多发性肌炎
包涵体肌炎
皮肌炎
肌炎
医学
生物标志物
疾病
肌病
生物标志物发现
炎性肌病
鉴定(生物学)
生物信息学
机器学习
计算机科学
病理
蛋白质组学
生物
生物化学
基因
植物
作者
Emily McLeish,Nataliya Slater,Frank Mastaglia,Merrilee Needham,Jérôme D. Coudert
摘要
Abstract Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of muscle disorders including adult and juvenile dermatomyositis, polymyositis, immune-mediated necrotising myopathy and sporadic inclusion body myositis, all of which present with variable symptoms and disease progression. The identification of effective biomarkers for IIMs has been challenging due to the heterogeneity between IIMs and within IIM subgroups, but recent advances in machine learning (ML) techniques have shown promises in identifying novel biomarkers. This paper reviews recent studies on potential biomarkers for IIM and evaluates their clinical utility. We also explore how data analytic tools and ML algorithms have been used to identify biomarkers, highlighting their potential to advance our understanding and diagnosis of IIM and improve patient outcomes. Overall, ML techniques have great potential to revolutionize biomarker discovery in IIMs and lead to more effective diagnosis and treatment.
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