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Prediction of interaction between enzymes and small molecules in metabolic pathways through integrating multiple classifiers

 
: Lu, J.; Zhu, Y.B.; Li, Y.J.; Lu, W.C.; Hu, L.L.; Niu, B.; Qing, P.F.; Gu, L.

Protein and peptide letters 17 (2010), Nr.12, S.1536-1541
ISSN: 0929-8665
Englisch
Zeitschriftenaufsatz
Fraunhofer SCAI ()

Abstract
Information about interactions between enzymes and small molecules is important for understanding various metabolic bioprocesses. In this article we applied a majority voting system to predict the interactions between enzymes and small molecules in the metabolic pathways, by combining several classifiers including AdaBoost, Bagging and KNN together. The advantage of such a strategy is based on the principle that a predictor based majority voting systems usually provide more reliable results than any single classifier. The prediction accuracies thus obtained on a training dataset and an independent testing dataset were 82.8% and 84.8%, respectively. The prediction accuracy for the networking couples in the independent testing dataset was 75.5%, which is about 4% higher than that reported in a previous study [1]. The web-server for the prediction method presented in this paper is available at http://chemdata.shu.edu.cn/small-enz.

: http://publica.fraunhofer.de/dokumente/N-156667.html