Machine learning-based prediction of disease progression in primary progressive multiple sclerosis.

ABSTRACT: Primary progressive multiple sclerosis (PPMS) affects 10-15% of multiple sclerosis patients and presents significant variability in the rate of disability progression. Identifying key biological features and patients at higher risk for fast progression is crucial to develop and optimize treatment strategies. Peripheral blood cell transcriptome has the potential to provide valuable information to predict patients' outcomes. In this study, we utilized a machine learning framework applied to the baseline blood transcriptional profiles and brain MRI radiological enumerations to develop prognostic models. These models aim to identify PPMS patients likely to experience significant disease progression and who could benefit from early treatment intervention. RNA-sequence analysis was performed on

SUBMITTER: Gurevich M 

PROVIDER: S-EPMC11707605 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature