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 focus on alterations in gene expression levels, we devised a feature selection method based on alterations in geneto- gene interactions. In this study, we applied the proposed method to a longitudinal microarray dataset and evaluated the IFN-β effect on MS patients to identify gene pairs with differentially correlated edges that are consistent over time in the responder group compared to the non-responder group. The resulting gene list had a good predictive ability on an independent validation set and explicit biological implications related to MS. To conclude, it is anticipated that the proposed method will gain widespread interest and application in personalized treatment research to facilitate prediction of which patients may respond to a specific regimen.
|State||Published - 2020|
Bibliographical noteFunding Information:
The following grant information was disclosed by the authors: Education Department of Jilin Province: JJKH20190032KJ. Natural Science Foundation of China: 81671177 and 31401123. Natural Science Foundation of Jilin Province Science and Technology Development Plan Project: 20190201043JC.
This study was supported by the Education Department of Jilin Province (grant No. JJKH20190032KJ), the Natural Science Foundation of China (grants Nos. 81671177 and 31401123), and the Natural Science Foundation of Jilin Province Science and Technology Development Plan Project (No. 20190201043JC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright © 2020 Jin et al.
- Differentially correlated edge
- Feature selection
- IFN-beta therapy
- Multiple sclerosis
- Treatment response
ASJC Scopus subject areas
- Neuroscience (all)
- Biochemistry, Genetics and Molecular Biology (all)
- Agricultural and Biological Sciences (all)