DNA N6-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou’s 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on Mus musculus, Rice, and “Combined-species” genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.

中文翻译：

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DNA6mA-MINT：DNA-6mA 修饰识别神经工具

DNA N6-甲基腺嘌呤 (6mA) 是众多生物过程的一部分，包括 DNA 修复、DNA 复制和 DNA 转录。在考虑其生物学功能时，6mA 修饰位点具有很大的影响。为此进行了生化实验研究，并取得了良好的效果。然而，在成本和时间参数下访问时，它们被证明不是一个实用的解决方案。这促使研究人员开发计算模型来满足修改识别的要求。在共识中，我们开发了周氏 5 步规则推荐的计算模型。神经网络 (NN) 模型使用卷积层从编码的二进制序列中提取高级特征。通过使用长短期记忆 (LSTM) 层对这些提取的特征进行了最佳解释。与最先进的技术相比，所提出的架构显示出更高的性能。提议的模型在 Mus musculus、Rice 和“组合物种”基因组上进行了评估，并进行了 5 倍和 10 倍的交叉验证。此外，通过访问用户友好的网络服务器，可以免费访问公开可用的内容。