Inferring Multiple Sclerosis Stages from the Blood Transcriptome Via Machine Learning

Overview

Journal Cell Rep Med

Publisher Cell Press

Specialties Biology
General Medicine

Date 2020 Nov 18

PMID 33205062

Citations 11

Authors

Massimo Acquaviva

Ramesh Menon

Marco Di Dario

Gloria Dalla Costa

Marzia Romeo

Francesca Sangalli

Bruno Colombo

Lucia Moiola

Vittorio Martinelli

Giancarlo Comi

Cinthia Farina

Affiliations

Soon will be listed here.

Abstract

Peripheral blood mononuclear cells (PBMCs) bear specific dysregulations in genes and pathways at distinct stages of multiple sclerosis (MS) that may help with classifying MS and non-MS subjects, specifying the early stage of disease, or discriminating among MS courses. Here we describe an unbiased machine learning workflow to build MS stage-specific classifiers based on PBMC transcriptomics profiles from more than 300 individuals, including healthy subjects and patients with clinically isolated syndromes, relapsing-remitting MS, primary or secondary progressive MS, or other neurological disorders. The pipeline, designed to optimize and compare the performance of distinct machine learning algorithms in the training cohort, generates predictive models not influenced by demographic features, such as age and gender, and displays high accuracy in the independent validation cohort. Proper application of machine learning to transcriptional profiles of circulating blood cells may allow identification of disease state and stage in MS.

Citing Articles

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