A genetic programming method for protein motif discovery and protein classification

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@Article{journals/soco/TsunodaFL11,
  author =       "Denise Fukumi Tsunoda and Alex Alves Freitas and 
                 Heitor Silverio Lopes",
  title =        "A genetic programming method for protein motif
                 discovery and protein classification",
  journal =      "Soft Computing - A Fusion of Foundations,
                 Methodologies and Applications",
  year =         "2011",
  volume =       "15",
  number =       "10",
  pages =        "1897--1908",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 algorithms, data mining, proteins patterns discovery",
  ISSN =         "1432-7643",
  DOI =          "doi:10.1007/s00500-010-0624-9",
  size =         "12 pages",
  abstract =     "Proteins can be grouped into families according to
                 some features such as hydrophobicity, composition or
                 structure, aiming to establish common biological
                 functions. This paper presents MAHATMA memetic
                 algorithm based highly adapted tool for motif
                 ascertainment-a system that was conceived to discover
                 features (particular sequences of amino acids, or
                 motifs) that occur very often in proteins of a given
                 family but rarely occur in proteins of other families.
                 These features can be used for the classification of
                 unknown proteins, that is, to predict their function by
                 their primary structure. Experiments were done with a
                 set of enzymes extracted from the Protein Data Bank.
                 The heuristic method used was based on genetic
                 programming using operators specially tailored for the
                 target problem. The final performance was measured
                 using sensitivity, specificity and hit rate. The best
                 results obtained for the enzyme dataset suggest that
                 the proposed evolutionary computation method is
                 effective in finding predictive features (motifs) for
                 protein classification.",
  notes =        "PDB, MAHATMA From the issue entitled Special Issue on
                 Intelligent Systems, Design and Applications (ISDA
                 2009)",
  affiliation =  "Federal University of Parana, Av. Prefeito Lothario
                 Meissner, 632, Room 38, Curitiba, PR, Brazil",
  bibdate =      "2011-09-14",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/soco/soco15.html#TsunodaFL11",
}

Genetic Programming entries for Denise Fukumi Tsunoda Alex Alves Freitas Heitor Silverio Lopes

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