生物信息学2025，Vol.23Issue(2)：81-87,7.DOI:10.12113/202403005
基于单细胞转录组数据的疾病表型预测研究进展
Advances in the prediction of disease phenotypes using single-cell transcriptomic data
摘要
Abstract
Single-cell RNA sequencing(scRNA-seq)has been widely applied in basic medical research.Analyzing and mining scRNA-seq data facilitates an in-depth understanding of the cellular composition and function of diseased tissues,reveals complex disease processes,elucidates drug mechanisms of action,and promotes the development of precision medicine.However,how to predict patient disease phenotypes based on the massive amounts of scRNA-seq data and identify key features is a critical issue for the clinical translation of single-cell technologies.This article reviews relevant methods for predicting patient disease phenotypes using single-cell transcriptomic data,and summarizes their principles,algorithms,advantages and disadvantages.Finally,recommendations and perspectives on the application of related research are provided.关键词
单细胞转录组测序/疾病表型预测/机器学习/特征筛选Key words
Single-cell transcriptome sequencing/Disease phenotype prediction/Machine learning/Feature selection分类
生物学引用本文复制引用
张凌瑞,胡龙飞,黄万翔,孙啸,范珏..基于单细胞转录组数据的疾病表型预测研究进展[J].生物信息学,2025,23(2):81-87,7.基金项目
江苏省基础研究计划自然科学基金—青年基金项目(No.BK20230278). （No.BK20230278）
