- 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)",
