Key result
Machine learning identifies 4 transcriptomic hub genes with good diagnostic potential for HF.
Why the study?
Comprehension of the genetic pathogenesis of heart failure remains significantly limited, and identifying specific transcriptomic genes may enhance early detection and targeted therapies.
Population
Heart failure datasets from the Gene Expression Omnibus database and The Cancer Genome Atlas
Design
Bioinformatics and machine-learning diagnostic and clustering study
Authors
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These genes should not yet inform heart failure diagnostics; leaves open prospective validation in clinical cohorts.
Effect estimate: AUC > 0.7
p-value: p=1.3 x 10^-43
Machine learning analysis of public transcriptomic datasets identified four novel genes (FCN3, FREM1, MNS1, SMOC2) as potential diagnostic biomarkers for heart failure.
Zhang et al. (2025) studied Heart failure. Machine learning algorithms was evaluated on Diagnostic potential of hub genes for heart failure (AUC > 0.7, p=1.3 x 10^-43). Machine learning analysis of transcriptomic datasets identified four hub genes (FCN3, FREM1, MNS1, and SMOC2) with good diagnostic potential for heart failure (AUC > 0.7).
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