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Better prediction of protein cellular localization sites with the k nearest neighbors classifier

By Paul Horton and Kenta Nakai

Abstract

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 structured probabilistic model specifically designed for the localization problem, the k nearest neighbors classitier, the binary decision tree classifier, and the naive Bayes classifier. The result of tests using stratified cross validation shows the k nearest neighbors classifier 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 reported accuracies for these datasets were 55 % and 81% respectively

Topics: Protein Localization, k Nearest Neighbor Classifier, Classification, Yeast, E.colz
Year: 1997
OAI identifier: oai:CiteSeerX.psu:10.1.1.322.457
Provided by: CiteSeerX
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