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Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding
Huang, Yu-An1; You, Zhu-Hong2; Chen, Xing3; Chan, Keith4; Luo, Xin4
2016-04-26
Source PublicationBMC BIOINFORMATICS
ISSN1471-2105
Volume17Pages:11
AbstractBackground: Proteins are the important molecules which participate in virtually every aspect of cellular function within an organism in pairs. Although high-throughput technologies have generated considerable protein-protein interactions (PPIs) data for various species, the processes of experimental methods are both time-consuming and expensive. In addition, they are usually associated with high rates of both false positive and false negative results. Accordingly, a number of computational approaches have been developed to effectively and accurately predict protein interactions. However, most of these methods typically perform worse when other biological data sources (e.g., protein structure information, protein domains, or gene neighborhoods information) are not available. Therefore, it is very urgent to develop effective computational methods for prediction of PPIs solely using protein sequence information. Results: In this study, we present a novel computational model combining weighted sparse representation based classifier (WSRC) and global encoding (GE) of amino acid sequence. Two kinds of protein descriptors, composition and transition, are extracted for representing each protein sequence. On the basis of such a feature representation, novel weighted sparse representation based classifier is introduced to predict protein interaction class. When the proposed method was evaluated with the PPIs data of S. cerevisiae, Human and H. pylori, it achieved high prediction accuracies of 96.82, 97.66 and 92.83 % respectively. Extensive experiments were performed for cross-species PPIs prediction and the prediction accuracies were also very promising. Conclusions: To further evaluate the performance of the proposed method, we then compared its performance with the method based on support vector machine (SVM). The results show that the proposed method achieved a significant improvement. Thus, the proposed method is a very efficient method to predict PPIs and may be a useful supplementary tool for future proteomics studies.
DOI10.1186/s12859-016-1035-4
Language英语
Funding ProjectNational Science Foundation of China[61373086] ; National Science Foundation of China[11301517] ; National Science Foundation of China[61572506] ; Guangdong Natural Science Foundation[2014A030313555] ; Shenzhen Scientific Research and Development Funding Program[JCYJ20140418095735569]
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS IDWOS:000374836500002
PublisherBIOMED CENTRAL LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/206
Collection中国科学院数学与系统科学研究院
Corresponding AuthorYou, Zhu-Hong; Chen, Xing
Affiliation1.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Guangdong, Peoples R China
2.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
4.Hong Kong Polytech Univ, Dept Comp, Kowloon 999077, Hong Kong, Peoples R China
Recommended Citation
GB/T 7714
Huang, Yu-An,You, Zhu-Hong,Chen, Xing,et al. Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding[J]. BMC BIOINFORMATICS,2016,17:11.
APA Huang, Yu-An,You, Zhu-Hong,Chen, Xing,Chan, Keith,&Luo, Xin.(2016).Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding.BMC BIOINFORMATICS,17,11.
MLA Huang, Yu-An,et al."Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding".BMC BIOINFORMATICS 17(2016):11.
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