Comparison of AdaBoost and Genetic Programming for combining Neural Networks for Drug Discovery

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

@InProceedings{langdon:2003:evowks,
  title =        "Comparison of {AdaBoost} and Genetic Programming for
                 combining Neural Networks for Drug Discovery",
  author =       "W. B. Langdon and S. J. Barrett and B. F. Buxton",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP},
                 Evo{MUSART}, Evo{ROB}, Evo{STIM}",
  editor =       "G{\"u}nther R. Raidl and Stefano Cagnoni and 
                 Juan Jes\'us Romero Cardalda and David W. Corne and 
                 Jens Gottlieb and Agn\`es Guillot and Emma Hart and 
                 Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and 
                 Martin Middendorf",
  volume =       "2611",
  series =       "LNCS",
  pages =        "87--98",
  address =      "University of Essex, UK",
  publisher =    "Springer-Verlag",
  publisher_address = "Berlin",
  month =        "14-16 " # apr,
  organisation = "EvoNet",
  year =         "2003",
  keywords =     "genetic algorithms, genetic programming, adaboost,
                 drug design, Receiver Operating Characteristics (ROC),
                 ensemble of classifiers, data fusion, artificial neural
                 networks, clementine, high through put screening
                 (HTS)",
  isbn13 =       "978-3-540-00976-4",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_evobio2003.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_evobio2003.ps.gz",
  DOI =          "doi:10.1007/3-540-36605-9_9",
  size =         "12 pages",
  abstract =     "Genetic programming (GP) based data fusion and
                 AdaBoost can both improve in vitro prediction of
                 Cytochrome P450 activity by combining artificial neural
                 networks (ANN). Pharmaceutical drug design data
                 provided by high throughput screening (HTS) is used to
                 train many base ANN classifiers. In data mining (KDD)
                 we must avoid over fitting. The ensembles do
                 extrapolate from the training data to other unseen
                 molecules. I.e. they predict inhibition of a P450
                 enzyme by compounds unlike the chemicals used to train
                 them. Thus the models might provide in silico screens
                 of virtual chemicals as well as physical ones from
                 GlaxoSmithKline (GSK)'s cheminformatics database. The
                 receiver operating characteristics (ROC) of boosted and
                 evolved ensemble are given.",
  notes =        "EvoWorkshops2003",
}

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

Citations