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	Protein sub-cellular localisation prediction by analysis of short-range residue correlations
	Jian Guo ; Yuanlie Lin ; Zhirong Sun
	2010-05-06 ; 2010-05-06
关键词	Practical/ biochemistry biology computing cellular biophysics genetics molecular biophysics proteins support vector machines/ protein sub-cellular localisation prediction short-range residue correlation analysis genome analysis support vector machine prokaryotic protein sequence eukaryotic protein sequence N-terminal error residue-couple model/ A8715B Biomolecular structure, configuration, conformation, and active sites A8715D Physical chemistry of biomolecular solutions condensed states A8725F Physics of subcellular structures A3620E Macromolecular constitution (chains and sequences) C7330 Biology and medical computing
中文摘要	Sub-cellular localisation performs an important role in genome analysis. This paper describes a new residue-couple model using a support vector machine to predict the sub-cellular localisation of proteins. This new approach provides better predictions than the existing methods. The total prediction accuracies on Reinhardt and Hubbard's dataset reach 92.0% for prokaryotic protein sequences and 86.9% for eukaryotic protein sequences with fivefold cross validation. For a new dataset with 8304 proteins located in eight sub-cellular locations, the total accuracy achieves 88.9%. Meanwhile, the model shows robust against N-terminal errors in the sequences.
语种	英语 ; 英语
出版者	Inderscience Enterprises ; Switzerland
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/13829]
专题	清华大学
推荐引用方式 GB/T 7714	Jian Guo,Yuanlie Lin,Zhirong Sun. Protein sub-cellular localisation prediction by analysis of short-range residue correlations[J],2010, 2010.
APA	Jian Guo,Yuanlie Lin,&Zhirong Sun.(2010).Protein sub-cellular localisation prediction by analysis of short-range residue correlations..
MLA	Jian Guo,et al."Protein sub-cellular localisation prediction by analysis of short-range residue correlations".(2010).

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