yan, Xin); Zhang, W (Zhang, Wei); Huang, YA (Huang, Yu-An)
2017
Source PublicationONCOTARGET
ISSN1949-2553
Volume8Issue:3Pages:5149-5159
Abstract

Protein-Protein Interactions (PPI) is not only the critical component of various biological processes in cells, but also the key to understand the mechanisms leading to healthy and diseased states in organisms. However, it is time-consuming and cost-intensive to identify the interactions among proteins using biological experiments. Hence, how to develop a more efficient computational method rapidly became an attractive topic in the post-genomic era. In this paper, we propose a novel method for inference of protein-protein interactions from protein amino acids sequences only. Specifically, protein amino acids sequence is firstly transformed into Position-Specific Scoring Matrix (PSSM) generated by multiple sequences alignments; then the Pseudo PSSM is used to extract feature descriptors. Finally, ensemble Rotation Forest (RF) learning system is trained to predict and recognize PPIs based solely on protein sequence feature. When performed the proposed method on the three benchmark data sets (Yeast, H. pylori, and independent dataset) for predicting PPIs, our method can achieve good average accuracies of 98.38%, 89.75%, and 96.25%, respectively. In order to further evaluate the prediction performance, we also compare the proposed method with other methods using same benchmark data sets. The experiment results demonstrate that the proposed method consistently outperforms other state-of-the-art method. Therefore, our method is effective and robust and can be taken as a useful tool in exploring and discovering new relationships between proteins. A web server is made publicly available at the URL http://202.119.201.126: 8888/PsePSSM/for academic use.

KeywordDisease Position-specific Scoring Matrix Multiple Sequences Alignments Cancer
DOI10.18632/oncotarget.14103
Indexed BySCI
WOS IDWOS:000393228400112
Citation statistics
Cited Times:13[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/4740
Collection多语种信息技术研究室
Corresponding AuthorYou, ZH (You, Zhu-Hong)
Affiliation1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China
2.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
3.China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221116, Peoples R China
4.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Guangdong, Peoples R China
5.Zaozhuang Univ, Coll Informat Sci & Engn, Zaozhuang 277100, Shandong, Peoples R China
6.Zaozhuang Univ, Sch Foreign Languages, Zaozhuang 277100, Shandong, Peoples R China
Recommended Citation
GB/T 7714
Wang, L ,You, ZH ,Chen, X ,et al. An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences[J]. ONCOTARGET,2017,8(3):5149-5159.
APA Wang, L .,You, ZH .,Chen, X .,Li, JQ .,Yan, X .,...&Huang, YA .(2017).An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences.ONCOTARGET,8(3),5149-5159.
MLA Wang, L ,et al."An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences".ONCOTARGET 8.3(2017):5149-5159.
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