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多发性硬化症与计算生物学(综述)

Multiple sclerosis and computational biology (Review).

作者信息

Diakou Io, Papakonstantinou Eleni, Papageorgiou Louis, Pierouli Katerina, Dragoumani Konstantina, Spandidos Demetrios A, Bacopoulou Flora, Chrousos George P, Goulielmos Georges Ν, Eliopoulos Elias, Vlachakis Dimitrios

机构信息

Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece.

Laboratory of Clinical Virology, School of Medicine, University of Crete, 71003 Heraklion, Greece.

出版信息

Biomed Rep. 2022 Oct 18;17(6):96. doi: 10.3892/br.2022.1579. eCollection 2022 Dec.

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.

摘要

多发性硬化症(MS)是一种自身免疫性神经退行性疾病,其在全球范围内的患病率呈上升趋势。由此产生的症状可能使人衰弱,并会大幅降低患者的生活质量。计算生物学涉及使用计算工具来回答生物医学问题,它可能为多发性硬化症背景下的新型医疗方法提供基础。健康数据的快速积累、不断增强的计算能力以及不断发展的技术,有助于使多发性硬化症研究实现现代化并得到完善。从发现新型生物标志物到优化治疗以及为患者提升多项生活质量,计算生物学方法和工具正在塑造多发性硬化症的诊断、管理和治疗领域。在这种复杂疾病中的最终目标将是个性化医疗,即根据个体患者疾病的特定生物学特性以及他们所面临的环境因素,提供针对个体患者量身定制的医疗服务。本综述文章总结了关于多发性硬化症、现代计算生物学以及现代计算方法对多发性硬化症影响的当前知识。

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本文引用的文献

1
Multiple Sclerosis Severity Estimation and Progression Prediction Based on Machine Learning Techniques.
Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1109-1112. doi: 10.1109/EMBC48229.2022.9871213.
2
Remyelination Trials: Are We Expecting the Unexpected?
Neurol Neuroimmunol Neuroinflamm. 2021 Aug 10;8(6). doi: 10.1212/NXI.0000000000001066. Print 2021 Nov.
4
Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer's Disease.
Cells. 2021 Jun 29;10(7):1627. doi: 10.3390/cells10071627.
5
Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis.
J Neurol. 2021 Dec;268(12):4834-4845. doi: 10.1007/s00415-021-10605-7. Epub 2021 May 10.
6
Multiple sclerosis is linked to MAPK overactivity in microglia.
J Mol Med (Berl). 2021 Aug;99(8):1033-1042. doi: 10.1007/s00109-021-02080-4. Epub 2021 May 5.
7
Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data.
Nat Commun. 2021 Apr 6;12(1):2078. doi: 10.1038/s41467-021-22265-2.
8
Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.
Front Microbiol. 2021 Feb 22;12:635781. doi: 10.3389/fmicb.2021.635781. eCollection 2021.
9
Pre-clinical and Clinical Implications of "Inside-Out" vs. "Outside-In" Paradigms in Multiple Sclerosis Etiopathogenesis.
Front Cell Neurosci. 2020 Oct 27;14:599717. doi: 10.3389/fncel.2020.599717. eCollection 2020.
10
The emerging role of artificial intelligence in multiple sclerosis imaging.
Mult Scler. 2022 May;28(6):849-858. doi: 10.1177/1352458520966298. Epub 2020 Oct 28.

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