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

Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation

19 Mar 2022

Abstract excerpt

OBJECTIVE: We aimed to screen out biomarkers for atrial fibrillation (AF) based on machine learning methods and evaluate the degree of immune infiltration in AF patients in detail. METHODS: Two datasets (GSE41177 and GSE79768) related to AF were downloaded from Gene expression omnibus (GEO) database and merged for further analysis. Differentially expressed genes (DEGs) were screened out using "limma" package in R...

Read the complete abstract on PubMed

Topics

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

Share to Atrial Fibrillation Management and Outcomes
Share to Cardiac Fibrosis and Remodeling
Share to IL-33, ST2, and ILC Pathways

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 35305619
DOI: 10.1186/s12920-022-01212-0

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillationPMID 35305619Read ↗

Analysis of Potential Genetic Biomarkers Using Machine Learning Methods and Immune Infiltration Regulatory Mechanisms Underlying Atrial Fibrillation Running Title: Identification of Biomarkers for Af via Machine LearningSemantic proximity Read ↗

LAASO algorithm-based bioinformatics analysis for exploring the pathogenesis and immune infiltration of atrial fibrillation and dilated cardiomyopathySemantic proximity Read ↗

Identification of key differentially expressed immune related genes in patients with persistent atrial fibrillation: an integrated bioinformation analysis.Semantic proximity Read ↗

Identification of key immune-related genes and immune infiltration in atrial fibrillation with valvular heart disease based on bioinformatics analysisSemantic proximity Read ↗

Potential Target Genes in the Development of Atrial Fibrillation: A Comprehensive Bioinformatics AnalysisSemantic proximity Read ↗

Identification of potential novel biomarkers and therapeutic targets involved in human atrial fibrillation based on bioinformatics analysisSemantic proximity Read ↗

Atrial fibrillation risk model based on LASSO and SVM algorithms and immune infiltration of key mitochondrial energy metabolism genesSemantic proximity Read ↗

Role of Necroptosis and Immune Infiltration in Atrial Fibrillation: Novel Insights Revealed by Integrated Computational Biology AnalysesSemantic proximity Read ↗

Uncovering potential novel biomarkers and immune infiltration characteristics in persistent atrial fibrillation using integrated bioinformatics analysisSemantic proximity Read ↗

A bioinformatics-based analysis of chromatin regulators in atrial fibrillation uncovers potential novel biomarkers and immune infiltration characteristicsSemantic proximity Read ↗

Crosstalk between Atrial Cardiomyocytes and Epicardial Adipose Tissue in Atrial Fibrillation: Insights from Machine Learning Methods and Rat Atrial Fibrillation ModelSemantic proximity Read ↗

Exploring the pathogenesis and immune infiltration in dilated cardiomyopathy complicated with atrial fibrillation by bioinformatics analysisSemantic proximity Read ↗

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