Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information | |
An, Ji-Yong1; You, Zhu-Hong2; Chen, Xing3; Huang, De-Shuang4; Yan, Guiying5![]() | |
刊名 | MOLECULAR BIOSYSTEMS
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2016 | |
卷号 | 12期号:12页码:3702-3710 |
ISSN号 | 1742-206X |
DOI | 10.1039/c6mb00599c |
英文摘要 | Self-interacting proteins (SIPs) play an essential role in cellular functions and the evolution of protein interaction networks (PINs). Due to the limitations of experimental self-interaction proteins detection technology, it is a very important task to develop a robust and accurate computational approach for SIPs prediction. In this study, we propose a novel computational method for predicting SIPs from protein amino acids sequence. Firstly, a novel feature representation scheme based on Local Binary Pattern (LBP) is developed, in which the evolutionary information, in the form of multiple sequence alignments, is taken into account. Then, by employing the Relevance Vector Machine (RVM) classifier, the performance of our proposed method is evaluated on yeast and human datasets using a five-fold cross-validation test. The experimental results show that the proposed method can achieve high accuracies of 94.82% and 97.28% on yeast and human datasets, respectively. For further assessing the performance of our method, we compared it with the state-of-the-art Support Vector Machine (SVM) classifier, and other existing methods, on the same datasets. Comparison results demonstrate that the proposed method is very promising and could provide a cost-effective alternative for predicting SIPs. In addition, to facilitate extensive studies for future proteomics research, a web server is freely available for academic use at http://219.219.62.123:8888/HASIPP. |
资助项目 | National Science Foundation of China[61373086] ; National Science Foundation of China[11301517] ; National Science Foundation of China[61572506] ; National Science Foundation of China[11631014] ; Guangdong Natural Science Foundation[2014A030313555] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences |
WOS研究方向 | Biochemistry & Molecular Biology |
语种 | 英语 |
出版者 | ROYAL SOC CHEMISTRY |
WOS记录号 | WOS:000388946800019 |
内容类型 | 期刊论文 |
源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/24180] ![]() |
专题 | 应用数学研究所 |
通讯作者 | You, Zhu-Hong; Chen, Xing |
作者单位 | 1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 21116, 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, Jiangsu, 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 |
推荐引用方式 GB/T 7714 | An, Ji-Yong,You, Zhu-Hong,Chen, Xing,et al. Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information[J]. MOLECULAR BIOSYSTEMS,2016,12(12):3702-3710. |
APA | An, Ji-Yong,You, Zhu-Hong,Chen, Xing,Huang, De-Shuang,Yan, Guiying,&Wang, Da-Fu.(2016).Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information.MOLECULAR BIOSYSTEMS,12(12),3702-3710. |
MLA | An, Ji-Yong,et al."Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information".MOLECULAR BIOSYSTEMS 12.12(2016):3702-3710. |
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