Towards the Use of Genetic Programming for the Prediction of Survival in Cancer
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InCollection{Giacobini:2014:evcoal,
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author = "Marco Giacobini and Paolo Provero and
Leonardo Vanneschi and Giancarlo Mauri",
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title = "Towards the Use of Genetic Programming for the
Prediction of Survival in Cancer",
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booktitle = "Evolution, Complexity and Artificial Life",
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publisher = "Springer",
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year = "2014",
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editor = "Stefano Cagnoni and Marco Mirolli and Marco Villani",
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pages = "177--192",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-37576-7",
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URL = "http://dx.doi.org/10.1007/978-3-642-37577-4_12",
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DOI = "doi:10.1007/978-3-642-37577-4_12",
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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 tumour 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
maximise 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
minimising 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.",
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language = "English",
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notes = "This is actually Mario Giacobini
a selection of the best papers presented at WIVACE
2012, Parma, Italy, thoroughly revised and extended by
the authors",
- }
Genetic Programming entries for
Mario Giacobini
Paolo Provero
Leonardo Vanneschi
Giancarlo Mauri
Citations