Machine learning and single-cell RNA sequencing analyses identify MS-related monocytes and a five-gene candidate biomarker signature.

ABSTRACT:

Objective

Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease of the central nervous system (CNS). Based on single-cell RNA sequencing (scRNA-seq) data from experimental autoimmune encephalomyelitis (EAE), this study applied machine learning algorithms combined with integrative bioinformatics methods to identify pivotal biomarkers associated with MS-related monocytes.

Materials and methods

Machine learning and scRNA-seq analyses were performed to characterize MS-related monocytes, leading to the identification of five optimally characterized candidate biomarkers associated with pathogenic alterations. The performance of multiple algorithms, such as logistic regression (LogReg), latent Dirichlet allocation (LDA), support vector machine (SVM), Naive Bayes (NB)

SUBMITTER: Pan D 

PROVIDER: S-EPMC12932200 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature