
Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes
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SLE pathogenesis is multifaceted, but certain autoantibodies are a hallmark and can be used as diagnostic and predictive biomarkers. But although over 200 autoantibodies have been described in SLE, only 10–20 are widely available. This machine-learning study aims to identify autoantibody clusters, and link them to long-term clinical outcomes.
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Original article:
Choi MY, Chen I, Clarke AE, Fritzler MJ, Buhler KA, Urowitz M, Hanly J, St-Pierre Y, Gordon C, Bae SC, Romero-Diaz J, Sanchez-Guerrero J, Bernatsky S, Wallace DJ, Isenberg DA, Rahman A, Merrill JT, Fortin PR, Gladman DD, Bruce IN, Petri M, Ginzler EM, Dooley MA, Ramsey-Goldman R, Manzi S, Jönsen A, Alarcón GS, van Vollenhoven RF, Aranow C, Mackay M, Ruiz-Irastorza G, Lim S, Inanc M, Kalunian K, Jacobsen S, Peschken C, Kamen DL, Askanase A, Buyon JP, Sontag D, Costenbader KH. Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes. Ann Rheum Dis. 2023 Jul;82(7):927-936. doi: 10.1136/ard-2022-223808. Epub 2023 Apr 21. PMID: 37085289.
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