Escherichia Coli DNA N-4-Methycytosine Site Prediction Accuracy Improved by Light Gradient Boosting Machine Feature Selection Technology

Abstract

Recently, several machine-learning-based DNA N-4-methycytosine (4mC) predictors have been developed to provide deeper insight into the biological functions and mechanisms of 4mC. However, the performance of the existing classifiers for identification of Escherichia coli DNA 4mC sites is inadequate. Here, we present a new support vector machine 4mC predictor, named iEC4mC-SVM, for Escherichia coli (E.coli) DNA 4mC site identification, optimized using light gradient boosting machine feature selection technology. The iEC4mC-SVM predictor had a 10-fold cross-validation accuracy of 85.4% and Jackknife cross-validation accuracy of 84.9%. The 83.2% independent testing accuracy of iEC4mC-SVM was 1.0-6.5% higher than those of state-of-the-art E. coli DNA 4mC site predictors. A t-distributed stochastic neighbor embedding analysis confirmed that the prediction performance enhancement of iEC4mC-SVM was due to the light gradient boosting machine feature selection.

Keywords

• Bioinformatics
• DNA
• machine learning
• support vector machine
• sequences