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

By Han C. W. Hsiao, Shih-hao Chen, Judson Pei-chun Chang and Jeffrey J. P. Tsai

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 naïve 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%

Topics: subcellular location prediction, naïve Bayesian classifier, k-nearest neighbor classifier
Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.193.7737
Provided by: CiteSeerX
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