Article

Random survival forests identify myocardial gene signatures associated with survival in heart failure with preserved ejection fraction

2025-06-11

Abstract excerpt

Heart failure with preserved ejection fraction (HFpEF) continues to be poorly understood at the molecular level. While previous studies have identified gene expression signatures unique to HFpEF, genes associated with clinical decompensation have not been determined. Here, we performed exploratory analysis of myocardial RNA-seq data to identify genes associated with event-free survival in HFpEF. We analyzed previo...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: cfe89887-a1c6-5e5b-8ec5-3158d3525e7f
DOI: 10.1101/2025.06.08.658488

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Random survival forests identify myocardial gene signatures associated with survival in heart failure with preserved ejection fractionDOI 10.1101/2025.06.08.658488Read

Deciphering human heart failure with preserved ejection fraction (HFpEF) at single cell resolutionSemantic proximity Read

Left Ventricular Gene Expression in Heart Failure With Preserved Ejection Fraction—Profibrotic and Proinflammatory Pathways and GenesSemantic proximity Read

Identification of fibrosis-associated biomarkers in heart failure and human cancersSemantic proximity Read

Prognostic Impact of HFpEF in Hypertrophic Cardiomyopathy: A Machine Learning-Based Risk Stratification StudySemantic proximity Read

Identification of signature genes and subtypes for heart failure diagnosis based on machine learningSemantic proximity Read

A comparative study of discriminating human heart failure etiology using gene expression profilesSemantic proximity Read

Chamber-enriched gene expression profiles in failing human hearts with reduced ejection fractionSemantic proximity Read

Myocardial RNA Sequencing Reveals New Potential Therapeutic Targets in Heart Failure with Preserved Ejection FractionSemantic proximity Read

Proteomic Associations of Adverse Outcomes in Human Heart FailureSemantic proximity Read

A Consensus Transcriptional Landscape of Human End-Stage Heart FailureSemantic proximity Read

Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.Semantic proximity Read

Multimodal Machine Learning Reveals the Genomic and Proteomic Architecture of Heart Failure with Preserved Ejection FractionSemantic proximity Read