A comparison of machine learning techniques for survival prediction in breast cancer

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Leonardo Vanneschi and Antonella Farinaccio and 
                 Giancarlo Mauri and Mauro Antoniotti and 
                 Paolo Provero and Mario Giacobini",
  title =        "A comparison of machine learning techniques for
                 survival prediction in breast cancer",
  journal =      "BioData Mining",
  year =         "2011",
  volume =       "4",
  number =       "12",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1186/1756-0381-4-12",
  size =         "13 pages",
  abstract =     "Background

                 The ability to accurately classify cancer patients into
                 risk classes, i.e. to predict the outcome of the
                 pathology on an individual basis, is a key ingredient
                 in making therapeutic decisions. In recent years gene
                 expression data have been successfully used to
                 complement the clinical and histological criteria
                 traditionally used in such prediction. Many 'gene
                 expression signatures' have been developed, i.e. sets
                 of genes whose expression values in a tumour can be
                 used to predict the outcome of the pathology. Here we
                 investigate the use of several machine learning
                 techniques to classify breast cancer patients using one
                 of such signatures, the well established 70-gene
                 signature. Results

                 We show that Genetic Programming performs significantly
                 better than Support Vector Machines, Multilayered
                 Perceptrons and Random Forests in classifying patients
                 from the NKI breast cancer data set, and comparably to
                 the scoring-based method originally proposed by the
                 authors of the 70-gene signature. Furthermore, Genetic
                 Programming is able to perform an automatic feature


                 Since the performance of Genetic Programming is likely
                 to be improvable compared to the out-of-the-box
                 approach used here, and given the biological insight
                 potentially provided by the Genetic Programming
                 solutions, we conclude that Genetic Programming methods
                 are worth further investigation as a tool for cancer
                 patient classification based on gene expression data.",

Genetic Programming entries for Leonardo Vanneschi Antonella Farinaccio Giancarlo Mauri Mauro Antoniotti Paolo Provero Mario Giacobini