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Multiple Sclerosis and Computational Biology (Review)

Abstract

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.

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References
1.
Fernandez-Menendez S, Fernandez-Moran M, Fernandez-Vega I, Perez-Alvarez A, Villafani-Echazu J . Epstein-Barr virus and multiple sclerosis. From evidence to therapeutic strategies. J Neurol Sci. 2016; 361:213-9. DOI: 10.1016/j.jns.2016.01.013. View

2.
Cervantes-Gracia K, Husi H . Integrative analysis of Multiple Sclerosis using a systems biology approach. Sci Rep. 2018; 8(1):5633. PMC: 5884799. DOI: 10.1038/s41598-018-24032-8. View

3.
Nedjati-Gilani G, Schneider T, Hall M, Cawley N, Hill I, Ciccarelli O . Machine learning based compartment models with permeability for white matter microstructure imaging. Neuroimage. 2017; 150:119-135. DOI: 10.1016/j.neuroimage.2017.02.013. View

4.
Naismith R, Wundes A, Ziemssen T, Jasinska E, Freedman M, Lembo A . Diroximel Fumarate Demonstrates an Improved Gastrointestinal Tolerability Profile Compared with Dimethyl Fumarate in Patients with Relapsing-Remitting Multiple Sclerosis: Results from the Randomized, Double-Blind, Phase III EVOLVE-MS-2 Study. CNS Drugs. 2020; 34(2):185-196. PMC: 7018784. DOI: 10.1007/s40263-020-00700-0. View

5.
McShane L, Polley M . Development of omics-based clinical tests for prognosis and therapy selection: the challenge of achieving statistical robustness and clinical utility. Clin Trials. 2013; 10(5):653-65. PMC: 4410005. DOI: 10.1177/1740774513499458. View