Cross Validation Consistency for the Assessment of Genetic Programming Results in Microarray Studies

  • Jason H. Moore
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)


DNA microarray technology has made it possible to measure the expression levels of thousands of genes simultaneously in a particular cell or tissue. The challenge for computational biologists and bioinformaticists will be to develop methods that are able to identify subsets of gene expression variables and features that classify cells and tissues into meaningful biological and clinical groups. Genetic programming (GP) has emerged as a machine learning tool for variable and feature selection in microarray data analysis. However, a limitation of GP is a lack of cross validation strategies for the assessment of GP results. This is partly due to the inherent complexity of GP due to its stochastic properties. Here, we introduce and review cross validation consistency (CVC) as a new modeling strategy for use with GP. We review the application of CVC to symbolic discriminant analysis (SDA), a GP-based analytical strategy for mining gene expression patterns in DNA microarray data.


Systemic Lupus Erythematosus Acute Myeloid Leukemia Linear Discriminant Analysis Multifactor Dimensionality Reduction Gene Expression Variable 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jason H. Moore
    • 1
  1. 1.Program in Human Genetics, Department of Molecular Physiology and BiophysicsVanderbilt UniversityNashvilleUSA

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