218 related articles for article (PubMed ID: 36737814)
1. Incorporating knowledge of disease-defining hub genes and regulatory network into a machine learning-based model for predicting treatment response in lupus nephritis after the first renal flare.
Lee DJ; Tsai PH; Chen CC; Dai YH
J Transl Med; 2023 Feb; 21(1):76. PubMed ID: 36737814
[TBL] [Abstract][Full Text] [Related]
2. Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis.
Wang L; Yang Z; Yu H; Lin W; Wu R; Yang H; Yang K
Front Immunol; 2022; 13():839197. PubMed ID: 36532018
[TBL] [Abstract][Full Text] [Related]
3. Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets.
Zeng H; Zhuang Y; Yan X; He X; Qiu Q; Liu W; Zhang Y
Lupus Sci Med; 2024 Apr; 11(1):. PubMed ID: 38637124
[TBL] [Abstract][Full Text] [Related]
4. Identification of driver genes in lupus nephritis based on comprehensive bioinformatics and machine learning.
Wang Z; Hu D; Pei G; Zeng R; Yao Y
Front Immunol; 2023; 14():1288699. PubMed ID: 38130724
[TBL] [Abstract][Full Text] [Related]
5. Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning.
Liu C; Zhou Y; Zhou Y; Tang X; Tang L; Wang J
Comput Biol Med; 2023 Jan; 152():106388. PubMed ID: 36470144
[TBL] [Abstract][Full Text] [Related]
6. Analysis of m6A-regulated genes and subtype classification in lupus nephritis.
Li D; Li Y; Zhu K; Yuan Y; He Z; Sun Q; Jin M
BMC Nephrol; 2024 Apr; 25(1):119. PubMed ID: 38570749
[TBL] [Abstract][Full Text] [Related]
7. Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning.
Li H; Zhou L; Zhou W; Zhang X; Shang J; Feng X; Yu L; Fan J; Ren J; Zhang R; Duan X
BMC Rheumatol; 2023 Dec; 7(1):44. PubMed ID: 38044432
[TBL] [Abstract][Full Text] [Related]
8. Immune cell infiltration characteristics and related core genes in lupus nephritis: results from bioinformatic analysis.
Cao Y; Tang W; Tang W
BMC Immunol; 2019 Oct; 20(1):37. PubMed ID: 31638917
[TBL] [Abstract][Full Text] [Related]
9. Immune gene expression in kidney biopsies of lupus nephritis patients at diagnosis and at renal flare.
Mejia-Vilet JM; Parikh SV; Song H; Fadda P; Shapiro JP; Ayoub I; Yu L; Zhang J; Uribe-Uribe N; Rovin BH
Nephrol Dial Transplant; 2019 Jul; 34(7):1197-1206. PubMed ID: 29800348
[TBL] [Abstract][Full Text] [Related]
10. Identification of hub ferroptosis-related genes and immune infiltration in lupus nephritis using bioinformatics.
Hu W; Chen X
Sci Rep; 2022 Nov; 12(1):18826. PubMed ID: 36335193
[TBL] [Abstract][Full Text] [Related]
11. Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis.
Wang DC; Xu WD; Wang SN; Wang X; Leng W; Fu L; Liu XY; Qin Z; Huang AF
Inflamm Res; 2023 Jun; 72(6):1315-1324. PubMed ID: 37300586
[TBL] [Abstract][Full Text] [Related]
12. Machine Learning for Prediction and Risk Stratification of Lupus Nephritis Renal Flare.
Chen Y; Huang S; Chen T; Liang D; Yang J; Zeng C; Li X; Xie G; Liu Z
Am J Nephrol; 2021; 52(2):152-160. PubMed ID: 33744876
[TBL] [Abstract][Full Text] [Related]
13. Cross-species transcriptome analysis for early detection and specific therapeutic targeting of human lupus nephritis.
Frangou E; Garantziotis P; Grigoriou M; Banos A; Nikolopoulos D; Pieta A; Doumas SA; Fanouriakis A; Hatzioannou A; Manolakou T; Alissafi T; Verginis P; Athanasiadis E; Dermitzakis E; Bertsias G; Filia A; Boumpas DT
Ann Rheum Dis; 2022 Oct; 81(10):1409-1419. PubMed ID: 35906002
[TBL] [Abstract][Full Text] [Related]
14. Comprehensive analysis of lactate-related gene profiles and immune characteristics in lupus nephritis.
Sun Z; Gao Z; Xiang M; Feng Y; Wang J; Xu J; Wang Y; Liang J
Front Immunol; 2024; 15():1329009. PubMed ID: 38455045
[TBL] [Abstract][Full Text] [Related]
15. Bioinformatic analysis reveals that the OAS family may play an important role in lupus nephritis.
Cao Y; Mi X; Wang Z; Zhang D; Tang W
J Natl Med Assoc; 2020 Dec; 112(6):567-577. PubMed ID: 32622555
[TBL] [Abstract][Full Text] [Related]
16. Machine learning models predicts risk of proliferative lupus nephritis.
Yang P; Liu Z; Lu F; Sha Y; Li P; Zheng Q; Wang K; Zhou X; Zeng X; Wu Y
Front Immunol; 2024; 15():1413569. PubMed ID: 38919623
[TBL] [Abstract][Full Text] [Related]
17. Glomerular Expression of S100A8 in Lupus Nephritis: An Integrated Bioinformatics Analysis.
Qijiao W; Zhihan C; Makota P; Qing Y; Fei G; Zhihong W; He L
Front Immunol; 2022; 13():843576. PubMed ID: 35572531
[TBL] [Abstract][Full Text] [Related]
18. Revealing common differential mRNAs, signaling pathways, and immune cells in blood, glomeruli, and tubulointerstitium of lupus nephritis patients based on transcriptomic data.
Zhao H; Zheng D
Ren Fail; 2023 Dec; 45(1):2215344. PubMed ID: 37334926
[TBL] [Abstract][Full Text] [Related]
19. A network-based approach reveals long non-coding RNAs associated with disease activity in lupus nephritis: key pathways for flare and potential biomarkers to be used as liquid biopsies.
Sentis G; Loukogiannaki C; Malissovas N; Nikolopoulos D; Manolakou T; Flouda S; Grigoriou M; Banos A; Boumpas DT; Filia A
Front Immunol; 2023; 14():1203848. PubMed ID: 37475860
[TBL] [Abstract][Full Text] [Related]
20. Renal tubular gen e biomarkers identification based on immune infiltrates in focal segmental glomerulosclerosis.
Bai J; Pu X; Zhang Y; Dai E
Ren Fail; 2022 Dec; 44(1):966-986. PubMed ID: 35713363
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
