Classification of Oncologic Data with Genetic Programming

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  title =        "Classification of Oncologic Data with Genetic
  author =       "Leonardo Vanneschi and Francesco Archetti and 
                 Mauro Castelli and Ilaria Giordani",
  journal =      "Journal of Artificial Evolution and Applications",
  year =         "2009",
  volume =       "2009",
  publisher =    "Hindawi Publishing Corporation",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  DOI =          "doi:10.1155/2009/848532",
  ISSN =         "16876229",
  bibsource =    "OAI-PMH server at",
  language =     "eng",
  oai =          "oai:doaj-articles:809187cab9ca01fd2c12625e6010851b",
  abstract =     "Discovering the models explaining the hidden
                 relationship between genetic material and tumor
                 pathologies is one of the most important open
                 challenges in biology and medicine. Given the large
                 amount of data made available by the DNA Microarray
                 technique, Machine Learning is becoming a popular tool
                 for this kind of investigations. In the last few years,
                 we have been particularly involved in the study of
                 Genetic Programming for mining large sets of biomedical
                 data. In this paper, we present a comparison between
                 four variants of Genetic Programming for the
                 classification of two different oncologic datasets: the
                 first one contains data from healthy colon tissues and
                 colon tissues affected by cancer; the second one
                 contains data from patients affected by two kinds of
                 leukemia (acute myeloid leukemia and acute
                 lymphoblastic leukemia). We report experimental results
                 obtained using two different fitness criteria: the
                 receiver operating characteristic and the percentage of
                 correctly classified instances. These results, and
                 their comparison with the ones obtained by three
                 nonevolutionary Machine Learning methods (Support
                 Vector Machines, MultiBoosting, and Random Forests) on
                 the same data, seem to hint that Genetic Programming is
                 a promising technique for this kind of
  notes =        "Article ID 848532",

Genetic Programming entries for Leonardo Vanneschi Francesco Archetti Mauro Castelli Ilaria Giordani