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

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題名:	Predicting subcellular locations of eukaryotic proteins using Bayesian and k-nearest neighbor classifiers
作者:	Han C.W. Hsiao;S.H. Chen;P.C. Chang;Jeffrey J.P. Tsai
貢獻者:	Department of Bioinformatics
關鍵詞:	subcellular location prediction;na?ve Bayesian classifier;k-nearest neighbor classifier;functional domain;feature reduction
日期:	2008-09
上傳時間:	2010-03-19 08:25:07 (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 na?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 (5): 1361-1375
顯示於類別:	[生物資訊與醫學工程學系 ] 期刊論文

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