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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4764

    Title: Predicting Subcellular Location of Eukaryotic Proteins using Baysian and K-Nearest Neighbor Classifier
    Authors: Jeffrey J. P. Tsai;H. W. Hsiao;S. H. Chen;P.C. Chang
    Keywords: subcellular location prediction;na�ve Bayesian classifier;k-nearest neighbor classifier;functional domain;feature reduction
    Date: 2008-09
    Issue Date: 2009-12-02 09:04:05 (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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