Escherichia Coli DNA N-4-Methycytosine Site Prediction Accuracy Improved by Light Gradient Boosting Machine Feature Selection Technology
Escherichia Coli DNA N-4-Methycytosine Site Prediction Accuracy Improved by Light Gradient Boosting Machine Feature Selection Technology
复制标题
光梯度增强机特征选择技术提高大肠杆菌 DNA N-4-甲基胞嘧啶位点预测精度
DOI：
10.1109/access.2020.2966576
复制
发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Xu, Lei
中科院分区：
文献类型：
--
作者：
Lv, Zhibin;Wang, Donghua;Xu, Lei
最近，开发了几种基于机器学习的 DNA N-4-甲基胞嘧啶 (4mC) 预测器，以更深入地了解 4mC 的生物学功能和机制。然而，现有分类器用于鉴定大肠杆菌 DNA 4mC 位点的性能不足。在这里，我们提出了一种新的支持向量机 4mC 预测器，名为 iEC4mC-SVM，用于大肠杆菌 (E.coli) DNA 4mC 位点识别，并使用光梯度增强机特征选择技术进行了优化。 iEC4mC-SVM 预测器的 10 倍交叉验证准确率为 85.4%，Jackknife 交叉验证准确率为 84.9%。 iEC4mC-SVM 的独立测试准确度为 83.2%，比最先进的大肠杆菌 DNA 4mC 位点预测器高出 1.0-6.5%。 t 分布随机邻域嵌入分析证实 iEC4mC-SVM 的预测性能增强归因于光梯度增强机特征选择。
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.