Better prediction of protein cellular localization sites with the k nearest neighbors classifier.
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 naïve 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.
Horton
P
P
Computer Science Division, University of California, Berkeley 94720, USA. paulh@cs.berkeley.edu
Nakai
K
K
D000465:Algorithms; D001426:Bacterial Proteins / Q000737:chemistry / Q000145:classification / Q000378:metabolism; D001499:Bayes Theorem; D001665:Binding Sites; D016208:Databases, Factual; D003663:Decision Trees; D004926:Escherichia coli / Q000378:metabolism; D005069:Evaluation Studies as Topic; D005656:Fungal Proteins / Q000737:chemistry / Q000145:classification / Q000378:metabolism; D011506:Proteins / Q000737:chemistry / Q000145:classification* / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D016415:Sequence Alignment; D012984:Software; D013347:Subcellular Fractions / Q000378:metabolism
D001426:Bacterial Proteins; D005656:Fungal Proteins; D011506:Proteins