Machine learning in rheumatology approaches the clinic
Author:
Publisher
Springer Science and Business Media LLC
Subject
Rheumatology
Link
http://www.nature.com/articles/s41584-019-0361-0.pdf
Reference8 articles.
1. Sieberts, S. K. et al. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nat. Commun. 7, 12460 (2016).
2. Kim, K. J. et al. Compendium of synovial signatures identifies pathologic characteristics for predicting treatment response in rheumatoid arthritis patients. Clin. Immunol. 202, 1–10 (2019).
3. Guan, Y. et al. Machine learning to predict anti-TNF drug responses of rheumatoid arthritis patients by integrating clinical and genetic markers. Arthritis Rheum. 71, 1987–1996 (2019).
4. Eng, S. W. M. et al. Patterns of joint involvement in juvenile idiopathic arthritis and prediction of disease course: a prospective study with multilayer non-negative matrix factorization. PLOS Med. 16, e1002750 (2019).
5. Plenge, R. M. et al. Crowdsourcing genetic prediction of clinical utility in the Rheumatoid Arthritis Responder Challenge. Nat. Genet. 45, 468–469 (2013).
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