Complex Function Sets Improve Symbolic Discriminant Analysis of Microarray Data

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@InProceedings{reif:2003:gecco,
  author =       "David M. Reif and Bill C. White and Nancy Olsen and 
                 Thomas Aune and Jason H. Moore",
  title =        "Complex Function Sets Improve Symbolic Discriminant
                 Analysis of Microarray Data",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2003",
  editor =       "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and 
                 D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and 
                 R. Standish and G. Kendall and S. Wilson and 
                 M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and 
                 A. C. Schultz and K. Dowsland and N. Jonoska and 
                 J. Miller",
  year =         "2003",
  pages =        "2277--2287",
  address =      "Chicago",
  publisher_address = "Berlin",
  month =        "12-16 " # jul,
  volume =       "2724",
  series =       "LNCS",
  ISBN =         "3-540-40603-4",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Real World
                 Applications",
  DOI =          "doi:10.1007/3-540-45110-2_121",
  size =         "12 pages",
  abstract =     "Our ability to simultaneously measure the expression
                 levels of thousands of genes in biological samples is
                 providing important new opportunities for improving the
                 diagnosis, prevention, and treatment of common
                 diseases. However, new technologies such as DNA
                 microarrays are generating new challenges for variable
                 selection and statistical modeling. In response to
                 these challenges, a genetic programming-based strategy
                 called symbolic discriminant analysis (SDA) for the
                 automatic selection of gene expression variables and
                 mathematical functions for statistical modeling of
                 clinical endpoints has been developed. The initial
                 development and evaluation of SDA has focused on a
                 function set consisting of only the four basic
                 arithmetic operators. The goal of the present study is
                 to evaluate whether adding more complex operators such
                 as square root to the function set improves SDA
                 modeling of microarray data. The results presented in
                 this paper demonstrate that adding complex functions to
                 the terminal set significantly improves SDA modeling by
                 reducing model size and, in some cases, reducing
                 classification error and runtime. We anticipate SDA
                 will be an important new evolutionary computation tool
                 to be added to the repertoire of methods for the
                 analysis of microarray data.",
  notes =        "GECCO-2003 A joint meeting of the twelvth
                 international conference on genetic algorithms
                 (ICGA-99) and the eigth annual genetic programming
                 conference (GP-2003)

                 square, sqrt, log, exp, abs, sin,cosine. lilgp. PVM, 2
                 demes, LOOCV. systemic lupus erythematosus.",
}

Genetic Programming entries for David M Reif Bill C White Nancy J Olsen Thomas M Aune Jason H Moore

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