Predicting Chemical Carcinogenesis Using Structural Information Only

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

@InProceedings{1999-kennedy-7,
  author =       "Claire J. Kennedy and Christophe Giraud-Carrier and 
                 Douglas W. Bristol",
  title =        "Predicting Chemical Carcinogenesis Using Structural
                 Information Only",
  booktitle =    "Third European Conference on the Principles of Data
                 Mining and Knowledge Discovery",
  ISBN =         "3-540-66490-4",
  publisher =    "Springer",
  pages =        "360--365",
  month =        sep,
  year =         "1999",
  editor =       "Jan Zytkow and Jan Rauch",
  volume =       "1704",
  series =       "Lecture Notes in Computer Science",
  keywords =     "genetic algorithms, genetic programming",
  abstract-url = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000393",
  URL =          "http://www.cs.bris.ac.uk/Publications/Papers/1000393.pdf",
  DOI =          "doi:10.1007/978-3-540-48247-5_43",
  pubtype =      "102",
  size =         "6 pages",
  abstract =     "This paper reports on the application of the Strongly
                 Typed Evolutionary Programming System (STEPS) to the
                 PTE2 challenge, which consists of predicting the
                 carcinogenic activity of chemical compounds from their
                 molecular structure and the outcomes of a number of
                 laboratory analyses. Most contestants so far have
                 relied heavily on results of short term toxicity (STT)
                 assays. Using both types of information made available,
                 most models incorporate attributes that make them
                 strongly dependent on STT results. Although such models
                 may prove to be accurate and informative, the use of
                 toxicological information requires time cost and in
                 some cases substantial use of laboratory animals. If
                 toxicological information only makes explicit,
                 properties implicit in the molecular structure of
                 chemicals, then provided a sufficiently expressive
                 representation language, accurate solutions may be
                 obtained from the structural information only. Such
                 solutions may offer more tangible insight into the
                 mechanistic paths and features that govern chemical
                 toxicity as well as prediction based on virtual
                 chemistry for the universe of compounds.",
  affiliation =  "Department of Computer Science, Merchant Venturers
                 Building, University of Bristol, Bristol, BS8 1UB
                 U.K.",
}

Genetic Programming entries for Claire J Kennedy Christophe Giraud-Carrier Douglas W Bristol

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