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Related Experiment Video

Updated: May 31, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

An evaluation protocol for subtype-specific breast cancer event prediction.

Herman M J Sontrop1, Wim F J Verhaegh, Marcel J T Reinders

  • 1Molecular Diagnostics Department, Philips Research, Eindhoven, The Netherlands.

Plos One
|July 16, 2011
PubMed
Summary

Developing subtype-specific breast cancer predictors can improve accuracy. Our study demonstrates that subtype-specific models outperform general predictors, even with limited sample sizes, by carefully controlling for sample and subtype distributions.

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Area of Science:

  • Molecular biology
  • Genomics
  • Cancer research

Background:

  • Breast cancer exhibits molecular heterogeneity, suggesting distinct subtypes.
  • Developing subtype-specific predictors may offer improved accuracy over monolithic models.
  • Stratification by subtype reduces sample size, posing challenges for predictor construction.

Purpose of the Study:

  • To present a novel protocol for comparing subtype-specific and general breast cancer predictors.
  • To rigorously assess the impact of sample size and distribution on predictor performance.
  • To determine if subtype-specific predictors offer advantages despite reduced sample sizes.

Main Methods:

  • A novel experimental protocol was developed for comprehensive predictor comparison.
  • Careful control of sample size, class, and subtype distributions was emphasized.

Related Experiment Videos

Last Updated: May 31, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

  • The methodology was applied to a large breast cancer compendium (>1500 arrays) using a current subtyping scheme.

    • Main Results:

    • Subtype-specific predictors demonstrated superior performance compared to general predictors.
    • The advantage of subtype-specific predictors was particularly evident when accounting for sample size.
    • The experimental protocol facilitated a robust comparison under controlled conditions.

    Conclusions:

    • Subtype-specific prediction models for breast cancer are more effective than general models.
    • Careful experimental design can mitigate the challenges associated with reduced sample sizes in subtype-specific analyses.
    • This approach enhances the development of targeted and accurate breast cancer diagnostics.