Validation of machine learning application for the identification of lipid metabolism-associated diagnostic model in ischemic stroke.

ABSTRACT:

Introduction

Ischemic Stroke (IS) is characterized by complex molecular alterations involving disruptions in lipid metabolism and immune interactions. However, the roles of lipid metabolism-associated genes in the pathogenesis of IS through immune regulation interaction are rarely explored. In this study, we aimed to explore the intricate correlation between lipid metabolism-associated immune changes and IS through a machine-learning algorithm.

Materials and methods

We downloaded the GSE16561, GSE22255, and GSE37587 datasets from NCBI. Using the GSE16561 dataset, we analyzed differential gene expression profiles related to lipid metabolism with the "Limma" R package. We constructed a diagnostic model employing techniques such as Least Absolute Shrinkage and Selection Operato

SUBMITTER: Meng X 

PROVIDER: S-EPMC11897713 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature