Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier
文献类型：期刊论文
| 作者 | Li, ZW (Li, Zheng-Wei); You, ZH (You, Zhu-Hong) ; Chen, X (Chen, Xing); Li, LP (Li, Li-Ping); Huang, DS (Huang, De-Shuang); Yan, GY (Yan, Gui-Ying); Nie, R (Nie, Ru); Huang, YA (Huang, Yu-An)
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| 刊名 | ONCOTARGET
 |
| 出版日期 | 2017 |
| 卷号 | 8期号:14页码:23638-23649 |
| 关键词 | disease position-specific scoring matrix weber local descriptor cancer protein-protein interactions |
| 通讯作者 | You, ZH |
| 英文摘要 | Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research. |
| 收录类别 | SCI |
| WOS记录号 | WOS:000398211100113 |
| 源URL | [http://ir.xjipc.cas.cn/handle/365002/4768]  |
| 专题 | 新疆理化技术研究所_多语种信息技术研究室 |
| 作者单位 | 1.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 & Control Engn, Xuzhou 221116, Peoples R China 4.Tongji Univ, Sch Elect & Informat Engn, Shanghai 201804, Peoples R China 5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 6.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Guangdong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, ZW ,You, ZH ,Chen, X ,et al. Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier[J]. ONCOTARGET,2017,8(14):23638-23649. |
| APA | Li, ZW .,You, ZH .,Chen, X .,Li, LP .,Huang, DS .,...&Huang, YA .(2017).Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.ONCOTARGET,8(14),23638-23649. |
| MLA | Li, ZW ,et al."Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier".ONCOTARGET 8.14(2017):23638-23649. |
入库方式： OAI收割
来源：新疆理化技术研究所
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