计算机应用研究2026，Vol.43Issue(3)：672-680,9.DOI:10.19734/j.issn.1001-3695.2025.08.0273
基于分布感知与扩张卷积金字塔的SNO位点预测方法
SNO site prediction method based on distribution perception and expanded convolutional pyramid
摘要
Abstract
S-nitrosylation(SNO)is a reversible post-translational modification of proteins that plays a crucial regulatory role in various physiological and pathological processes.To overcome the susceptibility of existing methods to false negative interfe-rence,cross-channel semantic redundancy,and challenges in semantic collaboration,the research integrates sample filtering with semantic modeling for SNO site prediction.This paper proposed a prediction model,SFEP-SNO,which integrated distri-bution awareness with semantic fusion.The model actively identified and removed false negative samples through a distribution-aware mechanism,thereby reducing their interference during training.In parallel,it employed an expanded convolutional py-ramid based on biologically driven feature encoding to achieve multi-scale semantic fusion and deep representation of key fea-tures.Experimental evaluations demonstrate that SFEP-SNO consistently improves accuracy,specificity,and AUC across mul-tiple independent datasets,confirming its capability to enhance discriminative performance in SNO site prediction and to facili-tate research on SNO-related mechanisms and disease diagnosis.关键词
S-亚硝基化/负样本采样/多语义特征表示/扩张卷积金字塔Key words
S-nitrosylation/negative sample selection/multi-semantic feature representation/expanded convolutional pyramid分类
信息技术与安全科学引用本文复制引用
段依蒙,牛帅军,闫宇利,李婷,李庆坤,王会青..基于分布感知与扩张卷积金字塔的SNO位点预测方法[J].计算机应用研究,2026,43(3):672-680,9.基金项目
山西省自然科学基金资助项目(202203021211121) （202203021211121）
