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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

318 related articles for article (PubMed ID: 36960398)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. Identification of potential key lipid metabolism-related genes involved in tubular injury in diabetic kidney disease by bioinformatics analysis.
    Fan Y; He J; Shi L; Zhang M; Chen Y; Xu L; Han N; Jiang Y
    Acta Diabetol; 2024 Aug; 61(8):1053-1068. PubMed ID: 38691241
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Integrin subunit beta 6 is a potential diagnostic marker for acute kidney injury in patients with diabetic kidney disease: a single cell sequencing data analysis.
    Yao C; Li Z; Su H; Sun K; Liu Q; Zhang Y; Zhu L; Jiang F; Fan Y; Shou S; Wu H; Jin H
    Ren Fail; 2024 Dec; 46(2):2409348. PubMed ID: 39356055
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identification of Markers for Diagnosis and Treatment of Diabetic Kidney Disease Based on the Ferroptosis and Immune.
    Ma J; Li C; Liu T; Zhang L; Wen X; Liu X; Fan W
    Oxid Med Cell Longev; 2022; 2022():9957172. PubMed ID: 36466094
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Identification of common biomarkers in diabetic kidney disease and cognitive dysfunction using machine learning algorithms.
    Peng J; Yang S; Zhou C; Qin C; Fang K; Tan Y; Da J; Zhang J; Zha Y
    Sci Rep; 2024 Sep; 14(1):22057. PubMed ID: 39333211
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Identification of Novel Key Molecular Signatures in the Pathogenesis of Experimental Diabetic Kidney Disease.
    Diao M; Wu Y; Yang J; Liu C; Xu J; Jin H; Wang J; Zhang J; Gao F; Jin C; Tian H; Xu J; Ou Q; Li Y; Xu G; Lu L
    Front Endocrinol (Lausanne); 2022; 13():843721. PubMed ID: 35432190
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Identification of novel key genes and potential candidate small molecule drugs in diabetic kidney disease using comprehensive bioinformatics analysis.
    Li B; Ye S; Fan Y; Lin Y; Li S; Peng H; Diao H; Chen W
    Front Genet; 2022; 13():934555. PubMed ID: 36035169
    [No Abstract]   [Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease.
    Zou Y; Zhao L; Zhang J; Wang Y; Wu Y; Ren H; Wang T; Zhang R; Wang J; Zhao Y; Qin C; Xu H; Li L; Chai Z; Cooper ME; Tong N; Liu F
    Ren Fail; 2022 Dec; 44(1):562-570. PubMed ID: 35373711
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Identification and Verification of Diagnostic Biomarkers for Glomerular Injury in Diabetic Nephropathy Based on Machine Learning Algorithms.
    Han H; Chen Y; Yang H; Cheng W; Zhang S; Liu Y; Liu Q; Liu D; Yang G; Li K
    Front Endocrinol (Lausanne); 2022; 13():876960. PubMed ID: 35663304
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Novel ferroptosis gene biomarkers and immune infiltration profiles in diabetic kidney disease via bioinformatics.
    Huang Y; Yuan X
    FASEB J; 2024 Jan; 38(2):e23421. PubMed ID: 38198194
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Predicting diagnostic gene biomarkers in patients with diabetic kidney disease based on weighted gene co expression network analysis and machine learning algorithms.
    Gao Q; Jin H; Xu W; Wang Y
    Medicine (Baltimore); 2023 Oct; 102(43):e35618. PubMed ID: 37904449
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Development of a clinical prediction model for diabetic kidney disease with glucose and lipid metabolism disorders based on machine learning and bioinformatics technology.
    Bi Z; Wang LJ; Lin YX; Zhang YY; Wang SH; Fang ZH
    Eur Rev Med Pharmacol Sci; 2024 Feb; 28(3):863-878. PubMed ID: 38375694
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 16.