内蒙古农业大学学报（自然科学版）2026，Vol.47Issue(3)：47-55,9.DOI:10.16853/j.cnki.1009-3575.2026.03.007
多特征结合机器学习算法识别蛋白质与组胺的结合位点
Multi-feature Combined Machine Learning Algorithm to Identify Proteins-Histamine Binding Sites
摘要
Abstract
Histamine,a bioactive molecule crucial in various diseases,plays a significant role in disease pathogenesis.An in-depth study of protein-histamine interactions helps to understand their binding mechanisms.This study combined multiple features with various machine learning algorithms and ensemble methods to predict proteins-histamine binding sites.The results showed that the gradient boosting(GB)model and average flexibility index(AF)feature within the machine learning algorithms yielded the best pre-diction results,achieving an accuracy(A)of 82.1%,a Matthews correlation coefficient(M)of 68.2%,and a sensitivity(S)of 65.5%.The multi-feature fusion and ensemble algorithm processing was applied to the codon usage frequency feature,which had poor prediction performance.The results indicated that the performance of the improved model was significantly enhanced.In fea-ture fusion,the combination of codon frequency and average flexibility index resulted in all metrics exceeding 60.0%.When the co-don frequency was used as a single feature in the ensemble algorithm prediction model,the accuracy(A)increased by 19.0%,and the Matthews correlation coefficient(M)increased by 66.1%compared to the optimal gradient boosting(GB)model.The result demonstrated that feature fusion and ensemble algorithms effectively predicted histamine binding sites.关键词
组胺/结合位点/特征融合/集成算法Key words
Histamine/Binding sites/Feature fusion/Ensemble algorithms分类
生物科学引用本文复制引用
牛晓玉,利民,冯永娥..多特征结合机器学习算法识别蛋白质与组胺的结合位点[J].内蒙古农业大学学报（自然科学版）,2026,47(3):47-55,9.基金项目
国家自然科学基金项目(62262050) （62262050）
