These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
217 related articles for article (PubMed ID: 36659918)
1. Network-based identification and prioritization of key transcriptional factors of diabetic kidney disease. Ahmed I; Ziab M; Da'as S; Hasan W; Jeya SP; Aliyev E; Nisar S; Bhat AA; Fakhro KA; Alshabeeb Akil AS Comput Struct Biotechnol J; 2023; 21():716-730. PubMed ID: 36659918 [TBL] [Abstract][Full Text] [Related]
2. Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis. Wang L; Su J; Liu Z; Ding S; Li Y; Hou B; Hu Y; Dong Z; Tang J; Liu H; Liu W BioData Min; 2024 Jul; 17(1):20. PubMed ID: 38951833 [TBL] [Abstract][Full Text] [Related]
3. Identification of copper-related biomarkers and potential molecule mechanism in diabetic nephropathy. Ming J; Sana SRGL; Deng X Front Endocrinol (Lausanne); 2022; 13():978601. PubMed ID: 36329882 [TBL] [Abstract][Full Text] [Related]
4. Seven basement membrane-specific expressed genes are considered potential biomarkers for the diagnosis and treatment of diabetic nephropathy. Gui H; Chen X; Ye L; Ma H Acta Diabetol; 2023 Apr; 60(4):493-505. PubMed ID: 36627452 [TBL] [Abstract][Full Text] [Related]
5. Integrated Bioinformatics and Clinical Correlation Analysis of Key Genes, Pathways, and Potential Therapeutic Agents Related to Diabetic Nephropathy. Chen S; Chen L; Jiang H Dis Markers; 2022; 2022():9204201. PubMed ID: 35637650 [TBL] [Abstract][Full Text] [Related]
6. Identification of PDK4 as Hub Gene for Diabetic Nephropathy Using Co-Expression Network Analysis. Han Y; Jin L; Wang L; Wei L; Tu C Kidney Blood Press Res; 2023; 48(1):522-534. PubMed ID: 37385224 [TBL] [Abstract][Full Text] [Related]
7. 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]
8. Identification of Lipotoxicity-Related Biomarkers in Diabetic Nephropathy Based on Bioinformatic Analysis. Nie H; Yang H; Cheng L; Yu J J Diabetes Res; 2024; 2024():5550812. PubMed ID: 38774257 [No Abstract] [Full Text] [Related]
9. Construction of a TF-miRNA-mRNA Regulatory Network for Diabetic Nephropathy. Dong F; Zheng L; Yang G Arch Esp Urol; 2024 Jan; 77(1):104-112. PubMed ID: 38374020 [TBL] [Abstract][Full Text] [Related]
10. Identification of key genes and biological regulatory mechanisms in diabetic nephropathy: Meta-analysis of gene expression datasets. Hojjati F; Roointan A; Gholaminejad A; Eshraghi Y; Gheisari Y Nefrologia (Engl Ed); 2023; 43(5):575-586. PubMed ID: 36681521 [TBL] [Abstract][Full Text] [Related]
11. Network pharmacology combined with Mendelian randomization analysis to identify the key targets of renin-angiotensin-aldosterone system inhibitors in the treatment of diabetic nephropathy. Zhou D; Zhou T; Tang S; Li Q; Li W; Gan G; Li M; Chen Q Front Endocrinol (Lausanne); 2024; 15():1354950. PubMed ID: 38332893 [TBL] [Abstract][Full Text] [Related]
12. Screening and Identification of Differentially Expressed Genes Between Diabetic Nephropathy Glomerular and Normal Glomerular via Bioinformatics Technology. Du J; Yang J; Meng L Comb Chem High Throughput Screen; 2021; 24(5):645-655. PubMed ID: 32954999 [TBL] [Abstract][Full Text] [Related]
13. Application of weighted gene co-expression network analysis to identify novel key genes in diabetic nephropathy. Wang Z; Chen X; Li C; Tang W J Diabetes Investig; 2022 Jan; 13(1):112-124. PubMed ID: 34245661 [TBL] [Abstract][Full Text] [Related]
14. Identification of necroptosis-related features in diabetic nephropathy and analysis of their immune microenvironent and inflammatory response. Hu K; He R; Xu M; Zhang D; Han G; Han S; Xiao L; Xia P; Ling J; Wu T; Li F; Sheng Y; Zhang J; Yu P Front Cell Dev Biol; 2023; 11():1271145. PubMed ID: 38020922 [No Abstract] [Full Text] [Related]
15. Identification of key biomarkers in diabetic nephropathy via bioinformatic analysis. Zeng M; Liu J; Yang W; Zhang S; Liu F; Dong Z; Peng Y; Sun L; Xiao L J Cell Biochem; 2019 May; 120(5):8676-8688. PubMed ID: 30485525 [TBL] [Abstract][Full Text] [Related]
16. Integrative analyses of biomarkers and pathways for diabetic nephropathy. Li B; Zhao X; Xie W; Hong Z; Zhang Y Front Genet; 2023; 14():1128136. PubMed ID: 37113991 [No Abstract] [Full Text] [Related]
17. Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm. Gholaminejad A; Fathalipour M; Roointan A BMC Nephrol; 2021 Jul; 22(1):245. PubMed ID: 34215202 [TBL] [Abstract][Full Text] [Related]
18. Identification and validation of P4HB as a novel autophagy-related biomarker in diabetic nephropathy. Bai F; Yu K; Yang Y; Zhang Y; Ding L; An X; Feng F; Sun N; Fan J; Liu L; Yang H; Yang X Front Genet; 2022; 13():965816. PubMed ID: 36226178 [TBL] [Abstract][Full Text] [Related]
19. 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]
20. Biomarkers of Arginine Methylation in Diabetic Nephropathy: Novel Insights from Bioinformatics Analysis. Guan Y; Yin X; Wang L; Diao Z; Huang H; Wang X Diabetes Metab Syndr Obes; 2024; 17():3399-3418. PubMed ID: 39290792 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]