Using biomarkers to predict progression from clinically isolated syndrome to multiple sclerosis
Using machine learning techniques, including support vector machines, this study demonstrates that gene expression signatures can accurately identify subjects with a clinically isolated syndrome who later progress to multiple sclerosis.
Gene-expression signatures: biomarkers toward diagnosing multiple sclerosis
Results indicate that gene-expression differences in blood accurately exclude or include a diagnosis of MS and suggest that these approaches may provide clinically useful prediction of MS.
Identification of Molecular Biomarkers for Multiple Sclerosis
Quantitative real-time polymerase chain reaction analysis was used to identify a minimum number of genes of which transcript levels discriminated multiple sclerosis patients from patients with other chronic diseases and from controls.