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Evolution, Complexity and Artificial Life pp 177–192Cite as

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

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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 tumor 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 maximize 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 minimizing 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.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-642-37577-4_18

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Acknowledgments

This work was partially supported by Neuroscience Program of the Compagnia di San Paolo in Torino.

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Authors and Affiliations

  1. Computational Epidemiology Group, Department of Veterinary Sciences, and Complex Systems Unit, Molecular Biotechnology Center, University of Torino, Torino, Italy

    Marco Giacobini

  2. Molecular Biotechnology Center, University of Torino, Torino, Italy

    Paolo Provero

  3. ISEGI, Universidade Nova de Lisboa, 1070-312, Lisboa, Portugal

    Leonardo Vanneschi

  4. DISCo, University of Milano-Bicocca, 20126, Milan, Italy

    Leonardo Vanneschi & Giancarlo Mauri

Authors
  1. Marco Giacobini
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  2. Paolo Provero
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  3. Leonardo Vanneschi
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  4. Giancarlo Mauri
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Corresponding author

Correspondence to Marco Giacobini .

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Editors and Affiliations

  1. Dept. of Information Engineering, University of Parma, Parma, Italy

    Stefano Cagnoni

  2. Consiglio Nazionale delle Ricerche, Istituto di Scienze e Tecnologie della Cognizione, Rome, Italy

    Marco Mirolli

  3. Facoltà di Scienze della Comunicazione, University of Modena and Reggio Emilia, Reggio Emilia, Italy

    Marco Villani

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Giacobini, M., Provero, P., Vanneschi, L., Mauri, G. (2014). Towards the Use of Genetic Programming for the Prediction of Survival in Cancer. In: Cagnoni, S., Mirolli, M., Villani, M. (eds) Evolution, Complexity and Artificial Life. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37577-4_12

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  • DOI: https://doi.org/10.1007/978-3-642-37577-4_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37576-7

  • Online ISBN: 978-3-642-37577-4

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