172 related articles for article (PubMed ID: 38390317)
1. Identification and validation of immune and cuproptosis - related genes for diabetic nephropathy by WGCNA and machine learning.
Chen Y; Liao L; Wang B; Wu Z
Front Immunol; 2024; 15():1332279. PubMed ID: 38390317
[TBL] [Abstract][Full Text] [Related]
2. Identifying C1QB, ITGAM, and ITGB2 as potential diagnostic candidate genes for diabetic nephropathy using bioinformatics analysis.
Hu Y; Yu Y; Dong H; Jiang W
PeerJ; 2023; 11():e15437. PubMed ID: 37250717
[TBL] [Abstract][Full Text] [Related]
3. Identification and validation of immune and oxidative stress-related diagnostic markers for diabetic nephropathy by WGCNA and machine learning.
Xu M; Zhou H; Hu P; Pan Y; Wang S; Liu L; Liu X
Front Immunol; 2023; 14():1084531. PubMed ID: 36911691
[TBL] [Abstract][Full Text] [Related]
4. Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments.
Zhong M; Zhu E; Li N; Gong L; Xu H; Zhong Y; Gong K; Jiang S; Wang X; Fei L; Tang C; Lei Y; Wang Z; Zheng Z
Front Endocrinol (Lausanne); 2023; 14():1134325. PubMed ID: 36960398
[TBL] [Abstract][Full Text] [Related]
5. Identification and validation of voltage-dependent anion channel 1-related genes and immune cell infiltration in diabetic nephropathy.
Lin J; Weng M; Zheng J; Nie K; Rao S; Zhuo Y; Wan J
J Diabetes Investig; 2024 Jan; 15(1):87-105. PubMed ID: 37737517
[TBL] [Abstract][Full Text] [Related]
6. Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease.
Zhang X; Chao P; Zhang L; Xu L; Cui X; Wang S; Wusiman M; Jiang H; Lu C
Front Immunol; 2023; 14():1030198. PubMed ID: 37063851
[TBL] [Abstract][Full Text] [Related]
7. Apoptosis and NETotic cell death affect diabetic nephropathy independently: An study integrative study encompassing bioinformatics, machine learning, and experimental validation.
Cai H; Zeng Y; Luo D; Shao Y; Liu M; Wu J; Gao X; Zheng J; Zhou L; Liu F
Genomics; 2024 Jul; 116(4):110879. PubMed ID: 38851464
[TBL] [Abstract][Full Text] [Related]
8. Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning.
Ouyang G; Wu Z; Liu Z; Pan G; Wang Y; Liu J; Guo J; Liu T; Huang G; Zeng Y; Wei Z; He S; Yuan G
Front Immunol; 2023; 14():1251750. PubMed ID: 37822923
[TBL] [Abstract][Full Text] [Related]
9. Identification and analysis of cellular senescence-associated signatures in diabetic kidney disease by integrated bioinformatics analysis and machine learning.
Luo Y; Zhang L; Zhao T
Front Endocrinol (Lausanne); 2023; 14():1193228. PubMed ID: 37396184
[TBL] [Abstract][Full Text] [Related]
10. Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice.
Zhao J; He K; Du H; Wei G; Wen Y; Wang J; Zhou X; Wang J
PeerJ; 2022; 10():e13932. PubMed ID: 36157062
[TBL] [Abstract][Full Text] [Related]
11. Screening and Identification of Hub Genes in the Development of Early Diabetic Kidney Disease Based on Weighted Gene Co-Expression Network Analysis.
Wei R; Qiao J; Cui D; Pan Q; Guo L
Front Endocrinol (Lausanne); 2022; 13():883658. PubMed ID: 35721731
[TBL] [Abstract][Full Text] [Related]
12. Identification of cuproptosis-related asthma diagnostic genes by WGCNA analysis and machine learning.
Wang F; Su Q; Li C
J Asthma; 2023 Nov; 60(11):2052-2063. PubMed ID: 37289763
[TBL] [Abstract][Full Text] [Related]
13. Diabetic kidney disease-predisposing proinflammatory and profibrotic genes identified by weighted gene co-expression network analysis (WGCNA).
Chen J; Luo SF; Yuan X; Wang M; Yu HJ; Zhang Z; Yang YY
J Cell Biochem; 2022 Feb; 123(2):481-492. PubMed ID: 34908186
[TBL] [Abstract][Full Text] [Related]
14. Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis.
Fu S; Cheng Y; Wang X; Huang J; Su S; Wu H; Yu J; Xu Z
Front Med (Lausanne); 2022; 9():918657. PubMed ID: 36250071
[TBL] [Abstract][Full Text] [Related]
15. Correlation Between Serum 25-Hydroxyvitamin D Levels in Albuminuria Progression of Diabetic Kidney Disease and Underlying Mechanisms By Bioinformatics Analysis.
Huang B; Wen W; Ye S
Front Endocrinol (Lausanne); 2022; 13():880930. PubMed ID: 35634488
[TBL] [Abstract][Full Text] [Related]
16. Identification of a novel immune landscape signature as effective diagnostic markers related to immune cell infiltration in diabetic nephropathy.
Zhou H; Mu L; Yang Z; Shi Y
Front Immunol; 2023; 14():1113212. PubMed ID: 36969169
[TBL] [Abstract][Full Text] [Related]
17. Identification of ULK1 as a novel mitophagy-related gene in diabetic nephropathy.
Yang YY; Gao ZX; Mao ZH; Liu DW; Liu ZS; Wu P
Front Endocrinol (Lausanne); 2022; 13():1079465. PubMed ID: 36743936
[TBL] [Abstract][Full Text] [Related]
18. GBP2 promotes M1 macrophage polarization by activating the notch1 signaling pathway in diabetic nephropathy.
Li X; Liu J; Zeng M; Yang K; Zhang S; Liu Y; Yin X; Zhao C; Wang W; Xiao L
Front Immunol; 2023; 14():1127612. PubMed ID: 37622120
[TBL] [Abstract][Full Text] [Related]
19. Identification and analysis of diverse cell death patterns in diabetic kidney disease using microarray-based transcriptome profiling and single-nucleus RNA sequencing.
Luo Y; Liu L; Zhang C
Comput Biol Med; 2024 Feb; 169():107780. PubMed ID: 38104515
[TBL] [Abstract][Full Text] [Related]
20. Identification and functional analysis of the hub Ferroptosis-Related gene EZH2 in diabetic kidney disease.
Wang H; Wang J; Ran Q; Leng Y; Liu T; Xiong Z; Zou D; Yang W
Int Immunopharmacol; 2024 May; 133():112138. PubMed ID: 38678670
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]