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Related Experiment Videos

Module-based outcome prediction using breast cancer compendia.

Martin H van Vliet1, Christiaan N Klijn, Lodewyk F A Wessels

  • 1Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands. M.H.vanVliet@TUDelft.nl

Plos One
|October 18, 2007
PubMed
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Predicting breast cancer outcomes is improved by using gene modules derived from biological pathways and datasets. This approach enhances predictor robustness and offers deeper biological insights compared to traditional gene-based methods.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Medicine

Background:

  • Large microarray datasets (compendia) and gene sets are underutilized in disease outcome prediction.
  • Leveraging compendia increases sample size, while gene sets reduce feature space complexity.
  • This integration offers potential for more robust machine learning predictors.

Purpose of the Study:

  • To develop and validate predictors of breast cancer outcome using gene modules.
  • To assess the performance of module-based predictors against gene-based predictors.
  • To investigate the impact of compendium specificity on predictive performance.

Main Methods:

  • Extracted gene modules from gene sets and compendia.
  • Constructed supervised predictors using modules predictive of breast cancer outcome.

Related Experiment Videos

  • Validated predictors on independent intra- and inter-dataset samples.

    • Main Results:

    • Module-based predictors derived from single breast cancer datasets outperformed gene-based predictors on validation data.
    • Predictive performance correlated with compendium specificity; breast cancer-specific compendia yielded better results.
    • Module-based prediction offered richer biological insights, identifying key processes like cell cycle and DNA damage response.

    Conclusions:

    • Gene modules from specific datasets and compendia enhance breast cancer outcome prediction.
    • Module-based approaches provide superior biological interpretability.
    • Selected modules, such as those related to cell cycle, significantly separate survival subgroups.