Prediction of protein subcellular locations by ensemble of improved K-nearest neighbor.

Author: Wei XUE ¹ ; Xiongfei WANG ¹ ; Nan ZHAO ¹ ; Rongli YANG ¹ ; Xiaoyu HONG ¹
Author Information

1. School of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China.
Publication Type:Journal Article
Keywords: Adaboost; K-nearest neighbor; basic local alignment search tool (Blast); protein sequence characteristics; subcellular locations
From: Chinese Journal of Biotechnology 2017;33(4):683-691
CountryChina
Language:Chinese
Abstract: Adaboost algorithm with improved K-nearest neighbor classifiers is proposed to predict protein subcellular locations. Improved K-nearest neighbor classifier uses three sequence feature vectors including amino acid composition, dipeptide and pseudo amino acid composition of protein sequence. K-nearest neighbor uses Blast in classification stage. The overall success rates by the jackknife test on two data sets of CH317 and Gram1253 are 92.4% and 93.1%. Adaboost algorithm with the novel K-nearest neighbor improved by Blast is an effective method for predicting subcellular locations of proteins.