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.
349 related articles for article (PubMed ID: 32597946)
1. Identification of feature autophagy-related genes in patients with acute myocardial infarction based on bioinformatics analyses. Du Y; Zhao E; Zhang Y Biosci Rep; 2020 Jul; 40(7):. PubMed ID: 32597946 [TBL] [Abstract][Full Text] [Related]
2. Identification of Featured Metabolism-Related Genes in Patients with Acute Myocardial Infarction. Xie H; Zha E; Zhang Y Dis Markers; 2020; 2020():8880004. PubMed ID: 33354250 [TBL] [Abstract][Full Text] [Related]
3. Thrombomodulin as a potential diagnostic marker of acute myocardial infarction and correlation with immune infiltration: Comprehensive analysis based on multiple machine learning. Liu G; Huang L; Lv X; Guan Y; Li L Transpl Immunol; 2024 Aug; 85():102070. PubMed ID: 38839020 [TBL] [Abstract][Full Text] [Related]
4. Identification of risk genes associated with myocardial infarction based on the recursive feature elimination algorithm and support vector machine classifier. Yang X Mol Med Rep; 2018 Jan; 17(1):1555-1560. PubMed ID: 29138828 [TBL] [Abstract][Full Text] [Related]
5. ACSL1, CH25H, GPCPD1, and PLA2G12A as the potential lipid-related diagnostic biomarkers of acute myocardial infarction. Liu ZY; Liu F; Cao Y; Peng SL; Pan HW; Hong XQ; Zheng PF Aging (Albany NY); 2023 Feb; 15(5):1394-1411. PubMed ID: 36863716 [TBL] [Abstract][Full Text] [Related]
6. Predicting Diagnostic Gene Biomarkers Associated With Immune Infiltration in Patients With Acute Myocardial Infarction. Zhao E; Xie H; Zhang Y Front Cardiovasc Med; 2020; 7():586871. PubMed ID: 33195475 [No Abstract] [Full Text] [Related]
7. Identification of potential biomarkers and immune-related pathways related to immune infiltration in patients with acute myocardial infarction. Lin Z; Xu H; Chen Y; Zhang X; Yang J Transpl Immunol; 2022 Oct; 74():101652. PubMed ID: 35764238 [TBL] [Abstract][Full Text] [Related]
8. Analysis of the prognostic value of mitochondria-related genes in patients with acute myocardial infarction. Qiu J; Gu Y BMC Cardiovasc Disord; 2024 Aug; 24(1):408. PubMed ID: 39103773 [TBL] [Abstract][Full Text] [Related]
9. Identification and validation of senescence-related genes in circulating endothelial cells of patients with acute myocardial infarction. Xiang J; Shen J; Zhang L; Tang B Front Cardiovasc Med; 2022; 9():1057985. PubMed ID: 36582740 [TBL] [Abstract][Full Text] [Related]
10. Identification of Biomarkers for Early Diagnosis of Acute Myocardial Infarction. Ge WH; Lin Y; Li S; Zong X; Ge ZC J Cell Biochem; 2018 Jan; 119(1):650-658. PubMed ID: 28636181 [TBL] [Abstract][Full Text] [Related]
11. Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods. Tuo Y; An N; Zhang M Mol Med Rep; 2018 Mar; 17(3):4281-4290. PubMed ID: 29328377 [TBL] [Abstract][Full Text] [Related]
12. Risk gene identification and support vector machine learning to construct an early diagnosis model of myocardial infarction. Fang HZ; Hu DL; Li Q; Tu S Mol Med Rep; 2020 Sep; 22(3):1775-1782. PubMed ID: 32705275 [TBL] [Abstract][Full Text] [Related]
13. A machine learning-based diagnostic model for myocardial infarction patients: Analysis of neutrophil extracellular traps-related genes and eQTL Mendelian randomization. Sheng M; Cui X Medicine (Baltimore); 2024 Mar; 103(12):e37363. PubMed ID: 38518057 [TBL] [Abstract][Full Text] [Related]
14. Identification of hub glycolysis-related genes in acute myocardial infarction and their correlation with immune infiltration using bioinformatics analysis. Zhang X; Zhang L; Gao Y; Liu Z; Gong K BMC Cardiovasc Disord; 2024 Jul; 24(1):349. PubMed ID: 38987688 [TBL] [Abstract][Full Text] [Related]
15. S100A9 and SOCS3 as diagnostic biomarkers of acute myocardial infarction and their association with immune infiltration. Lin ZL; Liu YC; Gao YL; Chen XS; Wang CL; Shou ST; Chai YF Genes Genet Syst; 2022 Jul; 97(2):67-79. PubMed ID: 35675985 [TBL] [Abstract][Full Text] [Related]
16. Application of angiogenesis-related genes associated with immune infiltration in the molecular typing and diagnosis of acute myocardial infarction. Liu G; Liao W; Lv X; Zhu M; Long X; Xie J Aging (Albany NY); 2024 Jun; 16(12):10402-10423. PubMed ID: 38885062 [TBL] [Abstract][Full Text] [Related]
17. Potential role of a three-gene signature in predicting diagnosis in patients with myocardial infarction. Yao Y; Zhao J; Zhou X; Hu J; Wang Y Bioengineered; 2021 Dec; 12(1):2734-2749. PubMed ID: 34130601 [TBL] [Abstract][Full Text] [Related]
18. Differential expression profiles of long non-coding RNAs as potential biomarkers for the early diagnosis of acute myocardial infarction. Li L; Cong Y; Gao X; Wang Y; Lin P Oncotarget; 2017 Oct; 8(51):88613-88621. PubMed ID: 29179461 [TBL] [Abstract][Full Text] [Related]
19. Construction of Novel Gene Signature-Based Predictive Model for the Diagnosis of Acute Myocardial Infarction by Combining Random Forest With Artificial Neural Network. Wu Y; Chen H; Li L; Zhang L; Dai K; Wen T; Peng J; Peng X; Zheng Z; Jiang T; Xiong W Front Cardiovasc Med; 2022; 9():876543. PubMed ID: 35694667 [TBL] [Abstract][Full Text] [Related]
20. Establishment of a SVM classifier to predict recurrence of ovarian cancer. Zhou J; Li L; Wang L; Li X; Xing H; Cheng L Mol Med Rep; 2018 Oct; 18(4):3589-3598. PubMed ID: 30106117 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]