From data to diagnosis: how machine learning is revolutionizing biomarker discovery in idiopathic inflammatory myopathies

• Published in: Briefings in bioinformatics Vol. 25; no. 1
• Main Authors: McLeish, Emily, Slater, Nataliya, Mastaglia, Frank L, Needham, Merrilee, Coudert, Jerome D
• Format: Journal Article
• Language: English
• Published: England 22.11.2023
• Subjects: Adult; Autoimmune Diseases; Biomarkers; Dermatomyositis - diagnosis; Disease Progression; Humans; Myositis - diagnosis; Myositis - therapy; idiopathic inflammatory myopathies; machine learning; biomarkers; myositis-specific autoantibodies
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbad514

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.