Ju Hong, Bai Jie, Jiang Jing, Che Yusheng, Chen Xin
Heilongjiang Agricultural Engineering Vocational College, Harbin, China.
Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education, Hangzhou, China.
Front Genet. 2023 Aug 21;14:1254827. doi: 10.3389/fgene.2023.1254827. eCollection 2023.
DNA N4-methylcytosine (4mC) is significantly involved in biological processes, such as DNA expression, repair, and replication. Therefore, accurate prediction methods are urgently needed. Deep learning methods have transformed applications that previously require sequencing expertise into engineering challenges that do not require expertise to solve. Here, we compare a variety of state-of-the-art deep learning models on six benchmark datasets to evaluate their performance in 4mC methylation site detection. We visualize the statistical analysis of the datasets and the performance of different deep-learning models. We conclude that deep learning can greatly expand the potential of methylation site prediction.
DNA N4-甲基胞嘧啶(4mC)在诸如DNA表达、修复和复制等生物过程中发挥着重要作用。因此,迫切需要准确的预测方法。深度学习方法已将以前需要测序专业知识的应用转变为无需专业知识即可解决的工程挑战。在此,我们在六个基准数据集上比较了各种先进的深度学习模型,以评估它们在4mC甲基化位点检测中的性能。我们可视化了数据集的统计分析和不同深度学习模型的性能。我们得出结论,深度学习可以极大地扩展甲基化位点预测的潜力。