Machine learning using genetic and clinical data identifies a signature that robustly predicts methotrexate response in rheumatoid arthritis.
Lee Jin LimAshley J W LimBrandon N S OoiJustina Wei Lynn TanEe Tzun Kohnull nullSamuel S ChongChiea Chuen KhorLisa Tucker-KelloggCaroline G L LeeKhai Pang LeongPublished in: Rheumatology (Oxford, England) (2022)
Sensitive and specific predictors of MTX response in RA patients were identified in this study through a novel strategy combining biologically meaningful and machine learning feature selection and training. These predictors may facilitate better treatment decision-making in RA management.
Keyphrases
- machine learning
- rheumatoid arthritis
- disease activity
- end stage renal disease
- big data
- artificial intelligence
- newly diagnosed
- genome wide
- chronic kidney disease
- ankylosing spondylitis
- ejection fraction
- interstitial lung disease
- systemic lupus erythematosus
- dna methylation
- gene expression
- low dose
- systemic sclerosis
- virtual reality
- patient reported