Symbolic Discriminant Analysis for Mining Gene Expression Patterns

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{moore:2001:ECML,
  author =       "Jason Moore and Joel Parker and Lance Hahn",
  title =        "Symbolic Discriminant Analysis for Mining Gene
                 Expression Patterns",
  booktitle =    "12th European Conference on Machine Learning
                 (ECML'01)",
  year =         "2001",
  editor =       "Luc {De Raedt} and Peter Flach",
  volume =       "2167",
  series =       "Lecture Notes in Computer Science",
  pages =        "372--381",
  address =      "Freiburg, Germany",
  month =        "3-7 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-42536-5",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2167&spage=372",
  size =         "10 pages",
  abstract =     "New laboratory technologies have made it possible to
                 measure the expression levels of thousands of genes
                 simultaneously in a particular cell or tissue. The
                 challenge for computational biologists will be to
                 develop methods that are able to identify subsets of
                 gene expression variables that classify cells and
                 tissues into meaningful clinical groups. 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 pre-specified.
                 To address this limitation and the limitation of
                 linearity, we developed symbolic discriminant analysis
                 (SDA) for the automatic selection of gene expression
                 variables and discriminant functions that can take any
                 form. We have implemented the genetic programming
                 machine learning methodology for optimizing SDA in
                 parallel on a Beowulf-style computer cluster.",
  notes =        "http://www.informatik.uni-freiburg.de/~ml/ecmlpkdd/index.html",
}

Genetic Programming entries for Jason H Moore Joel S Parker Lance W Hahn

Citations