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miRNA-Based Breast Cancer Subtyping Using AHALA Multi-Stage Classification Approach.

Mohammed Qaraad1, Eric P Rahrmann1, David Guinovart1

  • 1The Hormel Institute, University of Minnesota, 801 16th Ave NE, Austin, MN 55912, USA.

Cancers
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

A new algorithm, Adaptive Hill Climbing Artificial Lemming Algorithm (AHALA), accurately subtypes breast cancer using microRNA (miRNA) expression. This precision medicine approach identifies key biomarkers for improved diagnosis and treatment.

Keywords:
breast cancer subtypingmiRNA biomarkersmiRNA multi-classificationneural networkoptimization algorithms

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Breast cancer is molecularly heterogeneous, necessitating precise subtyping for effective precision medicine.
  • MicroRNA (miRNA) expression profiles offer potential for breast cancer subtyping but face challenges in feature selection and algorithm optimization.
  • Accurate subtyping is crucial for tailoring treatments to individual breast cancer molecular profiles.

Purpose of the Study:

  • To develop and validate a novel optimization framework for accurate miRNA-based breast cancer subtyping.
  • To enhance the potential of miRNAs as diagnostic biomarkers through advanced feature selection and machine learning.
  • To improve the precision of breast cancer classification for personalized treatment strategies.

Main Methods:

  • Proposed the Adaptive Hill Climbing Artificial Lemming Algorithm (AHALA), a hybrid optimization framework.
  • Applied low-variance filtering and differential gene expression analysis for dimensionality reduction.
  • Utilized AHALA to optimize deep neural network hyperparameters for multi-class breast cancer subtype classification using miRNA data.
  • Validated the method on TCGA breast cancer miRNA expression data and benchmarked against other optimization algorithms.

Main Results:

  • AHALA achieved high classification performance: 95.74% accuracy, 95.98% precision, 95.74% recall, 95.74% F1 score, and 0.9682 AUC.
  • The algorithm demonstrated superior convergence and significance compared to existing optimization methods.
  • Identified specific miRNAs (e.g., hsa-miR-190b, hsa-miR-429) associated with distinct breast cancer subtypes.

Conclusions:

  • The AHALA framework provides an efficient and potent method for miRNA-based breast cancer subtyping.
  • The algorithm's integration of global exploration and local search enhances classification performance and stability.
  • AHALA effectively identifies biologically significant biomarkers, marking it as a promising tool for breast cancer diagnostics.