Symbolic Discriminant Analysis of Microarray Data in Automimmune Disease

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@Article{moore:2002:SDA,
  author =       "Jason H. Moore and Joel S. Parker and 
                 Nancy J. Olsen and Thomas M. Aune",
  title =        "Symbolic Discriminant Analysis of Microarray Data in
                 Automimmune Disease",
  journal =      "Genetic Epidemiology",
  year =         "2002",
  volume =       "23",
  pages =        "57--69",
  keywords =     "genetic algorithms, genetic programming, DNA chip,
                 rheumatoid arthritis, systemic lupus erythematosus, flu
                 vaccine",
  DOI =          "doi:10.1002/gepi.1117",
  abstract =     "New laboratory technologies such as DNA microarrays
                 have made it possible to measure the expression levels
                 of thousands of genes simultaneously in a particular
                 cell or tissue. The challenge for genetic
                 epidemiologists will be to develop statistical and
                 computational methods that are able to identify subsets
                 of gene expression variables that classify and predict
                 clinical endpoints. Linear discriminant analysis is a
                 popular multivariate statistical approach for
                 classification of observations into groups. This is
                 because the theory is well described and the method is
                 easy to implement and interpret. However, an important
                 limitation is that linear discriminant functions need
                 to be prespecified. To address this limitation and the
                 limitation of linearity, we have developed symbolic
                 discriminant analysis (SDA) for the automatic selection
                 of gene expression variables and discriminant functions
                 that can take any form. In the present study, we
                 demonstrate that SDA is capable of identifying
                 combinations of gene expression variables that are able
                 to classify and predict autoimmune diseases",
  notes =        "LilGP, PVM, LOOCV, 110 node beowulf, Linux",
}

Genetic Programming entries for Jason H Moore Joel S Parker Nancy J Olsen Thomas M Aune

Citations