Power of grammatical evolution neural networks to detect gene-gene interactions in the presence of error

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@Article{Motsinger-Reif:2008:BMCrn,
  author =       "Alison A Motsinger-Reif and Theresa J Fanelli and 
                 Anna C Davis and Marylyn D Ritchie",
  title =        "Power of grammatical evolution neural networks to
                 detect gene-gene interactions in the presence of
                 error",
  journal =      "BMC Research Notes",
  year =         "2008",
  volume =       "1",
  number =       "65",
  month =        aug # " 13",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, GENN genotyping Multifactor MDR",
  DOI =          "doi:10.1186/1756-0500-1-65",
  format =       "application/zip",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.275.7964",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.275.7964",
  URL =          "ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/f2/e4/BMC_Res_Notes_2008_Aug_13_1_65.tar.gz",
  abstract =     "Background

                 With the advent of increasingly efficient means to
                 obtain genetic information, a great insurgence of data
                 has resulted, leading to the need for methods for
                 analyzing this data beyond that of traditional
                 parametric statistical approaches. Recently we
                 introduced Grammatical Evolution Neural Network (GENN),
                 a machine-learning approach to detect gene-gene or
                 gene-environment interactions, also known as epistasis,
                 in high dimensional genetic epidemiological data. GENN
                 has been shown to be highly successful in a range of
                 simulated data, but the impact of error common to real
                 data is unknown. In the current study, we examine the
                 power of GENN to detect interesting interactions in the
                 presence of noise due to genotyping error, missing
                 data, phenocopy, and genetic heterogeneity.
                 Additionally, we compare the performance of GENN to
                 that of another computational method -- Multifactor
                 Dimensionality Reduction (MDR). Findings

                 GENN is extremely robust to missing data and genotyping
                 error. Phenocopy in a dataset reduces the power of both
                 GENN and MDR. GENN is reasonably robust to genetic
                 heterogeneity and find that in some cases GENN has
                 substantially higher power than MDR to detect
                 functional loci in the presence of genetic
                 heterogeneity. Conclusion

                 GENN is a promising method to detect gene-gene
                 interaction, even in the presence of common types of
                 error found in real data.",
  notes =        "PMID:",
}

Genetic Programming entries for Alison A Motsinger Theresa J Fanelli Anna C Davis Marylyn D Ritchie

Citations