title: A comparison of machine learning techniques for survival prediction in breast cancer.
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 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.
  
  
  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 dataset, 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 selection.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Computational Biology Unit, Molecular Biotechnology Center, University of Torino, Italy. paolo.provero@unito.it.
authors.forename: Leonardo
authors.initials: L
authors.lastname: Vanneschi
authors.forename: Antonella
authors.initials: A
authors.lastname: Farinaccio
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Mauri
authors.forename: Mauro
authors.initials: M
authors.lastname: Antoniotti
authors.forename: Paolo
authors.initials: P
authors.lastname: Provero
authors.forename: Mario
authors.initials: M
authors.lastname: Giacobini