Design of an unbiased machine learning workflow to predict Multiple Sclerosis staging from blood transcriptome

Construction of gene expression-based classifiers to predict the different Multiple Sclerosis stages from peripheral blood mononuclear cells (PBMC) transcriptome of MS patients and controls. Total RNA obtained from PBMCs of patients affected with different MS forms was used to build highly specific gene expression-based classifiers predicting single MS stages and differentiating MS state from healthy controls and other neurological diseases. .