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Related Experiment Videos

A robust machine learning approach for breast cancer subtype classification using relative gene expression order

Shamita Uma Kandan1, Osman Abul2

  • 1Department of Electrical Engineering, College of Engineering, University of Sharjah, Sharjah, United Arab Emirates.

Computer Methods and Programs in Biomedicine
|June 16, 2026
PubMed
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Machine learning models using relative gene expression order effectively classify breast cancer subtypes. This approach enhances robustness and generalizability across diverse datasets, improving clinical decision-making.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Breast cancer subtype classification is crucial for personalized treatment and prognosis.
  • Machine learning (ML) models analyzing gene expression show promise but struggle with technical data variability.
  • Robust and generalizable ML classifiers are needed for reliable breast cancer subtyping.

Purpose of the Study:

  • To develop and evaluate a robust machine learning approach for breast cancer subtype classification.
  • To leverage relative gene expression order representations to overcome technical variability in gene expression data.
  • To assess the performance and clinical relevance of these ML models across diverse datasets.

Main Methods:

  • Proposed a machine learning approach using rank- and word2vec embedding-based relative gene expression order.
Keywords:
Breast cancer subtype classificationGene expressionMachine learningRank normalizationRobust classificationWord2vec embedding

Related Experiment Videos

  • Representations were derived from the within-sample relative expression order of PAM50 genes.
  • Models were trained and systematically evaluated for robustness and clinical relevance on benchmark datasets (SCAN-B, TCGA-BRCA).

    • Main Results:

    • Rank- and word2vec models achieved high precision and recall (≥91%) on cross-validation and test sets.
    • Models demonstrated high accuracy (≥95%) on the external TCGA-BRCA dataset.
    • Gene expression order representations preserved biological variation and enabled meaningful survival stratification.

    Conclusions:

    • Relative gene expression order, via ranks and word2vec embeddings, enables robust breast cancer subtype classification.
    • This method reduces dependence on cohort-wide normalization, addressing technical variability.
    • The approach supports reliable subtype classification in diverse clinical settings.