Combining Decision Trees and Neural Networks for Drug Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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

Citations