The power of quantitative grammatical evolution neural networks to detect gene-gene interactions

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Hardison:2011:GECCO,

• author = "Nicholas E. Hardison and Alison A. Motsinger-Reif",
• title = "The power of quantitative grammatical evolution neural networks to detect gene-gene interactions",
• booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation",
• year = "2011",
• editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger",
• isbn13 = "978-1-4503-0557-0",
• pages = "299--306",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, Bioinformatics, computational, systems, and synthetic biology",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Dublin, Ireland",
• DOI = "doi:10.1145/2001576.2001618",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Applying grammatical evolution to evolve neural networks (GENN) has been increasing used in genetic epidemiology to detect gene-gene or gene-environment interactions, also known as epistasis, in high dimensional data. GENN approaches have previously been shown to be highly successful in a range of simulated and real case-control studies, and has recently been applied to quantitative traits. In the current study, we evaluate the potential of an application of GENN to quantitative traits (QTGENN) to a range of simulated genetic models. We demonstrate the power of the approach, and compare this power to more traditional linear regression analysis approaches. We find that the QTGENN approach has relatively high power to detect both single-locus models as well as several completely epistatic two-locus models, and favourably compares to the regression methods.",
• notes = "Also known as \cite{2001618} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)",

}

Genetic Programming entries for Nicholas E Hardison Alison A Motsinger

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