Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques
Created by W.Langdon from
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- @InProceedings{Vanneschi:2010:EvoBIO,
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author = "Leonardo Vanneschi and Antonella Farinaccio and
Mario Giacobini and Marco Antoniotti and Giancarlo Mauri and
Paolo Provero",
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title = "Identification of Individualized Feature Combinations
for Survival Prediction in Breast Cancer: A Comparison
of Machine Learning Techniques",
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booktitle = "8th European Conference on Evolutionary Computation,
Machine Learning and Data Mining in Bioinformatics,
EvoBIO 2010",
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year = "2010",
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editor = "Clara Pizzuti and Marylyn D. Ritchie and
Mario Giacobini",
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volume = "6023",
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series = "LNCS",
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pages = "110--121",
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address = "Istanbul",
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month = "7-9 " # apr,
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organisation = "EvoStar",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-12210-1",
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DOI = "doi:10.1007/978-3-642-12211-8_10",
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abstract = "The ability to accurately classify cancer patients
into risk classes, i.e. to predict the outcome of the
pathology on an individual basis, is a key ingredient
in making therapeutic decisions. In recent years gene
expression data have been successfully used to
complement the clinical and histological criteria
traditionally used in such prediction. Many gene
expression signatures have been developed, i.e. sets of
genes whose expression values in a tumor can be used to
predict the outcome of the pathology. Here we
investigate the use of several machine learning
techniques to classify breast cancer patients using one
of such signatures, the well established 70-gene
signature. We show that Genetic Programming performs
significantly better than Support Vector Machines,
Multilayered Perceptron and Random Forest in
classifying patients from the NKI breast cancer
dataset, and slightly better than the scoring-based
method originally proposed by the authors of the
seventy-gene signature. Furthermore, Genetic
Programming is able to perform an automatic feature
selection. Since the performance of Genetic Programming
is likely to be improvable compared to the
out-of-the-box approach used here, and given the
biological insight potentially provided by the Genetic
Programming solutions, we conclude that Genetic
Programming methods are worth further investigation as
a tool for cancer patient classification based on gene
expression data.",
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notes = "EvoBIO'2010 held in conjunction with EuroGP'2010
EvoCOP2010 and EvoApplications2010",
- }
Genetic Programming entries for
Leonardo Vanneschi
Antonella Farinaccio
Mario Giacobini
Marco Antoniotti
Giancarlo Mauri
Paolo Provero
Citations