Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Vanneschi:2010:EvoBIO,
  author =       "Leonardo Vanneschi and Antonella Farinaccio and 
                 Mario Giacobini and Marco Antoniotti and Giancarlo Mauri and 
                 Paolo Provero",
  title =        "Identification of Individualized Feature Combinations
                 for Survival Prediction in Breast Cancer: A Comparison
                 of Machine Learning Techniques",
  booktitle =    "8th European Conference on Evolutionary Computation,
                 Machine Learning and Data Mining in Bioinformatics,
                 EvoBIO 2010",
  year =         "2010",
  editor =       "Clara Pizzuti and Marylyn D. Ritchie and 
                 Mario Giacobini",
  volume =       "6023",
  series =       "LNCS",
  pages =        "110--121",
  address =      "Istanbul",
  month =        "7-9 " # apr,
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-12210-1",
  DOI =          "doi:10.1007/978-3-642-12211-8_10",
  abstract =     "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 tumor 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. We show that Genetic Programming performs
                 significantly better than Support Vector Machines,
                 Multilayered Perceptron and Random Forest in
                 classifying patients from the NKI breast cancer
                 dataset, and slightly better than the scoring-based
                 method originally proposed by the authors of the
                 seventy-gene signature. Furthermore, Genetic
                 Programming is able to perform an automatic feature
                 selection. 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.",
  notes =        "EvoBIO'2010 held in conjunction with EuroGP'2010
                 EvoCOP2010 and EvoApplications2010",
}

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

Citations