Applied multivariate statistical analysis
Applied multivariate statistical analysis
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
Mask functions for the symbolic modeling of epistasis using genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Complex function sets improve symbolic discriminant analysis of microarray data
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Can neural network constraints in GP provide power to detect genes associated with human disease?
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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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.