183 related articles for article (PubMed ID: 37178159)
1. 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]
2. Genetic and immunological insights into COVID-19 with acute myocardial infarction: integrated analysis of mendelian randomization, transcriptomics, and clinical samples.
Zheng Z; Zhou Y; Song Y; Ying P; Tan X
Front Immunol; 2023; 14():1286087. PubMed ID: 38022594
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. Identification and validation of diagnostic biomarkers of coronary artery disease progression in type 1 diabetes via integrated computational and bioinformatics strategies.
Zhou Y; Liu C; Zhang Z; Chen J; Zhao D; Li L; Tong M; Zhang G
Comput Biol Med; 2023 Jun; 159():106940. PubMed ID: 37075605
[TBL] [Abstract][Full Text] [Related]
6. Identification of key biomarkers for predicting CAD progression in inflammatory bowel disease via machine-learning and bioinformatics strategies.
Tang X; Zhou Y; Chen Z; Liu C; Wu Z; Zhou Y; Zhang F; Lu X; Tang L
J Cell Mol Med; 2024 Mar; 28(6):e18175. PubMed ID: 38451044
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Comprehensive bioinformatics analytics and
Zhou S; Wang L; Huang X; Wang T; Tang Y; Liu Y; Xu M
Aging (Albany NY); 2024 May; 16(9):8361-8377. PubMed ID: 38713173
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Identification of Immune-Related Genes in Patients with Acute Myocardial Infarction Using Machine Learning Methods.
Zhu X; Yin T; Zhang T; Zhu Q; Lu X; Wang L; Liao S; Yao W; Zhou Y; Zhang H; Li X
J Inflamm Res; 2022; 15():3305-3321. PubMed ID: 35692951
[TBL] [Abstract][Full Text] [Related]
11. PRKAR1A and SDCBP Serve as Potential Predictors of Heart Failure Following Acute Myocardial Infarction.
Chen Q; Su L; Liu C; Gao F; Chen H; Yin Q; Li S
Front Immunol; 2022; 13():878876. PubMed ID: 35592331
[TBL] [Abstract][Full Text] [Related]
12. Potential biomarkers of acute myocardial infarction based on weighted gene co-expression network analysis.
Liu Z; Ma C; Gu J; Yu M
Biomed Eng Online; 2019 Jan; 18(1):9. PubMed ID: 30683112
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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 Jun; 85():102070. PubMed ID: 38839020
[TBL] [Abstract][Full Text] [Related]
15. Detecting early-warning biomarkers associated with heart-exosome genetic-signature for acute myocardial infarction: A source-tracking study of exosome.
Jin X; Xu W; Wu Q; Huang C; Song Y; Lian J
J Cell Mol Med; 2024 Apr; 28(8):e18334. PubMed ID: 38661439
[TBL] [Abstract][Full Text] [Related]
16. Identification of monocyte-associated genes as predictive biomarkers of heart failure after acute myocardial infarction.
Chen Q; Yin Q; Song J; Liu C; Chen H; Li S
BMC Med Genomics; 2021 Feb; 14(1):44. PubMed ID: 33563285
[TBL] [Abstract][Full Text] [Related]
17. Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction.
Wang S; Li J; Sun L; Cai J; Wang S; Zeng L; Sun S
BMC Med Inform Decis Mak; 2021 Nov; 21(1):301. PubMed ID: 34724938
[TBL] [Abstract][Full Text] [Related]
18. 5mC modification patterns provide novel direction for early acute myocardial infarction detection and personalized therapy.
Guo Y; Jiang H; Wang J; Li P; Zeng X; Zhang T; Feng J; Nie R; Liu Y; Dong X; Hu Q
Front Cardiovasc Med; 2022; 9():1053697. PubMed ID: 36620624
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Identification of Key Genes as Early Warning Signals of Acute Myocardial Infarction Based on Weighted Gene Correlation Network Analysis and Dynamic Network Biomarker Algorithm.
Song C; Qiao Z; Chen L; Ge J; Zhang R; Yuan S; Bian X; Wang C; Liu Q; Jia L; Fu R; Dou K
Front Immunol; 2022; 13():879657. PubMed ID: 35795669
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]