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Better Prediction of Protein Cellular Localization Sites with the it k Nearest Neighbors Classifier

Better Prediction of Protein Cellular Localization Sites with the it k Nearest Neighbors Classifier,Paul Horton,Kenta Nakai

Better Prediction of Protein Cellular Localization Sites with the it k Nearest Neighbors Classifier   (Citations: 210)
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We have compared four classifiers on the problem of predicting the cellular localization sites of proteins in yeast and E.coli. A set of sequence derived features, such as regions of high hydrophobicity, were used for each classifier. The methods compared were a struc- tured probabilistic model specifically designed for the localization problem, the k nearest neighbors classi- tier, the binary decision tree classifier, and the naive Bayes classifier. The result of tests using stratified cross validation shows the k nearest neighbors classi- fier to perform better than the other methods. In the case of yeast this difference was statistically significant using a cross-validated paired t test. The result is an accuracy of approximately 60°/o for 10 yeast classes and 86% for 8 E.coli classes. The best previously re- ported accuracies for these datasets were 55% and 81% respectively.
Conference: Intelligent Systems in Molecular Biology - ISMB , pp. 147-152, 1997
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