Combining Decision Trees and Neural Networks for Drug Discovery
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{langdon:2002:EuroGP,
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title = "Combining Decision Trees and Neural Networks for Drug
Discovery",
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author = "William B. Langdon and S. J. Barrett and
B. F. Buxton",
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editor = "James A. Foster and Evelyne Lutton and
Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
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booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
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volume = "2278",
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series = "LNCS",
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pages = "60--70",
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publisher = "Springer-Verlag",
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address = "Kinsale, Ireland",
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publisher_address = "Berlin",
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month = "3-5 " # apr,
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year = "2002",
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keywords = "genetic algorithms, genetic programming, drug design,
Receiver Operating Characteristics (ROC), ensemble of
classifiers, data fusion, artificial neural networks,
clementine, decision trees C4.5, high through put
screening (HTS)",
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ISBN = "3-540-43378-3",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_egp2002.pdf",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_egp2002.ps.gz",
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DOI = "doi:10.1007/3-540-45984-7_6",
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size = "10 pages",
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abstract = "Genetic programming (GP) offers a generic method of
automatically fusing together classifiers using their
receiver operating characteristics (ROC) to yield
superior ensembles. We combine decision trees (C4.5)
and artificial neural networks (ANN) on a difficult
pharmaceutical data mining (KDD) drug discovery
application. Specifically predicting inhibition of a
P450 enzyme. Training data came from high throughput
screening (HTS) runs. The evolved model may be used to
predict behaviour of virtual (i.e. yet to be
manufactured) chemicals. Measures to reduce over
fitting are also described.",
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notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
- }
Genetic Programming entries for
William B Langdon
S J Barrett
Bernard Buxton
Citations