Multiple sclerosis (MS) is a neuroinflammatory disorder characterized by autoimmune-mediated inflammatory lesions in CNS leading to myelin damage and axonal loss. MS is a heterogenous disease with variable and unpredictable disease course. Due to its complex nature, MS is difficult to diagnose and responses to specific treatments may vary between individuals. Therefore, there is an indisputable need for biomarkers for early diagnosis, prediction of disease exacerbations, monitoring the progression of disease, and for measuring responses to therapy. Genomic and proteomic studies have sought to understand the molecular basis of MS and find biomarker candidates. Advances in next-generation sequencing and mass-spectrometry techniques have yielded an unprecedented amount of genomic and proteomic data; yet, translation of the results into the clinic has been underwhelming. This has prompted the development of novel data science techniques for exploring these large datasets to identify biologically relevant relationships and ultimately point towards useful biomarkers. Herein we discuss optimization of omics study designs, advances in the generation of omics data, and systems biology approaches aimed at improving biomarker discovery and translation to the clinic for MS.

多发性硬化症 (MS) 是一种神经炎症性疾病，其特征是中枢神经系统中自身免疫介导的炎症病变，导致髓磷脂损伤和轴突损失。 MS 是一种异质性疾病，其病程可变且不可预测。由于其复杂性，多发性硬化症很难诊断，而且个体对特定治疗的反应可能有所不同。因此，毫无疑问需要生物标志物来进行早期诊断、预测疾病恶化、监测疾病进展以及测量对治疗的反应。基因组和蛋白质组研究试图了解多发性硬化症的分子基础并寻找候选生物标志物。新一代测序和质谱技术的进步产生了前所未有的基因组和蛋白质组数据；然而，将结果转化为临床的效果却并不理想。这促进了新的数据科学技术的发展，用于探索这些大型数据集，以识别生物学相关的关系，并最终找到有用的生物标志物。在此，我们讨论组学研究设计的优化、组学数据生成的进展以及旨在改善多发性硬化症生物标志物发现和临床转化的系统生物学方法。