ArticleFrontiers in cardiovascular medicine2022
Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Identification of key biomarkers for myocardial infarction by multi-omics analysis and machine learning.Frontiers in immunology · 2026Article
- Machine learning-based prediction of drug response in ischemia reperfusion animal model.Scientific reports · 2025Article
- Machine learning driven multiomics analysis identifies disulfidptosis associated molecular subtypes in ovarian cancer.Scientific reports · 2025Article
- Machine Learning-Driven Transcriptome Analysis of Keratoconus for Predictive Biomarker Identification.Biomedicines · 2025Article
- Metabolomics Insights into Gut Microbiota and Functional Constipation.Metabolites · 2025Review
- Identification of novel lipid metabolism-related biomarkers of aortic dissection by integrating single-cell RNA sequencing analysis and machine learning algorithms.Frontiers in immunology · 2025Article
- Integrating single-cell RNA-Seq and machine learning to dissect tryptophan metabolism in ulcerative colitis.Journal of translational medicine · 2024Article
- Identification of Immuno-Inflammation-Related Biomarkers for Acute Myocardial Infarction Based on Bioinformatics.Journal of inflammation research · 2023Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.
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