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.
203 related articles for article (PubMed ID: 36699999)
1. Identification of the potential biomarkers associated with circadian rhythms in heart failure. Sun Q; Zhao J; Liu L; Wang X; Gu X PeerJ; 2023; 11():e14734. PubMed ID: 36699999 [TBL] [Abstract][Full Text] [Related]
2. Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis. Kolur V; Vastrad B; Vastrad C; Kotturshetti S; Tengli A BMC Cardiovasc Disord; 2021 Jul; 21(1):329. PubMed ID: 34218797 [TBL] [Abstract][Full Text] [Related]
3. Identification of biomarkers, pathways, and potential therapeutic targets for heart failure using next-generation sequencing data and bioinformatics analysis. Ganekal P; Vastrad B; Vastrad C; Kotrashetti S Ther Adv Cardiovasc Dis; 2023; 17():17539447231168471. PubMed ID: 37092838 [TBL] [Abstract][Full Text] [Related]
4. Identification of circadian rhythm-related gene classification patterns and immune infiltration analysis in heart failure based on machine learning. Wang X; Rao J; Zhang L; Liu X; Zhang Y Heliyon; 2024 Mar; 10(6):e27049. PubMed ID: 38509983 [TBL] [Abstract][Full Text] [Related]
5. Identification of vital modules and genes associated with heart failure based on weighted gene coexpression network analysis. Bian W; Wang Z; Li X; Jiang XX; Zhang H; Liu Z; Zhang DM ESC Heart Fail; 2022 Apr; 9(2):1370-1379. PubMed ID: 35128826 [TBL] [Abstract][Full Text] [Related]
6. Regulatory mechanism of fibrosis-related genes in patients with heart failure. Tao Y; Gao C; Qian D; Cao D; Han L; Yang L Front Genet; 2022; 13():1032572. PubMed ID: 36324504 [No Abstract] [Full Text] [Related]
7. Identification of hub genes and transcription factor regulatory network for heart failure using RNA-seq data and robust rank aggregation analysis. Tu D; Ma C; Zeng Z; Xu Q; Guo Z; Song X; Zhao X Front Cardiovasc Med; 2022; 9():916429. PubMed ID: 36386304 [TBL] [Abstract][Full Text] [Related]
8. Identification of Biomarkers for Ovarian Cancer Diagnosis and Prognosis by Bioinformatics Analysis and q-PCR Validation. Zheng Q; Ye H; Zhong H; Wang M Ann Clin Lab Sci; 2022 Nov; 52(6):967-975. PubMed ID: 36564071 [TBL] [Abstract][Full Text] [Related]
9. Screening and identification of potential hub genes and immune cell infiltration in the synovial tissue of rheumatoid arthritis by bioinformatic approach. Feng ZW; Tang YC; Sheng XY; Wang SH; Wang YB; Liu ZC; Liu JM; Geng B; Xia YY Heliyon; 2023 Jan; 9(1):e12799. PubMed ID: 36699262 [TBL] [Abstract][Full Text] [Related]
10. Bioinformatics Analysis and Identification of Genes and Pathways in Ischemic Cardiomyopathy. Cao J; Liu Z; Liu J; Li C; Zhang G; Shi R Int J Gen Med; 2021; 14():5927-5937. PubMed ID: 34584445 [TBL] [Abstract][Full Text] [Related]
11. Identification of novel biomarkers involved in doxorubicin-induced acute and chronic cardiotoxicity, respectively, by integrated bioinformatics. Qian H; Qian Y; Liu Y; Cao J; Wang Y; Yang A; Zhao W; Lu Y; Liu H; Zhu W Front Cardiovasc Med; 2022; 9():996809. PubMed ID: 36712272 [TBL] [Abstract][Full Text] [Related]
12. Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm. Chen H; Jiang R; Huang W; Chen K; Zeng R; Wu H; Yang Q; Guo K; Li J; Wei R; Liao S; Tse HF; Sha W; Zhuo Z Front Cardiovasc Med; 2022; 9():993142. PubMed ID: 36304554 [TBL] [Abstract][Full Text] [Related]
13. Exploration of the pathogenesis of Sjögren's syndrome via DNA methylation and transcriptome analyses. Du Y; Li J; Wu J; Zeng F; He C Clin Rheumatol; 2022 Sep; 41(9):2765-2777. PubMed ID: 35562622 [TBL] [Abstract][Full Text] [Related]
14. 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]
15. Identification of hub genes associated with oxidative stress in heart failure and their correlation with immune infiltration using bioinformatics analysis. Gu J; Zhang LN; Gu X; Zhu Y PeerJ; 2023; 11():e15893. PubMed ID: 37609434 [TBL] [Abstract][Full Text] [Related]
16. Identification of nondiabetic heart failure-associated genes by bioinformatics approaches in patients with dilated ischemic cardiomyopathy. Yu A; Zhang J; Liu H; Liu B; Meng L Exp Ther Med; 2016 Jun; 11(6):2602-2608. PubMed ID: 27284354 [TBL] [Abstract][Full Text] [Related]
17. Integrating scRNA-seq to explore novel macrophage infiltration-associated biomarkers for diagnosis of heart failure. Li S; Ge T; Xu X; Xie L; Song S; Li R; Li H; Tong J BMC Cardiovasc Disord; 2023 Nov; 23(1):560. PubMed ID: 37974098 [TBL] [Abstract][Full Text] [Related]
18. Integrated microarray analysis to identify potential biomarkers and therapeutic targets in dilated cardiomyopathy. Zhang H; Huo J; Jiang W; Shan Q Mol Med Rep; 2020 Aug; 22(2):915-925. PubMed ID: 32626989 [TBL] [Abstract][Full Text] [Related]
19. Integrative analysis of potential biomarkers and immune cell infiltration in Parkinson's disease. Chen X; Cao W; Zhuang Y; Chen S; Li X Brain Res Bull; 2021 Dec; 177():53-63. PubMed ID: 34536521 [TBL] [Abstract][Full Text] [Related]
20. An integrative bioinformatics analysis of microarray data for identifying hub genes as diagnostic biomarkers of preeclampsia. Liu K; Fu Q; Liu Y; Wang C Biosci Rep; 2019 Sep; 39(9):. PubMed ID: 31416885 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]