计算机应用与软件2025，Vol.42Issue(10)：94-101,8.DOI:10.3969/j.issn.1000-386x.2025.10.013
基于自注意力机制的单核苷酸无义突变致病性预测
PATHOGENICITY PREDICTION OF SINGLE NUCLEOTIDE NONSENSE MUTATIONS BASED ON SELF-ATTENTION
摘要
Abstract
Single nucleotide nonsense mutations in gene sequences can have severe effects on downstream sequences,in order to solve the problem,a deep learning model based on self-attention is proposed to predict the pathogenicity of single nucleotide nonsense mutations,and named PON-NS.A novel nonsense mutation dataset was constructed by filtering nonsense mutations from ClinVar and VariSNP.The hidden features in the contextual sequence of the mutated location before and after the mutation were learned by the self-attention mechanism in Transformer and combined with sequence-derived features for prediction.Compared with existing methods,PON-NS achieved better performance in blind testing,with ACC,AUC and MCC respectively reaching 0.920,0.950 and 0.842.In particular,PON-NS reduced the false positive rate by 39.7％in the ExAC validation set compared with the DDIG-in method,which was also based on DNA level prediction.关键词
单点突变/无义突变/DNA序列/自注意力机制Key words
Single nucleotide mutation/Nonsense mutation/DNA sequences/Self-attention分类
计算机与自动化引用本文复制引用
刘勇,杨洋..基于自注意力机制的单核苷酸无义突变致病性预测[J].计算机应用与软件,2025,42(10):94-101,8.基金项目
江苏省高等学校自然科学研究重大项目(20KJA520010) （20KJA520010）
江苏高校优势学科建设工程资助项目. （）
