Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model

doi:10.1002/pro.2991

CSpace > 应用数学研究所

	Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model
	An, Ji-Yong 1; Meng, Fan-Rong 1; You, Zhu-Hong 1; Chen, Xing 2; Yan, Gui-Ying3 ; Hu, Ji-Pu 1
	2016-10-01
发表期刊	PROTEIN SCIENCE
ISSN	0961-8368
卷号	25 期号:10 页码:1825-1833
摘要	Predicting protein-protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high-throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM-BiGP that combines the relevance vector machine (RVM) model and Bi-gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi-gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five-fold cross-validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-BiGP method is significantly better than the SVM-based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future proteomics research. For facilitating extensive studies for future proteomics research, we developed a freely available web server called RVM-BiGP-PPIs in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at .
关键词	evolutionary information position specific scoring matrix proteomics
DOI	10.1002/pro.2991
语种	英语
WOS研究方向	Biochemistry & Molecular Biology
WOS类目	Biochemistry & Molecular Biology
WOS记录号	WOS:000383706700006
出版者	WILEY-BLACKWELL
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/23841
专题	应用数学研究所
通讯作者	You, Zhu-Hong
作者单位	1.China Univ Min & Technol, Sch Comp Sci Technol, Xuzhou 21116, Jiangsu, Peoples R China 2.China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 21116, Jiangsu, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	An, Ji-Yong,Meng, Fan-Rong,You, Zhu-Hong,et al. Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model[J]. PROTEIN SCIENCE,2016,25(10):1825-1833.
APA	An, Ji-Yong,Meng, Fan-Rong,You, Zhu-Hong,Chen, Xing,Yan, Gui-Ying,&Hu, Ji-Pu.(2016).Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model.PROTEIN SCIENCE,25(10),1825-1833.
MLA	An, Ji-Yong,et al."Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model".PROTEIN SCIENCE 25.10(2016):1825-1833.