From data to diagnosis: how machine learning is revolutionizing biomarker discovery in idiopathic inflammatory myopathies.
Brief Bioinform
; 25(1)2023 11 22.
Article
em En
| MEDLINE
| ID: mdl-38243695
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doenças Autoimunes
/
Dermatomiosite
/
Miosite
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Adult
/
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article