On Genetic Programming and Knowledge Discovery in Transcriptome Data

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

  title =        "On Genetic Programming and Knowledge Discovery in
                 Transcriptome Data",
  author =       "Jem Rowland",
  pages =        "158--165",
  booktitle =    "Proceedings of the 2004 IEEE Congress on Evolutionary
  year =         "2004",
  publisher =    "IEEE Press",
  month =        "20-23 " # jun,
  address =      "Portland, Oregon",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Computation in Bioinformatics and Computational
  DOI =          "doi:10.1109/CEC.2004.1330852",
  abstract =     "This paper concerns the use of Genetic Programming
                 (GP) for supervised classification of transcriptome
                 (gene expression) data. In such applications GP can
                 produce accurate predictive models that generalize well
                 and use only very few gene expression values. It is
                 often suggested that the selected genes are therefore
                 of biological significance in discriminating the
                 classes. The paper presents a preliminary study of
                 successful parsimonious GP models to investigate the
                 extent to which the selected variables contribute to
                 the classification. The work is based on a readily
                 available and well studied dataset that represents gene
                 expression values for two groups of patients with
                 different forms of Leukaemia.",
  notes =        "CEC 2004 - A joint meeting of the IEEE, the EPS, and
                 the IEE.",

Genetic Programming entries for Jem J Rowland