计算机科学与探索2026，Vol.20Issue(6)：1637-1646,10.DOI:10.3778/j.issn.1673-9418.2510043
DBm5U-Deep:预测m5U位点的多尺度深度学习模型
DBm5U-Deep:Multiscale Deep Learning Model for m5U Site Prediction
摘要
Abstract
RNA 5-methyluridine(m5U)plays crucial biological roles in organismal growth and development,gene expression regulation,and disease onset.Therefore,accurate identification and prediction of m5U-modified sites are of significant importance.Compared with traditional experimental methods,deep learning technologies enable more efficient and cost-effective m5U site prediction.However,existing approaches still exhibit limitations in feature representation capability and predictive accuracy.To address these challenges,the DBm5U-Deep model is proposed,innovatively constructing a collab-orative multi-scale deep framework.This model enhances sequence representation through hierarchical feature extraction and optimizes final predictions using a weighted averaging strategy.Specifically,ribonucleic acid(RNA)sequences are first segmented into 3-mer fragments and converted into low-dimensional continuous vector representations using the GloVe word vector model.These vectors are then fed into a core architecture comprising a convolutional neural network(CNN),a bidirectional long short-term memory network(BiLSTM),and a multi-attention mechanism to fully capture local features and long-range dependencies.Finally,a fully connected layer performs the classification prediction.In five-fold cross-validation,the DBm5U-Deep model achieves AUC and ACC of 97.26%and 92.87%,respectively.On an independent test set,AUC and ACC reach 97.30%and 93.55%,surpassing the current state-of-the-art model by 0.26 and 1.26 percent-age points,respectively.Experimental results demonstrate that DBm5U-Deep exhibits high accuracy and stability in m5U site prediction,providing an efficient and reliable computational tool for RNA modification functional studies and drug target screening.关键词
核糖核酸(RNA)甲基化/深度学习/生物信息学Key words
ribonucleic acid(RNA)methylation/deep learning/bioinformatics分类
信息技术与安全科学引用本文复制引用
王梦园,刘欢,龙威,聂金瞳..DBm5U-Deep:预测m5U位点的多尺度深度学习模型[J].计算机科学与探索,2026,20(6):1637-1646,10.基金项目
国家自然科学基金(62502400) （62502400）
四川省自然科学基金(2023NSFSC1417) （2023NSFSC1417）
西南科技大学研究生创新基金(25ycx1102). This work was supported by the National Natural Science Foundation of China(62502400),the Natural Science Foundation of Sichuan Province(2023NSFSC1417),and the Graduate Innovation Fund of Southwest University of Science and Technology(25ycx1102). （25ycx1102）
