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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

TENTACLES: a consensus machine learning tool for robust biomarker discovery in heterogeneous data.

Giorgio Montesi1, Gabriel Dos Santos Mouta1, Maria Novedrati1

  • 1Laboratory of Molecular Microbiology and Biotechnology, Department of Medical Biotechnologies, University of Siena, Siena, Italy.

Biodata Mining
|June 11, 2026
PubMed
Summary

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This summary is machine-generated.

TENTACLES, a new R package, enhances transcriptomic biomarker discovery by using ensemble methods to identify reproducible gene signatures across diverse datasets. This approach overcomes limitations of single-algorithm methods, improving generalizability and reducing feature numbers for robust biological signal extraction.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcriptomic biomarker discovery faces challenges with reproducibility due to model biases and data heterogeneity.
  • Single-algorithm approaches often lack generalizability across independent cohorts.
  • Ensemble methods show promise but are underexplored for consensus-based feature prioritization in transcriptomics.

Purpose of the Study:

  • To develop a robust framework for reproducible transcriptomic biomarker discovery using multi-algorithm consensus.
  • To address the limitations of existing methods in handling dataset heterogeneity and model-specific biases.
  • To create an open-source tool for automated feature prioritization and cross-cohort validation.

Main Methods:

  • Developed TENTACLES (Transcriptomic Exploration Tool through Aggregation of Classifiers), an open-source R package.
Keywords:
Biomarker discoveryComplex diseasesCrohn’s diseaseEnsemble learningMachine learning

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  • Integrated up to 15 supervised learning and 6 unsupervised clustering algorithms.
  • Employed a modular architecture for automated preprocessing, feature prioritization, and validation across multiple cohorts.

    • Main Results:

    • TENTACLES identified a 28-gene consensus panel for Crohn's disease with superior cross-cohort generalizability.
    • The consensus signature used 95% fewer features than conventional differential expression methods.
    • A minimal 5-gene core signature maintained robust discriminatory power in unsupervised validation.
    • The framework demonstrated the ability to extract stable biological signals from complex, noisy transcriptomic data.

    Conclusions:

    • TENTACLES offers a scalable, disease-agnostic solution for identifying reproducible gene signatures from heterogeneous transcriptomic data.
    • The software bridges ensemble modeling and practical biomarker discovery for reproducible results.
    • It serves as a versatile resource for researchers in diverse disease contexts seeking stable biomarkers.