Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition
文献类型：期刊论文
| 作者 | Huang, Yu-An1; You, Zhu-Hong2; Chen, Xing3; Yan, Gui-Ying4
|
| 刊名 | BMC SYSTEMS BIOLOGY
 |
| 出版日期 | 2016-12-23 |
| 卷号 | 10页码:10 |
| ISSN号 | 1752-0509 |
| 关键词 | Protein-protein interactions Protein sequence Continuous wavelet transform Sparse representation based classifier |
| DOI | 10.1186/s12918-016-0360-6 |
| 英文摘要 | Background: Protein-protein interactions (PPIs) are essential to most biological processes. Since bioscience has entered into the era of genome and proteome, there is a growing demand for the knowledge about PPI network. High-throughput biological technologies can be used to identify new PPIs, but they are expensive, time-consuming, and tedious. Therefore, computational methods for predicting PPIs have an important role. For the past years, an increasing number of computational methods such as protein structure-based approaches have been proposed for predicting PPIs. The major limitation in principle of these methods lies in the prior information of the protein to infer PPIs. Therefore, it is of much significance to develop computational methods which only use the information of protein amino acids sequence. Results: Here, we report a highly efficient approach for predicting PPIs. The main improvements come from the use of a novel protein sequence representation by combining continuous wavelet descriptor and Chou's pseudo amino acid composition (PseAAC), and from adopting weighted sparse representation based classifier (WSRC). This method, cross-validated on the PPIs datasets of Saccharomyces cerevisiae, Human and H. pylori, achieves an excellent results with accuracies as high as 92.50%, 95.54% and 84.28% respectively, significantly better than previously proposed methods. Extensive experiments are performed to compare the proposed method with state-of-the-art Support Vector Machine (SVM) classifier. Conclusions: The outstanding results yield by our model that the proposed feature extraction method combing two kinds of descriptors have strong expression ability and are expected to provide comprehensive and effective information for machine learning-based classification models. In addition, the prediction performance in the comparison experiments shows the well cooperation between the combined feature and WSRC. Thus, the proposed method is a very efficient method to predict PPIs and may be a useful supplementary tool for future proteomics studies. |
| 资助项目 | National Natural Science Foundation of China[61572506] ; National Natural Science Foundation of China[11301517] ; National Natural Science Foundation of China[11631014] ; National Natural Science Foundation of China[11371355] |
| WOS研究方向 | Mathematical & Computational Biology |
| 语种 | 英语 |
| 出版者 | BIOMED CENTRAL LTD |
| WOS记录号 | WOS:000392598000010 |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/359]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | You, Zhu-Hong; Chen, Xing |
| 作者单位 | 1.Hong Kong Polytech Univ, Dept Comp, Hong Hom, Hong Kong, 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.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100010, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Yu-An,You, Zhu-Hong,Chen, Xing,et al. Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition[J]. BMC SYSTEMS BIOLOGY,2016,10:10. |
| APA | Huang, Yu-An,You, Zhu-Hong,Chen, Xing,&Yan, Gui-Ying.(2016).Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition.BMC SYSTEMS BIOLOGY,10,10. |
| MLA | Huang, Yu-An,et al."Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition".BMC SYSTEMS BIOLOGY 10(2016):10. |
入库方式： OAI收割
来源：数学与系统科学研究院
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