基于机器学习的DNA N4-甲基胞嘧啶位点预测研究

DNA N4-Methylcytosine Site Prediction Study Based on Machine Learning

脱氧核糖核酸(DeoxyriboNucleic Acid,DNA)N4-甲基胞嘧啶(N4-methylcytosine,4mC)修饰涉及基因表达、DNA复制等多个过程,是一种较为常见的DNA位点预测方法。对6个不同物种进行独热编码(One-Hot encoding),在进行算法训练和模型评估后发现,支持向量机模型的各项评估结果均高于其他算法,由此说明采用支持向量机模型预测4mC位点是最优选择。

DeoxyriboNucleic Acid(DNA)N4-methylcytosine(4mC)modification involves multiple processes such as gene expression and DNA replication,and is a common DNA site prediction method.After algorithm training and model evaluation,it was found that the evaluation results of the support vector machine model were higher than those of other algorithms,which showed that the support vector machine model was the best choice to predict 4mC sites.