Genetic programming for human oral bioavailability of drugs

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Francesco Archetti and Stefano Lanzeni and 
                 Enza Messina and Leonardo Vanneschi",
  title =        "Genetic programming for human oral bioavailability of
  booktitle =    "{GECCO 2006:} Proceedings of the 8th annual conference
                 on Genetic and evolutionary computation",
  year =         "2006",
  editor =       "Maarten Keijzer and Mike Cattolico and Dirk Arnold and 
                 Vladan Babovic and Christian Blum and Peter Bosman and 
                 Martin V. Butz and Carlos {Coello Coello} and 
                 Dipankar Dasgupta and Sevan G. Ficici and James Foster and 
                 Arturo Hernandez-Aguirre and Greg Hornby and 
                 Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and 
                 Franz Rothlauf and Conor Ryan and Dirk Thierens",
  volume =       "1",
  ISBN =         "1-59593-186-4",
  pages =        "255--262",
  address =      "Seattle, Washington, USA",
  URL =          "",
  DOI =          "doi:10.1145/1143997.1144042",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, 10286-1405, USA",
  month =        "8-12 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Biological
                 Applications, bioavailability, bioinformatics,
                 complexity measures, molecular descriptors, performance
                 measures, SVM, ANN, LLSR, CFS, PCA, AIC, feature
                 selection, SMILES",
  size =         "8 pages",
  abstract =     "Automatically assessing the value of bioavailability
                 from the chemical structure of a molecule is a very
                 important issue in biomedicine and pharmacology. In
                 this paper, we present an empirical study of some well
                 known Machine Learning techniques, including various
                 versions of Genetic Programming, which have been
                 trained to this aim using a dataset of molecules with
                 known bioavailability. Genetic Programming has proven
                 the most promising technique among the ones that have
                 been considered both from the point of view of the
                 accurateness of the solutions proposed, of the
                 generalisation capabilities and of the correlation
                 between predicted data and correct ones. Our work
                 represents a first answer to the demand for
                 quantitative bioavailability estimation methods
                 proposed in literature, since the previous
                 contributions focus on the classification of molecules
                 into classes with similar bioavailability. Categories
                 and Subject Descriptors",
  notes =        "GECCO-2006 A joint meeting of the fifteenth
                 international conference on genetic algorithms
                 (ICGA-2006) and the eleventh annual genetic programming
                 conference (GP-2006).

                 ACM Order Number 910060

                 Winner best paper.",

Genetic Programming entries for Francesco Archetti Stefano Lanzeni Enza Messina Leonardo Vanneschi