Cancer classification using microarray and layered architecture genetic programming

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  author =       "Jung-Yi Lin",
  title =        "Cancer classification using microarray and layered
                 architecture genetic programming",
  booktitle =    "GECCO-2009 Late-Breaking Papers",
  year =         "2009",
  editor =       "Anna I. Esparcia and Ying-ping Chen and 
                 Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and 
                 Hans-Georg Beyer and Nikolaus Hansen and 
                 Steffen Finck and Raymond Ros and Darrell Whitley and 
                 Garnett Wilson and Simon Harding and W. B. Langdon and 
                 Man Leung Wong and Laurence D. Merkle and Frank W. Moore and 
                 Sevan G. Ficici and William Rand and Rick Riolo and 
                 Nawwaf Kharma and William R. Buckley and Julian Miller and 
                 Kenneth Stanley and Jaume Bacardit and Will Browne and 
                 Jan Drugowitsch and Nicola Beume and Mike Preuss and 
                 Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and 
                 Alexandru Floares and Aaron Baughman and 
                 Steven Gustafson and Maarten Keijzer and Arthur Kordon and 
                 Clare Bates Congdon and Laurence D. Merkle and 
                 Frank W. Moore",
  pages =        "2085--2090",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP,",
  DOI =          "doi:10.1145/1570256.1570281",
  abstract =     "An important problem of cancer diagnosis and treatment
                 is to distinguish tumors from malignant or benign.
                 Classifying tumors correctly leads us to target
                 specific therapies properly to maximizing efficiency
                 and reducing toxicity. Through the microarray
                 technology, it is possible that monitoring expression
                 in cells for numerous of genes simultaneously.
                 Therefore we are allowed to use potential information
                 hidden in the gene expression data to build a more
                 accurate and more reliable classification model on
                 tumor samples. In this paper we intend to investigate a
                 new approach for cancer classification using genetic
                 programming and microarray gene expression profiles.
                 The layered architecture genetic programming (LAGEP) is
                 applied to build the classification model. Some typical
                 cancer gene expression datasets are validated to
                 demonstrate the classification accuracy of the proposed
  notes =        "Distributed on CD-ROM at GECCO-2009.

                 ACM Order Number 910092.",

Genetic Programming entries for Mick Jung-Yi Lin