山西大学学报（自然科学版）2024，Vol.47Issue(5)：982-992,11.DOI:10.13451/j.sxu.ns.2024032
非线性子空间驱动下的耐药性预测方法
Nonlinear Subspace-Driven Drug Resistance Prediction
摘要
Abstract
The task of predicting drug resistance in cancer has emerged as a prospective research direction in the field of precision medicine.To address the challenge of limited representation of the synergistic relationship between drugs and cell lines in existing resistance prediction methods,this paper proposes a nonlinear subspace collaborative learning model,named NLS-DRP(Nonlinear Subspace-Driven Drug Resistance Prediction).The NLS-DRP consists of three key learning modules:the Cell branch,the Drug branch,and the Collaborative Fusion module.These modules are used to construct nonlinear subspaces for extracting cell line fea-tures,decompose drug structures to extract subsequence features,and design a nonlinear collaborative space for the fusion of cell line and drug features,respectively.Finally,by integrating the features from the three modules,the resistance of cell lines to drugs is predicted.Experiments conducted on two public datasets,the Genomics of Drug Sensitivity in Cancer(GDSC)and the Cancer Cell Line Encyclopedia(CCLE),demonstrate that the proposed NLS-DRP model significantly outperforms the benchmark methods,achieving a Pearson Correlation Coefficient(PCC)value of 0.945 8 and a Spearman's Correlation Coefficient(SCC)value of 0.924 2,thereby confirming the effectiveness of the method presented in our paper.关键词
图神经网络/特征融合/非线性子空间/智能用药Key words
graph neural network/feature fusion/nonlinear subspace/intelligent medication分类
信息技术与安全科学引用本文复制引用
董云云,张源榕,龚怡丰,白玉洁,常云青,杨炳乾,杨紫婷,徐双,强彦..非线性子空间驱动下的耐药性预测方法[J].山西大学学报（自然科学版）,2024,47(5):982-992,11.基金项目
国家自然科学基金(62306206 （）
62102280) （）
山西省重点研发计划项目(202101010101007 （）
202102020101001) （）
山西省基础研究计划资助项目(202203021212207 （）
20210302124167) （）
