Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model
文献类型：期刊论文
| 作者 | An, Ji-Yong1; Meng, Fan-Rong1; You, Zhu-Hong1; Chen, Xing2; Yan, Gui-Ying3 ; Hu, Ji-Pu1
|
| 刊名 | PROTEIN SCIENCE
 |
| 出版日期 | 2016-10-01 |
| 卷号 | 25期号:10页码:1825-1833 |
| 关键词 | evolutionary information position specific scoring matrix proteomics |
| ISSN号 | 0961-8368 |
| DOI | 10.1002/pro.2991 |
| 英文摘要 | 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 . |
| WOS研究方向 | Biochemistry & Molecular Biology |
| 语种 | 英语 |
| WOS记录号 | WOS:000383706700006 |
| 出版者 | WILEY-BLACKWELL |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/23840]  |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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