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

Paul Horton and Kenta Nakai

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 classifier, 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% for 10 yeast classes and 86% for 8 E.coli classes. The best previously reported accuracies for these datasets were 55% and 81% respectively.

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