计算机与现代化Issue(5)：57-65,9.DOI:10.3969/j.issn.1006-2475.2026.05.007
CBEIL17:IL-17诱导肽预测的深度学习方法
CBEIL17:Deep Learning Method for IL-17 Inducing Peptides Prediction
摘要
Abstract
Interleukin-17(IL-17)is an important glycoprotein whose sustained production is closely related to the pathological mechanisms of various autoimmune and inflammatory diseases.Meanwhile,it also plays an indispensable role in the defense mechanism of the body against extracellular pathogen infections.Therefore,accurate identification of IL-17-inducing peptides is of great significance for the study of IL-17.Since the biological experimental identification of IL-17-inducing peptides is time-consuming and costly,this study proposes a prediction method based on deep learning and an image feature enhancement strat-egy to improve the identification efficiency of IL-17-inducing peptides.First,multi-view feature encoding is performed on IL-17-inducing peptide sequences,including conventional feature descriptors,pretrained embedding features from protein lan-guage models,cellular automata image encoding features,and local structural features.Subsequently,the multi-view features are processed based on the image feature enhancement strategy,and the enhanced features are combined with the original fea-tures.Finally,the combined features are input into a convolutional neural network and a bidirectional temporal convolutional net-work to further extract deep features and construct the model.Experimental results show that this method achieves an AUC of 85.7%on the independent test dataset,demonstrating good predictive performance and providing a new tool for the identification of IL-17-inducing peptides.关键词
IL-17诱导肽/深度学习/特征增强/卷积神经网络/双向时间卷积网络Key words
IL-17-inducing peptides/deep learning/feature enhancement/convolutional neural network/bidirectional tempo-ral convolutional network分类
信息技术与安全科学引用本文复制引用
吴志强,丁逸雯,于博,曹安琦,李乐一,徐凯,程娜..CBEIL17:IL-17诱导肽预测的深度学习方法[J].计算机与现代化,2026,(5):57-65,9.基金项目
国家自然科学基金资助项目(62402006) （62402006）
