Prediction of protein subcellular locations by ensemble of improved K-nearest neighbor / 生物工程学报
Chinese Journal of Biotechnology
; (12): 683-691, 2017.
Article
in Zh
| WPRIM
| ID: wpr-310623
Responsible library:
WPRO
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.
Full text:
1
Index:
WPRIM
Type of study:
Prognostic_studies
Language:
Zh
Journal:
Chinese Journal of Biotechnology
Year:
2017
Type:
Article