Han Ke, Wang Jianchun, Chu Ying, Liao Qian, Ding Yijie, Zheng Dequan, Wan Jie, Guo Xiaoyi, Zou Quan
School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.
Methods. 2024 Oct;230:91-98. doi: 10.1016/j.ymeth.2024.07.012. Epub 2024 Aug 6.
DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately identifying its sites helps one to understand its biological effects more comprehensively. Previous traditional experimental methods are very labor-intensive and traditional machine learning methods also seem to be somewhat insufficient as the database of 6mA methylation groups becomes progressively larger, so we propose a deep learning-based method called multi-scale convolutional model based on global response normalization (CG6mA) to solve the prediction problem of 6mA site. This method is tested with other methods on three different kinds of benchmark datasets, and the results show that our model can get more excellent prediction results.
DNA N6-甲基腺嘌呤（6mA）在许多生物过程中发挥着重要作用，准确识别其位点有助于更全面地了解其生物学效应。以往传统的实验方法劳动强度大，随着6mA甲基化基团数据库的逐渐增大，传统机器学习方法似乎也略显不足，因此我们提出了一种基于深度学习的方法——基于全局响应归一化的多尺度卷积模型（CG6mA）来解决6mA位点的预测问题。该方法在三种不同的基准数据集上与其他方法进行了测试，结果表明我们的模型能够获得更优异的预测结果。
