Neural networks for genetic epidemiology: past, present, and future

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Alison A Motsinger-Reif and Marylyn D Ritchie",
  title =        "Neural networks for genetic epidemiology: past,
                 present, and future",
  journal =      "BioData Mining",
  year =         "2008",
  volume =       "1",
  number =       "3",
  month =        jul # " 17",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, ANN",
  DOI =          "doi:10.1186/1756-0381-1-3",
  abstract =     "During the past two decades, the field of human
                 genetics has experienced an information explosion. The
                 completion of the human genome project and the
                 development of high throughput SNP technologies have
                 created a wealth of data; however, the analysis and
                 interpretation of these data have created a research
                 bottleneck. While technology facilitates the
                 measurement of hundreds or thousands of genes,
                 statistical and computational methodologies are lacking
                 for the analysis of these data. New statistical methods
                 and variable selection strategies must be explored for
                 identifying disease susceptibility genes for common,
                 complex diseases. Neural networks (NN) are a class of
                 pattern recognition methods that have been successfully
                 implemented for data mining and prediction in a variety
                 of fields. The application of NN for statistical
                 genetics studies is an active area of research. Neural
                 networks have been applied in both linkage and
                 association analysis for the identification of disease
                 susceptibility genes.

                 In the current review, we consider how NN have been
                 used for both linkage and association analyses in
                 genetic epidemiology. We discuss both the successes of
                 these initial NN applications, and the questions that
                 arose during the previous studies. Finally, we
                 introduce evolutionary computing strategies, Genetic
                 Programming Neural Networks (GPNN) and Grammatical
                 Evolution Neural Networks (GENN), for using NN in
                 association studies of complex human diseases that
                 address some of the caveats illuminated by previous
  notes =        "PMID:",

Genetic Programming entries for Alison A Motsinger Marylyn D Ritchie