168 related articles for article (PubMed ID: 38783198)
1. Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning.
Jin N; Rong J; Chen X; Huang L; Ma H
BMC Cardiovasc Disord; 2024 May; 24(1):272. PubMed ID: 38783198
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Identification of Key Genes Involved in Acute Myocardial Infarction by Comparative Transcriptome Analysis.
Sheng X; Fan T; Jin X
Biomed Res Int; 2020; 2020():1470867. PubMed ID: 33083450
[TBL] [Abstract][Full Text] [Related]
4. Identification of co-expressed central genes and transcription factors in acute myocardial infarction and diabetic nephropathy.
Li B; Zhao X; Xie W; Hong Z; Cao Y; Zhang Y; Ding Y
BMC Med Genomics; 2024 May; 17(1):134. PubMed ID: 38764052
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Identification and validation of potential diagnostic signature and immune cell infiltration for HIRI based on cuproptosis-related genes through bioinformatics analysis and machine learning.
Xiao F; Huang G; Yuan G; Li S; Wang Y; Tan Z; Liu Z; Tomlinson S; He S; Ouyang G; Zeng Y
Front Immunol; 2024; 15():1372441. PubMed ID: 38690269
[TBL] [Abstract][Full Text] [Related]
7. Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics Analysis.
Chen DQ; Kong XS; Shen XB; Huang MZ; Zheng JP; Sun J; Xu SH
Cardiovasc Ther; 2019; 2019():8490707. PubMed ID: 31772617
[TBL] [Abstract][Full Text] [Related]
8. Identification and analysis of key genes associated with acute myocardial infarction by integrated bioinformatics methods.
Guo S; Wu J; Zhou W; Liu X; Liu Y; Zhang J; Jia S; Li J; Wang H
Medicine (Baltimore); 2021 Apr; 100(15):e25553. PubMed ID: 33847684
[TBL] [Abstract][Full Text] [Related]
9. Identifying patterns of immune related cells and genes in the peripheral blood of acute myocardial infarction patients using a small cohort.
Zheng PF; Zou QC; Chen LZ; Liu P; Liu ZY; Pan HW
J Transl Med; 2022 Jul; 20(1):321. PubMed ID: 35864510
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Identification of important genes related to ferroptosis and hypoxia in acute myocardial infarction based on WGCNA.
Liu K; Chen S; Lu R
Bioengineered; 2021 Dec; 12(1):7950-7963. PubMed ID: 34565282
[TBL] [Abstract][Full Text] [Related]
12. Machine learning-based mRNA signature in early acute myocardial infarction patients: the perspective toward immunological, predictive, and personalized.
Pan HH; Yuan N; He LY; Sheng JL; Hu HL; Zhai CL
Funct Integr Genomics; 2023 May; 23(2):160. PubMed ID: 37178159
[TBL] [Abstract][Full Text] [Related]
13. Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.
Wu YK; Liu CD; Liu C; Wu J; Xie ZG
Front Immunol; 2024; 15():1387311. PubMed ID: 38711508
[TBL] [Abstract][Full Text] [Related]
14. Identification and Characterization of Necroptosis-Related Differentially Expressed Genes in Acute Myocardial Infarction: Insights into Immune-Related Pathways and Protein-Protein Interactions.
Zha Y; Zhu T; Li T
Cell Mol Biol (Noisy-le-grand); 2023 May; 69(5):192-196. PubMed ID: 37571880
[TBL] [Abstract][Full Text] [Related]
15. Integrated Bioinformatics-Based Analysis of Hub Genes and the Mechanism of Immune Infiltration Associated With Acute Myocardial Infarction.
Wu Y; Jiang T; Hua J; Xiong Z; Chen H; Li L; Peng J; Xiong W
Front Cardiovasc Med; 2022; 9():831605. PubMed ID: 35463752
[TBL] [Abstract][Full Text] [Related]
16. Transcriptome from circulating cells suggests dysregulated pathways associated with long-term recurrent events following first-time myocardial infarction.
Suresh R; Li X; Chiriac A; Goel K; Terzic A; Perez-Terzic C; Nelson TJ
J Mol Cell Cardiol; 2014 Sep; 74():13-21. PubMed ID: 24801707
[TBL] [Abstract][Full Text] [Related]
17. Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients.
Zhang L; Liu Y; Wang K; Ou X; Zhou J; Zhang H; Huang M; Du Z; Qiang S
J Transl Med; 2023 Oct; 21(1):761. PubMed ID: 37891664
[TBL] [Abstract][Full Text] [Related]
18. T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis
Chi H; Zhao S; Yang J; Gao X; Peng G; Zhang J; Xie X; Song G; Xu K; Xia Z; Chen S; Zhao J
Front Immunol; 2023; 14():1137025. PubMed ID: 37006257
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Uncovering potential diagnostic biomarkers of acute myocardial infarction based on machine learning and analyzing its relationship with immune cells.
Kang L; Zhao Q; Jiang K; Yu X; Chao H; Yin L; Wang Y
BMC Cardiovasc Disord; 2023 Jan; 23(1):2. PubMed ID: 36600215
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]