Kristopher Hoover
ABSTRACT
The detection of gene-gene and gene-environment interactions in genetic association studies presents a difficult computational and statistical challenge, especially as advances in genotyping technology have rapidly expanded the number of potential genetic predictors in such studies. The scale of these studies makes exhaustive search approaches infeasible, inspiring the application of evolutionary computation algorithms to perform variable selection and build classification models. Recently, an application of grammatical evolution to evolve decision trees (GEDT) has been introduced for detecting interaction models. Initial results were promising, but relied on arbitrary parameter choices for the evolutionary process. In the current study, we present the results of a parameter sweep evaluating the power of GEDT and show that improved parameter choices improves the performance of the method. The results of these experiments are important for the continued optimization, evaluation, and comparison of this and related methods, and for proper application in real data.
- O'Neill, M. and C. Ryan, Grammatical Evolution: Evolutionary automatic programming in an arbitrary language. 2003, Boston: Kluwer Academic Publishers. Google Scholar
Digital Library
- Motsinger-Reif, A.A., et al., Grammatical evolution decision trees for detecting gene-gene interactions. BioData Min, 2010. 3(1): p. 8.Google ScholarCross Ref
- Li, W. and J. Reich, A complete enumeration and classification of two-locus disease models. Hum Hered, 2000. 50(6): p. 334--49.Google ScholarCross Ref
- Dudek, S.M., et al., Data simulation software for whole-genome association and other studies in human genetics. Pac Symp Biocomput, 2006: p. 499--510.Google Scholar
Index Terms
- Optimization of grammatical evolution decision trees
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