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Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model
An, Ji-Yong1; Meng, Fan-Rong1; You, Zhu-Hong1; Chen, Xing2; Yan, Gui-Ying3; Hu, Ji-Pu1
2016-10-01
Source PublicationPROTEIN SCIENCE
ISSN0961-8368
Volume25Issue:10Pages:1825-1833
AbstractPredicting 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 .
Keywordevolutionary information position specific scoring matrix proteomics
DOI10.1002/pro.2991
Language英语
WOS Research AreaBiochemistry & Molecular Biology
WOS SubjectBiochemistry & Molecular Biology
WOS IDWOS:000383706700006
PublisherWILEY-BLACKWELL
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/23841
Collection应用数学研究所
Affiliation1.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
Recommended Citation
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.
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