Modeling Gene Regulatory Network Based on Genetic Programming

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@InProceedings{Liu:2010:ICECE,
  author =       "Jing Liu2 and Aiguo Wu",
  title =        "Modeling Gene Regulatory Network Based on Genetic
                 Programming",
  booktitle =    "2010 International Conference on Electrical and
                 Control Engineering (ICECE)",
  year =         "2010",
  month =        jun,
  pages =        "5341--5344",
  abstract =     "The purpose to establish the gene regulatory network
                 model is to study the interaction relationship between
                 genes on the system level, and thus to understand the
                 essentials of creatures activity. Currently, the
                 mainstream methods for modelling either prerequire the
                 regulatory relationship or are not able to demonstrate
                 the dynamics of the regulatory network. This paper
                 proposes a model based on differential equation, to
                 study gene regulatory network using Genetic
                 Programming. This method is able to adjust to
                 continuously external changes; search for the
                 regulatory models suitable for the experiment data
                 using genetic operators; and realise the prediction for
                 the random regulatory relationship between genes. Based
                 on the experiment, and compared with linear
                 differential equation model, the calculation result is
                 better suitable for the experiment data. This method is
                 sufficient for the gene regulatory network structure
                 reconstruction.",
  keywords =     "genetic algorithms, genetic programming, gene
                 regulatory network modelling, gene regulatory network
                 structure reconstruction, linear differential equation
                 model, biology, linear differential equations",
  DOI =          "doi:10.1109/iCECE.2010.1296",
  notes =        "In chinese. Also known as \cite{5630756}",
}

Genetic Programming entries for Jing Liu2 Aiguo Wu

Citations