A Binary Classifier for the Prediction of EC Numbers of Enzymes

Author(s): Hao Cui, Lei Chen*.

Journal Name: Current Proteomics

Volume 16 , Issue 5 , 2019

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Background: Identification of Enzyme Commission (EC) number of enzymes is quite important for understanding the metabolic processes that produce enough energy to sustain life. Previous studies mainly focused on predicting six main functional classes or sub-functional classes, i.e., the first two digits of the EC number.

Objective: In this study, a binary classifier was proposed to identify the full EC number (four digits) of enzymes. Enzymes and their known EC numbers were paired as positive samples and negative samples were randomly produced that were as many as positive samples. The associations between any two samples were evaluated by integrating the linkages between enzymes and EC numbers.

Method: The classic machining learning algorithm, support vector machine (SVM), was adopted as the prediction engine. The five-fold cross-validation test on five datasets indicated that the overall accuracy, Matthews correlation coefficient and F1-measure were about 0.786, 0.576 and 0.771, respectively, suggesting the utility of the proposed classifier.

Results: In addition, the effectiveness of the classifier was elaborated by comparing it with other classifiers that were based on other classic machine learning algorithms.

Conclusion: Finally, the proposed classifier was quite suitable for identification of EC number of enzymes by testing it on five datasets containing randomly produced samples.

Keywords: Enzyme, EC number, support vector machine, protein-protein interaction, Weka, binary classification, five-fold cross-validation.

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Article Details

Year: 2019
Page: [381 - 389]
Pages: 9
DOI: 10.2174/1570164616666190126103036
Price: $95

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