Integrating machine learning for the identification of ubiquitination-associated genes in moyamoya disease.

ABSTRACT:

Introduction

Moyamoya disease (MMD) is an infrequent cerebrovascular disorder typified by bilateral internal carotid artery obstruction, yet its pathogenic mechanism remains elusive. This study examines the role of epigenetic ubiquitination-related genes in MMD.

Methods

We utilized two datasets (GSE157628 and GSE141024) from the GEO database and sourced ubiquitination-related genes from the GeneCards database. Differentially expressed genes were identified, followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to elucidate key gene functions. Machine learning techniques, including LASSO logistic regression and support vector machine, helped identify crucial genes. Immune characteristics were analyzed using sin

SUBMITTER: Niu H 

PROVIDER: S-EPMC12479308 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature