Prediction of protein subcellular locations by ensemble of improved K-nearest neighbor
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Fundamental Research Funds for the Central Universities (No. KYZ201668), Natural Science Foundation of Jiangsu Province (No. BK2012363), National Science and Technology Support Program Project (No. 2015BAK36B05).

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    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, two 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.

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薛卫,王雄飞,赵南,杨荣丽,洪晓宇. 集成改进KNN算法预测蛋白质亚细胞定位[J]. Chinese Journal of Biotechnology, 2017, 33(4): 683-691

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  • Received:October 18,2016
  • Online: March 31,2017
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