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A CART-based approach to discover emerging patterns in microarray data.

Anne-Laure Boulesteix1, Gerhard Tutz, Korbinian Strimmer

  • 1Seminar for Applied Stochastics, Department of Statistics, University of Munich, Akademiestrasse 1, D-80799 Munich, Germany. boulesteix@stat.uni-muenchen.de

Bioinformatics (Oxford, England)
|December 12, 2003
PubMed
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This study introduces a new method for discovering emerging patterns (EPs) in gene expression data for cancer diagnosis. The approach efficiently identifies significant EPs, improving classification accuracy and gene interaction analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cancer diagnosis relies on gene expression profiles, necessitating supervised learning and gene selection.
  • Emerging patterns (EPs) offer advantages by modeling gene interactions for improved classification accuracy.
  • Discovering short, statistically significant EPs remains a significant challenge in bioinformatics.

Purpose of the Study:

  • To develop a novel, efficient method for discovering statistically significant emerging patterns (EPs) in microarray data.
  • To enhance cancer diagnosis through improved gene expression profile analysis and classification.
  • To provide a computationally fast tool for elucidating gene interactions and avoiding overfitting.

Main Methods:

  • A Classification and Regression Trees (CART)-based approach was developed to discover EPs.

Related Experiment Videos

  • Decision trees were grown to extract EPs, combined with Fisher's exact test for statistical significance.
  • Maximum-likelihood linear discriminant analysis was used for sample classification based on inferred EPs.

    • Main Results:

    • The CART-based method successfully recovered a large proportion of known EPs in simulations.
    • Classification accuracy on real cancer data (colon, leukemia) was comparable to top-performing algorithms.
    • The approach assigns statistical significance to EPs, enables pattern ranking, and prevents data overfitting.

    Conclusions:

    • The developed CART-based approach offers a versatile and computationally efficient tool for cancer diagnosis using gene expression data.
    • This method facilitates the elucidation of local gene interactions and improves classification accuracy.
    • The freely available R program provides a practical resource for researchers in the field.