On the use of genetic programming for the prediction of survival in cancer

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Farinaccio:2010:gecco,
  author =       "Antonella Farinaccio and Leonardo Vanneschi and 
                 Mario Giacobini and Giancarlo Mauri and Paolo Provero",
  title =        "On the use of genetic programming for the prediction
                 of survival in cancer",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "163--170",
  keywords =     "genetic algorithms, genetic programming,
                 Bioinformatics, computational, systems and synthetic
                 biology, SVM, ANN, MLP, voted percenptron, RBF",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830514",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The classification of cancer patients into risk
                 classes is a very active field of research, with direct
                 clinical applications. We have recently compared
                 several machine learning methods on the well known
                 70-genes signature dataset. In that study, genetic
                 programming showed promising results, given that it
                 outperformed all the other techniques. Nevertheless,
                 the study was preliminary, mainly because the
                 validation dataset was preprocessed and all its
                 features binarized in order to use logical operators
                 for the genetic programming functional nodes. If this
                 choice allowed simple interpretation of the solutions
                 from the biological viewpoint, on the other hand the
                 binarisation of data was limiting, since it amounts to
                 a sizable loss of information. The goal of this paper
                 is to overcome this limitation, using the 70-genes
                 signature dataset with real-valued expression data. The
                 results we present show that genetic programming using
                 the number of incorrectly classified instances as
                 fitness function is not able to outperform the other
                 machine learning methods. However, when a weighted
                 average between false positives and false negatives is
                 used to calculate fitness values, genetic programming
                 obtains performances that are comparable with the other
                 methods in the minimisation of incorrectly classified
                 instances and outperforms all the other methods in the
                 minimization of false negatives, which is one of the
                 main goals in breast cancer clinical applications. Also
                 in this case, the solutions returned by genetic
                 programming are simple, easy to understand, and they
                 use a rather limited subset of the available
                 features.",
  notes =        "NKI 70-gene breast cancer. p168 Implicit feature
                 selection. AF257175, NM_001809.

                 Also known as \cite{1830514} GECCO-2010 A joint meeting
                 of the nineteenth international conference on genetic
                 algorithms (ICGA-2010) and the fifteenth annual genetic
                 programming conference (GP-2010)",
}

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

Citations