Page 161 - 南京医科大学自然版
P. 161
第45卷第12期 严舒桐,刘 琪. 机器学习在类风湿性关节炎诊疗及并发症预测中的研究进展[J].
2025年12月 南京医科大学学报(自然科学版),2025,45(12):1834-1844 ·1843 ·
tol,2021,40(6):2211-2219 network in rheumatoid arthritis based on network pharma⁃
[44]GENTE K,FEISST M,MARX D,et al. Altered serum me⁃ cology,bioinformatics analysis,and experimental verifica⁃
tabolome as an indicator of paraneoplasia or concomitant tion[J]. Sci Rep,2024,14(1):6291
cancer in patients with rheumatic disease[J]. Ann [57]YE Z,YE B J,MING Z L,et al. Forecasting rheumatoid
Rheum Dis,2024,83(8):974-983 arthritis patient arrivals by including meteorological fac⁃
[45]CAI Y,DENG L C,YAO J. Analysis and identification of tors and air pollutants[J]. Sci Rep,2024,14(1):17840
ferroptosis ⁃ related diagnostic markers in rheumatoid ar⁃ [58]MENDOZA⁃PINTO C,SÁNCHEZ⁃TECUATL M,BERRA⁃
thritis[J]. Ann Med,2024,56(1):2397572 ROMANI R,et al. Machine learning in the prediction of
[46]ZHOU Y,LI X,NG L,et al. Identification of copper death⁃ treatment response in rheumatoid arthritis:a systematic
associated molecular clusters and immunological profiles review[J]. Semin Arthritis Rheum,2024,68:152501
in rheumatoid arthritis[J]. Front Immunol,2023,14: [59]KALWEIT M,BURDEN A M,BOEDECKER J,et al. Pa⁃
1103509 tient groups in rheumatoid arthritis identified by deep
[47]FU W C,WANG T B,LU Y H,et al. The role of lacty⁃ learning respond differently to biologic or targeted synthetic
lation in plasma cells and its impact on rheumatoid arthri⁃ DMARDs[J]. PLoS Comput Biol,2023,19(6):e1011073
tis pathogenesis:Insights from single⁃cell RNA sequenc⁃ [60]RIVELLESE F,SURACE A E A,GOLDMANN K,et al.
ing and machine learning[J]. Front Immunol,2024,15: Rituximab versus tocilizumab in rheumatoid arthritis:sy⁃
1453587 novial biopsy ⁃ based biomarker analysis of the phase 4
[48]YANG F,SHEN J Y,ZHAO Z M,et al. Unveiling the link R4RA randomized trial[J]. Nat Med,2022,28(6):1256-
between lactate metabolism and rheumatoid arthritis 1268
through integration of bioinformatics and machine learn⁃ [61]UKALOVIC D,LEEB B F,RINTELEN B,et al. Predic⁃
ing[J]. Sci Rep,2024,14(1):9166 tion of ineffectiveness of biological drugs using machine
[49]ADAMI G,FASSIO A,ROSSINI M,et al. Machine learn⁃ learning and explainable AI methods:data from the Aus⁃
ing to characterize bone biomarkers profile in rheumatoid trian Biological Registry BioReg[J]. Arthritis Res Ther,
arthritis[J]. Front Immunol,2023,14:1291727 2024,26(1):44
[50]WU X,SHUAI W,CHEN C,et al. Rapid screening for au⁃ [62]HAGEMAN I,MOL F,ATIQI S,et al. Novel DNA methy⁃
toimmune diseases using Fourier transform infrared spec⁃ lome biomarkers associated with adalimumab response in
troscopy and deep learning algorithms[J]. Front Immu⁃ rheumatoid arthritis patients[J]. Front Immunol,2023,
nol,2023,14:1328228 14:1303231
[51]LIN C M A,COOLES F A H,ISAACS J D. Precision medi⁃ [63]TAO W Y,CONCEPCION A N,VIANEN M,et al. Mul⁃
cine:the precision gap in rheumatic disease[J]. Nat Rev tiomics and machine learning accurately predict clinical
Rheumatol,2022,18(12):725-733 response to adalimumab and etanercept therapy in pa⁃
[52]LI G Q,CHEN H Y,SHEN J C,et al. Unveiling new thera⁃ tients with rheumatoid arthritis[J]. Arthritis Rheumatol,
peutic horizons in rheumatoid arthritis:an in⁃depth explo⁃ 2021,73(2):212-222
ration of circular RNAs derived from plasma exosomes[J]. [64]CHEN Y L,WANG Q,LIU H N,et al. The prognostic
J Orthop Surg Res,2025,20(1):109 value of whole⁃genome DNA methylation in response to
[53]PLANT D,MACIEJEWSKI M,SMITH S,et al. Profiling leflunomide in patients with rheumatoid arthritis[J].
of gene expression biomarkers as a classifier of methotrexate Front Immunol,2023,14:1173187
nonresponse in patients with rheumatoid arthritis[J]. [65]CHANG M J,FENG Q F,HAO J W,et al. Deciphering
Arthritis Rheumatol,2019,71(5):678-684 the molecular landscape of rheumatoid arthritis offers new
[54]DUQUESNE J,BOUGET V,COURNÈDE P H,et al. Ma⁃ insights into the stratified treatment for the condition[J].
chine learning identifies a profile of inadequate responder Front Immunol,2024,15:1391848
to methotrexate in rheumatoid arthritis[J]. Rheumatology [66]BAXTER N B,LIN C H,WALLACE B I,et al. Develop⁃
(Oxford),2023,62(7):2402-2409 ment of a machine learning model to predict the use of
[55]ZHANG Z Y,YE M T,GE Y S,et al. Eco⁃friendly nano⁃ surgery in patients with rheumatoid arthritis[J]. Arthritis
technology in rheumatoid arthritis:ANFIS ⁃ XGBoost en⁃ Care Res(Hoboken),2024,76(5):636-643
hanced layered nanomaterials[J]. Environ Res,2024,262 [67]HETLAND M L,STRANGFELD A,BONFANTI G,et al.
(Pt 1):119832 Machine learning prediction and explanatory models of se⁃
[56]JIANG J,HUANG M,ZHANG S S,et al. Identification of rious infections in patients with rheumatoid arthritis treated
Hedyotis diffusa Willd⁃specific mRNA⁃miRNA⁃lncRNA with tofacitinib[J]. Arthritis Res Ther,2024,26(1):153

