The 5-Methyluridine (m5U), predominantly present in RNA and especially enriched in transfer RNA (tRNA), significantly enhances translational accuracy and protein synthesis by ensuring precise genetic information decoding and optimal tRNA functionality within cellular mechanisms. The identification of m5U modification sites is crucial, as this modification has gained significant attention in diseases such as breast cancer, stress response, and viral infections, offering insights into its molecular mechanisms and regulatory functions in disease contexts. Nevertheless, due to the arduous nature, intricate procedures, reliance on sophisticated and expensive instrumentation, and the need for specialized expertise, conventional biochemical approaches for identifying m5U modification sites result in substantial resource expenditures and notable temporal investments. Consequently, the pressing need for a precise and efficient computational method highlights the urgency for alternative approaches in identifying m5U modification sites. In this study, we introduce a novel computational approach called “Deep-m5U,” which combines the strengths of Convolutional Neural Networks (CNNs) and tetranucleotide composition to accurately identify methyluridine modification sites and improve overall performance. The developed Deep-m5U method leverages CNNs to accurately detect protein-coding regions aand capture relevant motifs, while incorporating tetra-nucleotide composition to capture global compositional characteristics, resulting in a more robust model that significantly enhances performance. We evaluated the Deep-m5U model on two publicly available benchmark datasets: the full transcript and mature mRNA datasets. Our results showcase superior performance, achieving accuracies of 91.26% and 95.63% respectively, surpassing the current cutting-edge methods. Moreover, the open-source code for Deep-m5U is freely accessible at: https://github.com/waleed551/Deep-m5U .

中文翻译：

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通过利用卷积神经网络和四核苷酸组成的新颖特征融合模型揭示 RNA 5-甲基尿苷修饰位点的潜在模式表示

5-甲基尿苷 (m5U) 主要存在于 RNA 中，尤其富含转移 RNA (tRNA)，通过确保细胞机制中精确的遗传信息解码和最佳 tRNA 功能，显着提高翻译准确性和蛋白质合成。 m5U 修饰位点的识别至关重要，因为这种修饰在乳腺癌、应激反应和病毒感染等疾病中引起了广泛关注，有助于深入了解其在疾病背景下的分子机制和调节功能。然而，由于其艰巨的性质、复杂的程序、对复杂且昂贵的仪器的依赖以及对专业知识的需要，用于识别 m5U 修饰位点的传统生化方法导致大量的资源支出和显着的时间投资。因此，对精确有效的计算方法的迫切需要凸显了寻找替代方法来识别 m5U 修饰位点的紧迫性。在这项研究中，我们引入了一种名为“Deep-m5U”的新型计算方法，该方法结合了卷积神经网络（CNN）和四核苷酸组成的优势，可以准确识别甲基尿苷修饰位点并提高整体性能。开发的 Deep-m5U 方法利用 CNN 准确检测蛋白质编码区域并捕获相关基序，同时结合四核苷酸组成来捕获全局组成特征，从而形成更稳健的模型，显着提高性能。我们在两个公开可用的基准数据集上评估了 Deep-m5U 模型：完整转录本和成熟的 mRNA 数据集。我们的结果展示了卓越的性能，准确率分别达到 91.26% 和 95.63%，超越了当前的尖端方法。此外，Deep-m5U 的开源代码可在以下位置免费访问：https://github.com/waleed551/Deep-m5U 。