Prediction of protein subcellular locations by combining k-local Hyperplane Distance Nearest Neighbor

Abstract

A huge number of protein sequences have been generated and collected. However, the functions of most of them are still unknown. Protein subcellular localization is important to elucidate protein function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been done to accomplish such a task, there is the need for further research to improve the accuracy of prediction. In this paper, with K-local Hyperplane Distance Nearest Neighbor algorithm (HKNN) as base classifier, an ensemble classifier is proposed to predict the subcellular locations of proteins in eukaryotic cells. Each basic HKNN classifiers are constructed from a separated feature set, and finally combined with majority voting scheme. Results obtained through 5-fold cross-validation test on the same protein dataset showed an improvement in pre-diction accuracy over existing algorithms. © Springer-Verlag Berlin Heidelberg 2007.