ArticleDisease markers2020
Identification of Featured Metabolism-Related Genes in Patients with Acute Myocardial Infarction.
Article in Disease markers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Generative Artificial Intelligence vs. Transformer and Benchmarking Against Deep/Machine Learning: Classification and Scientific Validation of Heart Failure Patients Using Women's Transcriptomic Gene Data.Diagnostics (Basel, Switzerland) · 2026Article
- AKR1C3 protects cardiomyocytes against hypoxia-induced cell apoptosis through the Nrf-2/NF-κB pathway.Acta biochimica et biophysica Sinica · 2025Article
- Unbalanced Redox With Autophagy in Cardiovascular Disease.Journal of lipid and atherosclerosis · 2023Review
- Identifying Genes Related to Acute Myocardial Infarction Based on Network Control Capability.Genes · 2022Article
- Transcription factor JDP2 activates PDE4B to participate in hypoxia/reoxygenation-induced H9c2 cell injury.Experimental and therapeutic medicine · 2022Article
- Nonlinear Relationship Between Chinese Visceral Adiposity Index and New-Onset Myocardial Infarction in Patients with Hypertension and Obstructive Sleep Apnoea: Insights from a Cohort Study.Journal of inflammation research · 2022Article
- AKR1C3 and Its Transcription Factor HOXB4 Are Promising Diagnostic Biomarkers for Acute Myocardial Infarction.Frontiers in cardiovascular medicine · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveA growing body of emerging evidence indicates that metabolic processes play a pivotal role in the biological processes underlying acute myocardial infarction (AMI). The aim of the current study was to identify featured metabolism-related genes in patients with AMI using a support vector machine (SVM) and to further explore the value of these genes in the diagnosis of AMI.
methodsGene microarray expression data related to AMI were downloaded from the GSE66360 dataset in the Gene Expression Omnibus (GEO) database. This data set consisted of 50 AMI samples and 49 normal controls that were randomly classified into a discovery cohort (21 AMI samples and 22 normal controls) and a validation cohort (28 AMI and 28 normal controls). We applied a machine learning method that combined SVM with recursive feature elimination (RFE) to discriminate AMI patients from normal controls. Based on this, an SVM classifier was constructed. Receiver operating characteristic (ROC) analysis was used to investigate the predictive value for the early diagnosis of AMI in the two cohorts and was then further verified in an independent external cohort.
resultsThree metabolism-related genes were identified based on SVM-RFE (
conclusionThe identified metabolism-related genes may play important roles in the development of AMI and may represent diagnostic and therapeutic biomarkers of AMI.
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