Machine Learning for Chromatin Regulators in Coronary Artery Disease Diagnosis
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View abstract on PubMed
Summary
This summary is machine-generated.Chromatin regulator-related genes (CRRGs) play a role in coronary artery disease (CAD). This study identified key CRRGs for CAD diagnosis and revealed their association with immune responses and cellular functions.
Area Of Science
- Genomics
- Cardiovascular Medicine
- Bioinformatics
Background
- Coronary artery disease (CAD) is a significant global health concern.
- The role of chromatin regulator-related genes (CRRGs) in CAD pathogenesis requires further investigation.
Purpose Of The Study
- To elucidate the mechanisms of CRRGs in CAD.
- To develop a diagnostic model for CAD utilizing CRRGs.
Main Methods
- Utilized machine learning and classification models on CAD datasets from the GEO database.
- Employed R software for data analysis and gene identification.
- Performed differential expression analysis and functional enrichment studies.
Main Results
- Identified USP44, MOCS1, SSRP1, ZNF516, and SCML1 as key CRRGs for CAD diagnosis using a random forest model.
- Observed associations between differentially expressed CRRGs and aberrant immune cell infiltration in CAD patients.
- Discovered two CAD subtypes based on CRRG expression, with distinct differentially expressed genes and enriched pathways including inflammation and hormone signaling.
Conclusions
- CRRGs are implicated in CAD and offer potential as novel therapeutic targets.
- The identified CRRGs and pathways provide new insights into CAD mechanisms.
- A CRRG-based diagnostic model shows promise for CAD management.
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