Classification of Oncologic Data with Genetic Programming
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gp-bibliography.bib Revision:1.8098
- @Article{Vanneschi:2009:JAEA,
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title = "Classification of Oncologic Data with Genetic
Programming",
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author = "Leonardo Vanneschi and Francesco Archetti and
Mauro Castelli and Ilaria Giordani",
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journal = "Journal of Artificial Evolution and Applications",
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year = "2009",
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volume = "2009",
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publisher = "Hindawi Publishing Corporation",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://downloads.hindawi.com/journals/jaea/2009/848532.pdf",
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DOI = "doi:10.1155/2009/848532",
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ISSN = "16876229",
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bibsource = "OAI-PMH server at www.doaj.org",
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language = "eng",
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oai = "oai:doaj-articles:809187cab9ca01fd2c12625e6010851b",
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abstract = "Discovering the models explaining the hidden
relationship between genetic material and tumor
pathologies is one of the most important open
challenges in biology and medicine. Given the large
amount of data made available by the DNA Microarray
technique, Machine Learning is becoming a popular tool
for this kind of investigations. In the last few years,
we have been particularly involved in the study of
Genetic Programming for mining large sets of biomedical
data. In this paper, we present a comparison between
four variants of Genetic Programming for the
classification of two different oncologic datasets: the
first one contains data from healthy colon tissues and
colon tissues affected by cancer; the second one
contains data from patients affected by two kinds of
leukemia (acute myeloid leukemia and acute
lymphoblastic leukemia). We report experimental results
obtained using two different fitness criteria: the
receiver operating characteristic and the percentage of
correctly classified instances. These results, and
their comparison with the ones obtained by three
nonevolutionary Machine Learning methods (Support
Vector Machines, MultiBoosting, and Random Forests) on
the same data, seem to hint that Genetic Programming is
a promising technique for this kind of
classification.",
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notes = "Article ID 848532",
- }
Genetic Programming entries for
Leonardo Vanneschi
Francesco Archetti
Mauro Castelli
Ilaria Giordani
Citations