An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences
文献类型：期刊论文
| 作者 | Wang, L (Wang, Lei); You, ZH (You, Zhu-Hong) ; Chen, X (Chen, Xing); Li, JQ (Li, Jian-Qiang); Yan, X (Yan, Xin); Zhang, W (Zhang, Wei); Huang, YA (Huang, Yu-An)
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| 刊名 | ONCOTARGET
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| 出版日期 | 2017 |
| 卷号 | 8期号:3页码:5149-5159 |
| 关键词 | disease position-specific scoring matrix multiple sequences alignments cancer |
| 通讯作者 | You, ZH |
| 英文摘要 | Protein-Protein Interactions (PPI) is not only the critical component of various biological processes in cells, but also the key to understand the mechanisms leading to healthy and diseased states in organisms. However, it is time-consuming and cost-intensive to identify the interactions among proteins using biological experiments. Hence, how to develop a more efficient computational method rapidly became an attractive topic in the post-genomic era. In this paper, we propose a novel method for inference of protein-protein interactions from protein amino acids sequences only. Specifically, protein amino acids sequence is firstly transformed into Position-Specific Scoring Matrix (PSSM) generated by multiple sequences alignments; then the Pseudo PSSM is used to extract feature descriptors. Finally, ensemble Rotation Forest (RF) learning system is trained to predict and recognize PPIs based solely on protein sequence feature. When performed the proposed method on the three benchmark data sets (Yeast, H. pylori, and independent dataset) for predicting PPIs, our method can achieve good average accuracies of 98.38%, 89.75%, and 96.25%, respectively. In order to further evaluate the prediction performance, we also compare the proposed method with other methods using same benchmark data sets. The experiment results demonstrate that the proposed method consistently outperforms other state-of-the-art method. Therefore, our method is effective and robust and can be taken as a useful tool in exploring and discovering new relationships between proteins. A web server is made publicly available at the URL http://202.119.201.126: 8888/PsePSSM/for academic use. |
| 收录类别 | SCI |
| WOS记录号 | WOS:000393228400112 |
| 源URL | [http://ir.xjipc.cas.cn/handle/365002/4740]  |
| 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 & Elect Engn, Xuzhou 221116, Peoples R China 4.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Guangdong, Peoples R China 5.Zaozhuang Univ, Coll Informat Sci & Engn, Zaozhuang 277100, Shandong, Peoples R China 6.Zaozhuang Univ, Sch Foreign Languages, Zaozhuang 277100, Shandong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, L ,You, ZH ,Chen, X ,et al. An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences[J]. ONCOTARGET,2017,8(3):5149-5159. |
| APA | Wang, L .,You, ZH .,Chen, X .,Li, JQ .,Yan, X .,...&Huang, YA .(2017).An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences.ONCOTARGET,8(3),5149-5159. |
| MLA | Wang, L ,et al."An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences".ONCOTARGET 8.3(2017):5149-5159. |
入库方式： OAI收割
来源：新疆理化技术研究所
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