Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset

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  author =       "Francesco Archetti and Ilaria Giordani and 
                 Leonardo Vanneschi",
  title =        "Genetic programming for anticancer therapeutic
                 response prediction using the NCI-60 dataset",
  journal =      "Computers \& Operations Research",
  volume =       "37",
  number =       "8",
  pages =        "1395--1405",
  year =         "2010",
  note =         "Operations Research and Data Mining in Biological
  ISSN =         "0305-0548",
  DOI =          "doi:10.1016/j.cor.2009.02.015",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, Regression, Microarray data, Anticancer
                 therapy, NCI-60",
  abstract =     "Statistical methods, and in particular machine
                 learning, have been increasingly used in the drug
                 development workflow. Among the existing machine
                 learning methods, we have been specifically concerned
                 with genetic programming. We present a genetic
                 programming-based framework for predicting anticancer
                 therapeutic response. We use the NCI-60 microarray
                 dataset and we look for a relationship between gene
                 expressions and responses to oncology drugs
                 Fluorouracil, Fludarabine, Floxuridine and Cytarabine.
                 We aim at identifying, from genomic measurements of
                 biopsies, the likelihood to develop drug resistance.
                 Experimental results, and their comparison with the
                 ones obtained by Linear Regression and Least Square
                 Regression, hint that genetic programming is a
                 promising technique for this kind of application.
                 Moreover, genetic programming output may potentially
                 highlight some relations between genes which could
                 support the identification of biological meaningful
                 pathways. The structures that appear more frequently in
                 the 'best' solutions found by genetic programming are

Genetic Programming entries for Francesco Archetti Ilaria Giordani Leonardo Vanneschi