计算机技术与发展2026，Vol.36Issue(4)：1-8,8.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0288
深度学习驱动的肽段从头测序模型演进研究
Deep Learning-driven Advancements in De Novo Peptide Sequencing Models
摘要
Abstract
We systematically review recent advances in deep learning techniques for de novo peptide sequencing,with a specific focus on the model evolution and performance optimization of three mainstream architectures:convolutional neural networks(CNN),graph convo-lutional networks(GCN),and Transformers.Through longitudinal comparisons of representative model architectures across different de-velopmental stages and transversal analysis of technical characteristics among different models,we systematically elucidate the pivotal role of deep learning in enhancing the accuracy and efficiency of de novo peptide sequencing.Experimental validation demonstrates that CNN models demonstrate significant advantages in processing speed owing to their efficient local feature extraction capability;GCN models enhance cross-species generalization ability by modeling topological relationships among spectral peaks;while the Transformer architecture excels in sequencing long peptides and handling non-canonical fragmentation patterns,owing to its global dependency modeling and parallel decoding mechanism.This study provides effective theoretical support and valuable technical references for database-independent peptide identification in proteomics and identifies preliminary directions for future model optimization.关键词
肽段从头测序/深度学习/卷积神经网络/图卷积网络/TransformerKey words
de novo peptide sequencing/deep learning/convolutional neural network(CNN)/graph convolutional network(GCN)/Transformer分类
信息技术与安全科学引用本文复制引用
柳楠,刘承慧,张航,邱宁宁..深度学习驱动的肽段从头测序模型演进研究[J].计算机技术与发展,2026,36(4):1-8,8.基金项目
国家自然科学基金青年项目(61902221) （61902221）
山东省研究生教育教学改革研究项目(SDYJG21173) （SDYJG21173）
山东省教育教学改革研究重点项目(Z2024153) （Z2024153）
