research-article

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

Authors:

Antonella Farinaccio,

Leonardo Vanneschi,

Mario Giacobini,

Giancarlo Mauri,

Paolo ProveroAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

Pages 163 - 170

https://doi.org/10.1145/1830483.1830514

Published: 07 July 2010 Publication History

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 binarization 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 minimization 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.

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Cited By

Silva SAnunciação OLotz M(2011)A comparison of machine learning methods for the prediction of breast cancerProceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics10.5555/2008362.2008380(159-170)Online publication date: 27-Apr-2011
https://dl.acm.org/doi/10.5555/2008362.2008380
Silva SAnunciação OLotz M(2011)A Comparison of Machine Learning Methods for the Prediction of Breast CancerEvolutionary Computation, Machine Learning and Data Mining in Bioinformatics10.1007/978-3-642-20389-3_17(159-170)Online publication date: 2011
https://doi.org/10.1007/978-3-642-20389-3_17

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cover image ACM Conferences

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

July 2010

1520 pages

ISBN:9781450300728

DOI:10.1145/1830483

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

Copyright © 2010 ACM.

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Published: 07 July 2010

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July 7 - 11, 2010

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Cited By

Silva SAnunciação OLotz M(2011)A comparison of machine learning methods for the prediction of breast cancerProceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics10.5555/2008362.2008380(159-170)Online publication date: 27-Apr-2011
https://dl.acm.org/doi/10.5555/2008362.2008380
Silva SAnunciação OLotz M(2011)A Comparison of Machine Learning Methods for the Prediction of Breast CancerEvolutionary Computation, Machine Learning and Data Mining in Bioinformatics10.1007/978-3-642-20389-3_17(159-170)Online publication date: 2011
https://doi.org/10.1007/978-3-642-20389-3_17

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