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Journal of Information Science and Engineering, Vol. 24 No. 5, pp. 1361-1375 (September 2008)

Predicting Subcellular Locations of Eukaryotic Proteins Using Bayesian and k-Nearest Neighbor Classifiers

Han-Wen Hsiao, Shih-Hao Chen, Judson Pei-Chun Chang and Jeffrey J. P. Tsai+
Department of Bioinformatics
Asia University
Wufeng, 413 Taiwan
+Department of Computer Science
University of Illinois at Chicago
Chicage, IL 60607, U.S.A.

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%.

Keywords: subcellular location prediction, naive Bayesian classifier, k-nearest neighbor classifier, functional domain, feature reduction

Full Text () Retrieve PDF document (200809_05.pdf)

Received November 7, 2006; revised March 14, 2007; accepted June 27, 2007.
Communicated by Tsan-sheng Hsu.