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 biomarkers associated with phagocytosis regulatory factors in coronary artery disease using machine learning and network analysis.

Mammalian genome : official journal of the International Mammalian Genome Society - 1 Jun 2025

Jia Runan, Li Zhiya, Du Yingying, Liu Huixian, Liang Ruirui

Abstract excerpt

BACKGROUND: Coronary artery disease (CAD) is the leading cause of death worldwide, and aberrant phagocytosis may be involved in its development. Understanding this aspect may provide new avenues for prompt CAD diagnosis. METHODS: CAD-related information was obtained from Gene Expression Omnibus datasets GSE66360, GSE113079, and GSE59421. We identified 995 upregulated and 1086 downregulated differentially...

Read the complete abstract on PubMed

Topics

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

Share to Coronary Artery Disease
Share to Interleukin-1beta

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Machine Learning

Share to Phagocytosis

Share to Protein Interaction Maps

Share to Receptors, IgG

Share to Syk Kinase

Share to Toll-Like Receptor 2

Source

Open in PubMed Open full text

Identifiers

PMID: 39953122
DOI: 10.1007/s00335-025-10111-5
PMCID: PMC12130075

Genes

CAD
FCER1G
HCK
TF
FCGR2A
IL1B
SYK
TLR2
RUNX1

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identification of biomarkers associated with phagocytosis regulatory factors in coronary artery disease using machine learning and network analysis.PMID 39953122Read ↗

Identification of key genes in coronary artery disease: an integrative approach based on weighted gene co-expression network analysis and their correlation with immune infiltrationSemantic proximity Read ↗

Bioinformatics-Based Characterization of Key Biomarkers and Immune Infiltration in Coronary Artery DiseaseSemantic proximity Read ↗

Identification of diagnostic biomarkers and therapeutic targets in peripheral immune landscape from coronary artery diseaseSemantic proximity Read ↗

Machine Learning and Bioinformatics Unravel Gene Signatures of Coronary Artery Disease Comorbidity with PeriodontitisSemantic proximity Read ↗

Bioinformatics analysis of common differential genes of coronary artery disease and carotid atherosclerosisSemantic proximity Read ↗

Identification of biomarkers and therapeutic targets in the peripheral immune landscape from coronary artery diseaseSemantic proximity Read ↗

A macrophage gene-regulatory network linked to clinical severity of coronary artery disease : The STARNET and NGS-PREDICT primary blood macrophage studies.Semantic proximity Read ↗

Transcriptome signatures reveal candidate key genes in the peripheral blood mononuclear cells of patients with coronary artery disease and prediction of small drug moleculesSemantic proximity Read ↗

Integrated analysis of gene expression changes associated with coronary artery diseaseSemantic proximity Read ↗

Identifying functional modules for coronary artery disease by a prior knowledge-based approach.Semantic proximity Read ↗

Comprehensive bioinformatic methods and machine learning algorithms to identify diagnostic biomarkers of coronary artery disease progression in pulmonary arterial hypertensionSemantic proximity Read ↗

Basement Membrane-Related Genes C1QTNF3, PTGER2, CAMK2N1, PRSS36, and B3GNT7: Novel Biomarkers for Coronary Artery Disease.Semantic proximity Read ↗

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