Understanding the evolutionary process of grammatical evolution neural networks for feature selection in genetic epidemiology

Alison A. Motsinger, David M. Reif, Scott M. Dudek, Marylyn D. Ritchie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

The identification of genetic factors/features that predict complex diseases is an important goal of human genetics. The commonality of gene-gene interactions in the underlying genetic architecture of common diseases presents a daunting analytical challenge. Previously, we introduced a grammatical evolution neural network (GENN) approach that has high power to detect such interactions in the absence of any marginal main effects. While the success of this method is encouraging, it elicits questions regarding the evolutionary process of the algorithm itself and the feasibility of scaling the method to account for the immense dimensionality of datasets with enormous numbers of features. When the features of interest show no main effects, how is GENN able to build correct models? How and when should evolutionary parameters be adjusted according to the scale of a particular dataset? In the current study, we monitor the performance of GENN during its evolutionary process using different population sizes and numbers of generations. We also compare the evolutionary characteristics of GENN to that of a random search neural network strategy to better understand the benefits provided by the evolutionary learning process - including advantages with respect to chromosome size and the representation of functional versus non-functional features within the models generated by the two approaches. Finally, we apply lessons from the characterization of GENN to analyses of datasets containing increasing numbers of features to demonstrate the scalability of the method.

Original languageEnglish (US)
Title of host publicationProceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06
Pages263-270
Number of pages8
DOIs
Publication statusPublished - Dec 1 2006
Event3rd Computational Intelligence in Bioinformatics and Computational Biology Symposium, CIBCB - Toronto, ON, Canada
Duration: Sep 28 2006Sep 29 2006

Publication series

NameProceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06

Other

Other3rd Computational Intelligence in Bioinformatics and Computational Biology Symposium, CIBCB
CountryCanada
CityToronto, ON
Period9/28/069/29/06

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Biomedical Engineering
  • Applied Mathematics
  • Computational Mathematics

Cite this

Motsinger, A. A., Reif, D. M., Dudek, S. M., & Ritchie, M. D. (2006). Understanding the evolutionary process of grammatical evolution neural networks for feature selection in genetic epidemiology. In Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06 (pp. 263-270). [4133181] (Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06). https://doi.org/10.1109/CIBCB.2006.330945