| Genetic Programming based DNA Microarray Analysis for Classification of Cancer (2008) | |||||||||||||||
Abstract | |||||||||||||||
| Abstract. In this study the advantages of statistical gene selection are combined with the power of Genetic Programming (GP) to build classifiers for assigning gene expression microarray data samples to categories characteristic of certain cell states. To that end we implemented different statistical measures in a program called GE-NEACTIVATOR and tested their applicability to gene selection. Subsequently we used the general purpose GP-system DISCIPULUS to train classifiers. We applied our approach to four different human cancer gene expression datasets publicly available, including multi-class sets. The results indicate that using gene selection and GP as implemented in DISCIPULUS is an appropriate method for gene expression data analysis. 1 | |||||||||||||||
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