KMS Of Academy of mathematics and systems sciences, CAS
| 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 ; Wang, Da-Fu1
| |
| 2016 | |
| Source Publication | MOLECULAR BIOSYSTEMS
 |
| ISSN | 1742-206X |
| Volume | 12Issue:12Pages:3702-3710 |
| Abstract | 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. |
| DOI | 10.1039/c6mb00599c |
| Language | 英语 |
| Funding Project | 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 Research Area | Biochemistry & Molecular Biology |
| WOS Subject | Biochemistry & Molecular Biology |
| WOS ID | WOS:000388946800019 |
| Publisher | ROYAL SOC CHEMISTRY |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/24181 |
| Collection | 应用数学研究所 |
| Corresponding Author | You, Zhu-Hong; Chen, Xing |
| Affiliation | 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 |
| Recommended Citation 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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