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Related Experiment Video

Updated: May 10, 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

When complexity does not pay: benchmarking deep learning and ensemble methods for biomarker discovery.

Cyrille Mesue Njume1, Irene Petracci2, Sonia Bellini2

  • 1Department of Molecular Biology and Genetics, Ayazaga Campus, Istanbul Technical University, Reşitpaşa, Sarıyer, 34467 Istanbul, Turkey.

Briefings in Bioinformatics
|May 8, 2026
PubMed
Summary

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This summary is machine-generated.

Benchmarking multi-omics biomarker discovery reveals ensemble feature selection enhances accuracy. Simpler models often match deep learning, highlighting the need for transparency and interpretability in computational methods.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Translational Medicine

Background:

  • Multi-omics data integration offers potential for biomarker discovery but faces challenges due to computational complexity.
  • Evaluating the practical utility and interpretability of various computational methods is crucial for clinical application.

Purpose of the Study:

  • To benchmark 27 feature selection strategies and 11 predictive models across Alzheimer's disease, progressive supranuclear palsy, and breast cancer cohorts.
  • To compare traditional machine learning, ensemble methods, and deep learning for predictive performance, stability, and interpretability.
  • To assess the impact of omics data integration levels (single, dual, triple) on biomarker discovery.

Main Methods:

  • Developed a comprehensive benchmarking framework for feature selection and predictive modeling.
Keywords:
biomarker discoveryensemble rank aggregationfeature selection benchmarkingintegrative bioinformaticsmulti-omics integration

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Last Updated: May 10, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
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  • Evaluated 27 feature selection strategies and 11 predictive models, including logistic regression, support vector machines, multilayer perceptrons, and deep learning.
  • Utilized three real-world disease cohorts and validated biomarkers against five independent databases.

    • Main Results:

    • Ensemble feature selection consistently improved robustness and accuracy, especially for compact biomarker panels.
    • Simpler classifiers (e.g., logistic regression) achieved comparable or superior results to deep learning models, with lower computational cost and higher interpretability.
    • Triple-omics data integration demonstrated the highest validation performance (Triple > Dual > Single).
    • Identified both established and novel biomarkers with confirmed clinical relevance.

    Conclusions:

    • A pragmatic approach prioritizing transparency, reproducibility, and biological insight is recommended for biomarker discovery over algorithmic complexity.
    • Ensemble methods and simpler classifiers offer effective and interpretable solutions for multi-omics biomarker identification.
    • The developed web-based tool supports reproducibility and community adoption of the benchmarking pipeline.