Machine learning-based screening of asthma biomarkers and related immune infiltration.

ABSTRACT:

Introduction

Asthma has an annual increasing morbidity rate and imposes a heavy social burden on public healthcare systems. The aim of this study was to use machine learning to identify asthma-specific genes for the prediction and diagnosis of asthma.

Methods

Differentially expressed genes (DEGs) related to asthma were identified by examining public sequencing data from the Gene Expression Omnibus, coupled with the support vector machine recursive feature elimination and least absolute shrinkage and selection operator regression model. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis and correlation analyses between gene and immune cell levels were performed. An ovalbumin-induced asthma mouse model was established, and eukaryot

SUBMITTER: Zhong X 

PROVIDER: S-EPMC11831286 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature