Global top-scoring pair decision tree for gene expression data analysis

Authors:
Marcin Czajkowski;Marek Kretowski
Affiliations:
Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland;Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland
Venue:
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Year:
2013

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Abstract

Extracting knowledge from gene expression data is still a major challenge. Relative expression algorithms use the ordering relationships for a small collection of genes and are successfully applied for micro-array classification. However, searching for all possible subsets of genes requires a significant number of calculations, assumptions and limitations. In this paper we propose an evolutionary algorithm for global induction of top-scoring pair decision trees. We have designed several specialized genetic operators that search for the best tree structure and the splits in internal nodes which involve pairwise comparisons of the gene expression values. Preliminary validation performed on real-life micro-array datasets is promising as the proposed solution is highly competitive to other relative expression algorithms and allows exploring much larger solution space.