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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8167


    Title: Predicting subcellular locations of eukaryotic proteins using Bayesian and k-nearest neighbor classifiers
    Authors: Han C.W. Hsiao;S.H. Chen;P.C. Chang;Jeffrey J.P. Tsai
    Contributors: Department of Bioinformatics
    Keywords: subcellular location prediction;na?ve Bayesian classifier;k-nearest neighbor classifier;functional domain;feature reduction
    Date: 2008-09
    Issue Date: 2010-03-19 16:25:07 (UTC+8)
    Publisher: Asia University
    Abstract: 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%.
    Relation: Journal of Information Science and Engineering 24 (5): 1361-1375
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

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