Combining Decision Trees and Neural Networks for Drug Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{langdon:2002:EuroGP,

• title = "Combining Decision Trees and Neural Networks for Drug Discovery",
• author = "William B. Langdon and S. J. Barrett and B. F. Buxton",
• editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
• booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002",
• volume = "2278",
• series = "LNCS",
• pages = "60--70",
• publisher = "Springer-Verlag",
• address = "Kinsale, Ireland",
• publisher_address = "Berlin",
• month = "3-5 " # apr,
• year = "2002",
• 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)",
• ISBN = "3-540-43378-3",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_egp2002.pdf",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_egp2002.ps.gz",
• DOI = "doi:10.1007/3-540-45984-7_6",
• size = "10 pages",
• 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.",
• notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",

}

Genetic Programming entries for William B Langdon S J Barrett Bernard Buxton

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