ArticleFrontiers in cardiovascular medicine2025
Identification of signature genes and subtypes for heart failure diagnosis based on machine learning.
Article in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Conserved Transcriptional Circuits Regulate Cardiac Fibroblast-Mediated Fibrosis.Circulation research · 2026Article
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- Unveiling the Therapeutic Mechanisms of Chinese Herbs in Heart Failure: Integrating Network Pharmacology, Molecular Docking, and Simulation Analysis.Pharmaceuticals (Basel, Switzerland) · 2025Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Heart failure (HF) is a multifaceted clinical condition, and our comprehension of its genetic pathogenesis continues to be significantly limited. Consequently, identifying specific genes for HF at the transcriptomic level may enhance early detection and allow for more targeted therapies for these individuals. Methods: HF datasets were acquired from the Gene Expression Omnibus (GEO) database (GSE57338), and through the application of bioinformatics and machine-learning algorithms. We identified four candidate genes ( Results: A total of 295 differential genes were identified in the HF dataset, and intersected with the blue module gene with the highest correlation to HF identified by weighted correlation network analysis ( Conclusions: Our research identified four unique genes (
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