Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset
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- @Article{Archetti20101395,
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author = "Francesco Archetti and Ilaria Giordani and
Leonardo Vanneschi",
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title = "Genetic programming for anticancer therapeutic
response prediction using the NCI-60 dataset",
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journal = "Computers \& Operations Research",
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volume = "37",
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number = "8",
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pages = "1395--1405",
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year = "2010",
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note = "Operations Research and Data Mining in Biological
Systems",
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ISSN = "0305-0548",
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DOI = "doi:10.1016/j.cor.2009.02.015",
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URL = "http://www.sciencedirect.com/science/article/B6VC5-4VS40CF-4/2/a55e5b35bc3d30ac9057d5fb8cdcd2d0",
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keywords = "genetic algorithms, genetic programming, Machine
learning, Regression, Microarray data, Anticancer
therapy, NCI-60",
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abstract = "Statistical methods, and in particular machine
learning, have been increasingly used in the drug
development workflow. Among the existing machine
learning methods, we have been specifically concerned
with genetic programming. We present a genetic
programming-based framework for predicting anticancer
therapeutic response. We use the NCI-60 microarray
dataset and we look for a relationship between gene
expressions and responses to oncology drugs
Fluorouracil, Fludarabine, Floxuridine and Cytarabine.
We aim at identifying, from genomic measurements of
biopsies, the likelihood to develop drug resistance.
Experimental results, and their comparison with the
ones obtained by Linear Regression and Least Square
Regression, hint that genetic programming is a
promising technique for this kind of application.
Moreover, genetic programming output may potentially
highlight some relations between genes which could
support the identification of biological meaningful
pathways. The structures that appear more frequently in
the 'best' solutions found by genetic programming are
presented.",
- }
Genetic Programming entries for
Francesco Archetti
Ilaria Giordani
Leonardo Vanneschi
Citations