Structural analysis of regulatory DNA sequences using grammar inference and Support Vector Machine

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@Article{Damasevicius2010633,
  author =       "Robertas Damasevicius",
  title =        "Structural analysis of regulatory DNA sequences using
                 grammar inference and Support Vector Machine",
  journal =      "Neurocomputing",
  volume =       "73",
  number =       "4-6",
  pages =        "633--638",
  year =         "2010",
  note =         "Bayesian Networks / Design and Application of Neural
                 Networks and Intelligent Learning Systems (KES 2008 /
                 Bio-inspired Computing: Theories and Applications
                 (BIC-TA 2007)",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2009.09.018",
  URL =          "http://www.sciencedirect.com/science/article/B6V10-4XRYT4P-1/2/2e5b008bc8df4d5a39553b40fe6728c3",
  keywords =     "genetic algorithms, genetic programming, DNA sequence
                 analysis, Grammar inference, L-grammar, Support Vector
                 Machine, SVM",
  abstract =     "Regulatory DNA sequences such as promoters or splicing
                 sites control gene expression and are important for
                 successful gene prediction. Such sequences can be
                 recognized by certain patterns or motifs that are
                 conserved within a species. These patterns have many
                 exceptions which makes the structural analysis of
                 regulatory sequences a complex problem. Grammar rules
                 can be used for describing the structure of regulatory
                 sequences; however, the manual derivation of such rules
                 is not trivial. In this paper, stochastic L-grammar
                 rules are derived automatically from positive examples
                 and counterexamples of regulatory sequences using
                 genetic programming techniques. The fitness of grammar
                 rules is evaluated using a Support Vector Machine (SVM)
                 classifier. SVM is trained on known sequences to obtain
                 a discriminating function which serves for evaluating a
                 candidate grammar ruleset by determining the percentage
                 of generated sequences that are classified correctly.
                 The combination of SVM and grammar rule inference can
                 mitigate the lack of structural insight in machine
                 learning approaches such as SVM.",
  notes =        "TATA box",
}

Genetic Programming entries for Robertas Damasevicius

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