Article
Integrating genetic, epigenetic, and clinical signatures via machine learning for robust prediction of leflunomide response in rheumatoid arthritis: a multi-center validation study.
Frontiers in immunology - 1 Jan 2026
Chen Meng, Liu Haina, Jin Lei, Feng Xin, Dai Bingbing, Wang Fang, Wang Qiao, Chen Yulan, Yi Man, Jia Bowen, Dong Kangyi, Zhang Jintao, Fan Zhijun, Li Jiahui, Zhao Feng, Jia Yuanyuan, Wang Jianpeng, Liu Miao, Xu Jiayi, Fu Lingyu
Abstract excerpt
Objective: To develop and validate a machine learning(ML)-based integrated predictive model combining genetic, epigenetic, and clinical factors for predicting leflunomide (LEF) treatment response in rheumatoid arthritis (RA) patients. Methods: A total of 357 RA patients (231 in the model development cohort [MDC], 126 in the external validation cohort [EVC]) were recruited from multiple centers in China....
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