Han Ke, Wang Jianchun, Wang Yu, Zhang Lei, Yu Mengyao, Xie Fang, Zheng Dequan, Xu Yaoqun, Ding Yijie, Wan Jie
School of Computer and Information Engineering, Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin University of Commerce, Harbin, 150028, China.
College of Pharmacy, Harbin University of Commerce, Harbin, 150076, China.
Brief Bioinform. 2023 Jan 19;24(1). doi: 10.1093/bib/bbac514.
Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.
脱氧核糖核酸（DNA）的N6-甲基腺嘌呤在各种生物过程中起着至关重要的作用，准确识别其位点可以更全面地了解其生物学效应。有几种预测6mA位点的方法。随着技术的不断发展，成本高、效率低的传统技术正逐渐被计算机方法所取代。广泛使用的计算机方法可分为两类：传统机器学习方法和深度学习方法。我们首先列出一些现有的预测6mA位点的实验方法，然后分析计算机方法中从序列输入到结果的一般过程，并回顾现有的模型架构。最后，对结果进行总结和比较，以便后续研究人员为其工作选择最合适的方法。
