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Related Experiment Video

Updated: Jun 4, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Explainable machine learning-driven identification of heart failure biomarkers: a multi-model feature selection

Yuhe Zhao1, Ruoyu Zhang1, Kelan Zha2

  • 1Department of Cardiology, The Seventh People's Hospital of Chongqing/The Central Hospital Affiliated to Chongqing University of Technology, Chongqing, China.

Scientific Reports
|May 25, 2026
PubMed
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Researchers developed a machine learning framework to find new heart failure (HF) biomarkers. FNDC1, LPCAT3, and TIMP2 were identified as potential transcriptomic signatures for HF diagnosis and understanding its mechanisms.

Area of Science:

  • Genomics
  • Biomarker Discovery
  • Computational Biology

Background:

  • Heart failure (HF) presents complex challenges in pathophysiology and biomarker identification.
  • Existing biomarkers for HF have limitations in accuracy and scope.
  • Novel transcriptomic signatures are needed for improved HF diagnosis and management.

Purpose of the Study:

  • To develop a machine learning (ML) framework for identifying novel transcriptomic signatures of heart failure (HF).
  • To prioritize and validate potential HF biomarkers using integrated transcriptomic datasets.
  • To explore the functional implications of identified biomarkers in HF pathogenesis.

Main Methods:

  • Integration and harmonization of three Gene Expression Omnibus (GEO) RNA-seq datasets (GSE141910, GSE198945, GSE263297).
Keywords:
FNDC1Heart failureLPCAT3Machine learningSHAPTIMP2

Related Experiment Videos

Last Updated: Jun 4, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

  • A triphasic feature selection process involving LASSO, Random Forest (RF), and SVM-Recursive Feature Elimination (SVM-RFE).
  • A 10-model ensemble system evaluated using Leave-One-Study-Out Cross-Validation (LOSO-CV) and validated with an external cohort (GSE135055) and RT-qPCR.

    • Main Results:

    • Three candidate biomarkers—FNDC1, LPCAT3, and TIMP2—were identified and prioritized.
    • FNDC1 and TIMP2 showed significant upregulation, while LPCAT3 was suppressed in HF tissues (p < 0.001), confirmed by RT-qPCR.
    • The ML models achieved high diagnostic performance (peak LOSO-CV AUC 0.973) and external validation (AUC up to 0.876).
    • SHAP analysis highlighted FNDC1 as the most influential predictor, with functional enrichment linking signatures to extracellular matrix remodeling and lipid metabolism.

    Conclusions:

    • FNDC1, LPCAT3, and TIMP2 represent promising novel transcriptomic biomarkers for heart failure.
    • These biomarkers are associated with key pathological mechanisms in HF, including extracellular matrix remodeling and lipid metabolism.
    • The developed ML framework offers a robust approach for discovering transcriptomic signatures in complex diseases like HF.