Towards the Use of Genetic Programming for the Prediction of Survival in Cancer

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InCollection{Giacobini:2014:evcoal,
  author =       "Marco Giacobini and Paolo Provero and 
                 Leonardo Vanneschi and Giancarlo Mauri",
  title =        "Towards the Use of Genetic Programming for the
                 Prediction of Survival in Cancer",
  booktitle =    "Evolution, Complexity and Artificial Life",
  publisher =    "Springer",
  year =         "2014",
  editor =       "Stefano Cagnoni and Marco Mirolli and Marco Villani",
  pages =        "177--192",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-37576-7",
  URL =          "http://dx.doi.org/10.1007/978-3-642-37577-4_12",
  DOI =          "doi:10.1007/978-3-642-37577-4_12",
  abstract =     "Risk stratification of cancer patients, that is the
                 prediction of the outcome of the pathology on an
                 individual basis, is a key ingredient in making
                 therapeutic decisions. In recent years, the use of gene
                 expression profiling in combination with the clinical
                 and histological criteria traditionally used in such a
                 prediction has been successfully introduced. Sets of
                 genes whose expression values in a tumour can be used
                 to predict the outcome of the pathology (gene
                 expression signatures) were introduced and tested by
                 many research groups. A well-known such signature is
                 the 70-genes signature, on which we recently tested
                 several machine learning techniques in order to
                 maximise its predictive power. Genetic Programming (GP)
                 was shown to perform significantly better than other
                 techniques including Support Vector Machines,
                 Multilayer Perceptrons, and Random Forests in
                 classifying patients. Genetic Programming has the
                 further advantage, with respect to other methods, of
                 performing an automatic feature selection. Importantly,
                 by using a weighted average between false positives and
                 false negatives in the definition of the fitness, we
                 showed that GP can outperform all the other methods in
                 minimising false negatives (one of the main goals in
                 clinical applications) without compromising the overall
                 minimization of incorrectly classified instances. The
                 solutions returned by GP are appealing also from a
                 clinical point of view, being simple, easy to
                 understand, and built out of a rather limited subset of
                 the available features.",
  language =     "English",
  notes =        "This is actually Mario Giacobini

                 a selection of the best papers presented at WIVACE
                 2012, Parma, Italy, thoroughly revised and extended by
                 the authors",
}

Genetic Programming entries for Mario Giacobini Paolo Provero Leonardo Vanneschi Giancarlo Mauri

Citations