Feature selection based on differentially correlated gene pairs reveals the mechanism of IFN-β therapy for multiple sclerosis.

ABSTRACT: Multiple sclerosis (MS) is one of the most common neurological disabilities of the central nervous system. Immune-modulatory therapy with Interferon-β (IFN-β) is a commonly used first-line treatment to prevent MS patients from relapses. Nevertheless, a large proportion of MS patients on IFN-β therapy experience their first relapse within 2 years of treatment initiation. Feature selection, a machine learning strategy, is routinely used in the fields of bioinformatics and computational biology to determine which subset of genes is most relevant to an outcome of interest. The majority of feature selection methods focus on alterations in gene expression levels. In this study, we sought to determine which genes are most relevant to relapse of MS patients on IFN-β therapy. Rather than the usual

SUBMITTER: Jin T 

PROVIDER: S-EPMC7081782 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature