Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Identification of Immune-Related Genes in Patients with Acute Myocardial Infarction Using Machine Learning Methods

1 Jun 2022

Abstract excerpt

Objective: This study aimed to analyze immune-related genes and immune cell components in the peripheral blood of patients with acute myocardial infarction (AMI). Methods: Six datasets were obtained from the GEO repository comprising 88 healthy samples and 215 AMI samples. We performed the weighted gene co-expression analysis (WGCNA) and five machine learning (ML) methods to identify immune-related genes and...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Adipokines, Inflammation, and Metabolic Diseases
Share to Atherosclerosis and Cardiovascular Diseases
Share to Cardiac Fibrosis and Remodeling

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 35692951
DOI: 10.2147/jir.s360498

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identification of Immune-Related Genes in Patients with Acute Myocardial Infarction Using Machine Learning MethodsPMID 35692951Read ↗

Identifying patterns of immune related cells and genes in the peripheral blood of acute myocardial infarction patients using a small cohortSemantic proximity Read ↗

Biomarkers identification for acute myocardial infarction detection via weighted gene co-expression network analysisSemantic proximity Read ↗

Predicting Diagnostic Gene Biomarkers Associated With Immune Infiltration in Patients With Acute Myocardial InfarctionSemantic proximity Read ↗

Integration of Machine Learning to Identify Diagnostic Genes in Leukocytes for Acute Myocardial Infarction PatientsSemantic proximity Read ↗

Identification of Immune Activation-Related Biomarkers in Acute Myocardial Infarction (AMI) by Bioinformatic Analysis and Clinical ValidationSemantic proximity Read ↗

Identification of Immuno-Inflammation-Related Biomarkers for Acute Myocardial Infarction Based on BioinformaticsSemantic proximity Read ↗

Identification and validation of prognostic signature and immune infiltrate characteristics for acute myocardial infarction based on neutrophil extracellular trapsSemantic proximity Read ↗

Integrated Bioinformatics-Based Analysis of Hub Genes and the Mechanism of Immune Infiltration Associated With Acute Myocardial InfarctionSemantic proximity Read ↗

Integrated analysis and validation of ferroptosis-related genes and immune infiltration in acute myocardial infarctionSemantic proximity Read ↗

Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulationSemantic proximity Read ↗

Identification of Potential Biomarkers Associated with Acute Myocardial Infarction by Weighted Gene Coexpression Network AnalysisSemantic proximity Read ↗

Identification of key genes associated with acute myocardial infarction using WGCNA and two-sample mendelian randomization studySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.