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Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier
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); You, ZH
2017
发表期刊ONCOTARGET
卷号8期号:14页码:23638-23649
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.
关键词Disease Position-specific Scoring Matrix Weber Local Descriptor Cancer Protein-protein Interactions
DOI10.18632/oncotarget.15564
收录类别SCI
WOS记录号WOS:000398211100113
引用统计
文献类型期刊论文
条目标识符http://ir.xjipc.cas.cn/handle/365002/4768
专题多语种信息技术研究室
通讯作者You, ZH
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
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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 .,...&You, ZH.(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.
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