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Editorial
. 2024 May 23;25(4):bbae295.
doi: 10.1093/bib/bbae295.

Balancing Clinical Applicability and Scientific Depth in ML Models for MDA5-DM Prognosis

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Editorial

Balancing Clinical Applicability and Scientific Depth in ML Models for MDA5-DM Prognosis

Emily McLeish et al. Brief Bioinform. .
No abstract available

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References

    1. Lu X, Peng Q, Wang G. Anti-MDA5 antibody-positive dermatomyositis: pathogenesis and clinical progress. Nat Rev Rheumatol 2024;20:48–62. 10.1038/s41584-023-01054-9. - DOI - PubMed
    1. Jin Q, Fu L, Yang H. et al. Peripheral lymphocyte count defines the clinical phenotypes and prognosis in patients with anti-MDA5-positive dermatomyositis. J Intern Med 2023;293:494–507. 10.1111/joim.13607. - DOI - PubMed
    1. McLeish E, Slater N, Mastaglia FL. et al. From data to diagnosis: how machine learning is revolutionizing biomarker discovery in idiopathic inflammatory myopathies. Brief Bioinform 2024;25:bbad514. - PMC - PubMed
    1. Moncada-Torres A, van Maaren MC, Hendriks MP. et al. Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci Rep 2021;11:6968. 10.1038/s41598-021-86327-7. - DOI - PMC - PubMed

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