Predicting subcellular locations of eukaryotic proteins using bayesian and k-nearest neighbor classifiers

ASIA unversity > 資訊學院 > 生物資訊與醫學工程學系 > 期刊論文 > Item 310904400/8653

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题名:	Predicting subcellular locations of eukaryotic proteins using bayesian and k-nearest neighbor classifiers
作者:	Hsiao, Han C. W.;Chen, Shih-Hao;Chang, Judson Pei-Chun;Tsai, Jeffrey J. P.
贡献者:	Department of Bioinformatics
关键词:	Amines;Amino acids;Bayesian networks;Classifiers;Forecasting;Location;Organic acids;Proteins;Amino acid compositions;Bayesian;Bayesian classifiers;Dipeptide compositions;Eukaryotic proteins;Feature reduction;Functional domain;Functional domains;Hybrid approaches;k-nearest neighbor classifier;K-nearest neighbor classifiers;Protein subcellular locations;Reliable methods;Subcellular location prediction;Subcellular locations
日期:	2008
上传时间:	2010-04-07 13:21:20 (UTC+0)
出版者:	Asia University
摘要:	Biologically, the function of a protein is highly related to its subcellular location. It is of necessity to develop a reliable method for protein subcellular location prediction, especially when a large amount of proteins are to be analyzed. Various methods have been proposed to perform the task. The results, however, are not satisfactory in terms of effectiveness and efficiency. A hybrid approach combining nai&die;ve Bayesian classifier and k-nearest neighbor classifier is proposed to classify eukaryotic proteins represented as a combination of amino acid composition, dipeptide composition, and functional domain composition. Experimental results show that the total accuracy of a set of 17,655 proteins can reach up to 91.5%.
關聯:	Journal of Information Science and Engineering 24:1361-1375
显示于类别:	[生物資訊與醫學工程學系 ] 期刊論文

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