西安交通大学学报（医学版）2026，Vol.47Issue(2)：283-290,8.DOI:10.7652/jdyxb202602012
基于转录组的慢性肾脏病预测研究
A transcriptome-based study on CKD prediction
摘要
Abstract
Objective To explore the effectiveness of identifying potential characteristic genes for chronic kidney disease(CKD)and predicting sample classification based on transcriptome.Methods Transcriptomic data from CKD and healthy kidney samples were analyzed using correlation-based dimensionality reduction and random forest to select key genes.These genes were used for clustering and constructing a neural network model to classify CKD and healthy samples.Clustering significance was assessed via silhouette scores,accuracy,and confusion matrix.Results Eleven key genes were identified from 14 801 features.Clustering showed high cohesion and separation(silhouette score:0.84),with an accuracy of 0.97.The neural network achieved an accuracy of 0.985 and an ROC-AUC of 0.99.Conclusion This approach can effectively identify CKD-related genes and enable accurate classification,offering insights for research and clinical applications.关键词
慢性肾脏病(CKD)/基因筛选/随机森林/神经网络/聚类分析Key words
chronic kidney disease(CKD)/gene screening/random forest/neural network/clustering analysis分类
医药卫生引用本文复制引用
颜世贵,姚雨璐,何娟,李桂荣,孙宏波..基于转录组的慢性肾脏病预测研究[J].西安交通大学学报（医学版）,2026,47(2):283-290,8.基金项目
国家级大学生创新训练资助计划项目(No.202410698215)Supported by the National College Students'Innovation and Entrepreneurship Training Program(No.202410698215) （No.202410698215）
