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Prediction of Interaction Between Enzymes and Small Molecules in Metabolic Pathways Through Integrating Multiple Classifiers

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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 webserver for the prediction method presented in this paper is available at

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Keywords: 10-Fold Cross-Validation; A-B-K voting system; Amino acid; Bagging; Enzyme; K-nearest neighbor algorithm; KNN; Matthew's correlation coefficient; Metabolism; SVM algorithm; benchmark dataset; gluconeogenesis; glycolysis; interaction; jackknife test; majority voting; metabolic pathways; oxidative phosphorylation; pseudo amino acid composition; small molecule

Document Type: Research Article

Publication date: 2010-12-01

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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