Accurate detection of N6-methyladenine (6mA) sites by biochemical experiments will help to reveal their biological functions, still, these wet experiments are laborious and expensive. Therefore, it is necessary to introduce a powerful computational model to identify the 6mA sites on a genomic scale, especially for plant genomes. In view of this, we proposed a model called iDNA6mA-Rice-DL for the effective identification of 6mA sites in rice genome, which is an intelligent computing model based on deep learning method. Traditional machine learning methods assume the preparation of the features for analysis. However, our proposed model automatically encodes and extracts key DNA features through an embedded layer and several groups of dense layers. We use an independent dataset to evaluate the generalization ability of our model. An area under the receiver operating characteristic curve (auROC) of 0.98 with an accuracy of 95.96% was obtained. The experiment results demonstrate that our model had good performance in predicting 6mA sites in the rice genome. A user-friendly local web server has been established. The Docker image of the local web server can be freely downloaded at https://hub.docker.com/r/his1server/idna6ma-rice-dl.

中文翻译：

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iDNA6mA-Rice-DL：通过深度学习方法识别水稻基因组中 DNA N6-甲基腺嘌呤位点的本地网络服务器

通过生化实验准确检测N6-甲基腺嘌呤（6mA）位点将有助于揭示其生物学功能，但这些湿实验既费力又昂贵。因此，有必要引入一个强大的计算模型来识别基因组规模上的 6mA 位点，特别是对于植物基因组。有鉴于此，我们提出了一种名为iDNA6mA-Rice-DL的模型，用于有效识别水稻基因组中的6mA位点，这是一种基于深度学习方法的智能计算模型。传统的机器学习方法假定为分析准备特征。然而，我们提出的模型通过嵌入层和几组密集层自动编码和提取关键 DNA 特征。我们使用一个独立的数据集来评估我们模型的泛化能力。获得的受试者工作特征曲线下面积 (auROC) 为 0.98，准确率为 95.96%。实验结果表明，我们的模型在预测水稻基因组中的 6mA 位点方面具有良好的性能。一个用户友好的本地网络服务器已经建立。本地 Web 服务器的 Docker 镜像可以在以下位置免费下载https://hub.docker.com/r/his1server/idna6ma-rice-dl.