Genetic Programming for Predicting Protein Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{DBLP:conf/iberamia/GarciaALS08,
  author =       "Beatriz Garcia and Ricardo Aler and 
                 Agapito Ledezma and Araceli Sanchis",
  title =        "Genetic Programming for Predicting Protein Networks",
  booktitle =    "Proceedings of the 11th Ibero-American Conference on
                 AI, IBERAMIA 2008",
  year =         "2008",
  editor =       "Hector Geffner and Rui Prada and 
                 Isabel Machado Alexandre and Nuno David",
  volume =       "5290",
  series =       "Lecture Notes in Computer Science",
  pages =        "432--441",
  address =      "Lisbon, Portugal",
  month =        oct # " 14-17",
  publisher =    "Springer",
  note =         "Advances in Artificial Intelligence",
  keywords =     "genetic algorithms, genetic programming, Protein
                 interaction prediction, data integration,
                 bioinformatics, evolutionary computation, machine
                 learning, classification, control bloat",
  isbn13 =       "978-3-540-88308-1",
  URL =          "http://www.caos.inf.uc3m.es/~beatriz/papers/garcia_et.al._iberamia08-paper_InPress.pdf",
  DOI =          "doi:10.1007/978-3-540-88309-8_44",
  size =         "10 pages",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "One of the definitely unsolved main problems in
                 molecular biology is the protein-protein functional
                 association prediction problem. Genetic Programming
                 (GP) is applied to this domain. GP evolves an
                 expression, equivalent to a binary classifier, which
                 predicts if a given pair of proteins interacts. We take
                 advantages of GP flexibility, particularly, the
                 possibility of defining new operations. In this paper,
                 the missing values problem benefits from the definition
                 of if-unknown, a new operation which is more
                 appropriate to the domain data semantics. Besides, in
                 order to improve the solution size and the
                 computational time, we use the Tarpeian method which
                 controls the bloat effect of GP. According to the
                 obtained results, we have verified the feasibility of
                 using GP in this domain, and the enhancement in the
                 search efficiency and interpretability of solutions due
                 to the Tarpeian method.",
  notes =        "lilgp. BIND, DIP, Butland, IntAct, EcoCyc, KEGG, iHoP.
                 P436 Training 'instances is reduced to 264,752'
                 Actually 10000 used for training. Function set: +, -, *
                 and protected division, if, if_?. FS 'closed always
                 returning the unknown value ? if any of their input
                 values is ?'. Comparison with WEKA.",
}

Genetic Programming entries for Beatriz Garcia Ricardo Aler Mur Agapito Ledezma Araceli Sanchis

Citations