Automatic Inferring Drug Gene Regulatory Networks with Missing Information Using Neural Networks and Genetic Programming

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@InProceedings{Floares:2008:ijcnn,
  author =       "Alexandru George Floares",
  title =        "Automatic Inferring Drug Gene Regulatory Networks with
                 Missing Information Using Neural Networks and Genetic
                 Programming",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "3078--3085",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1821-3",
  file =         "NN0852.pdf",
  DOI =          "doi:10.1109/IJCNN.2008.4634233",
  ISSN =         "1098-7576",
  abstract =     "Automatically inferring drug gene regulatory networks
                 models from microarray time series data is a
                 challenging task. The ordinary differential equations
                 models are sensible, but difficult to build. We
                 extended our reverse engineering algorithm for gene
                 networks (RODES), based on genetic programming, by
                 adding a neural networks feedback linearisation
                 component. Thus, RODES automatically discovers the
                 structure, estimate the parameter, and identify the
                 molecular mechanisms, even when information is missing
                 from the data. It produces systems of ordinary
                 differential equations from experimental or simulated
                 microarray time series data. On simulated data the
                 accuracy and the CPU time were very good. This is due
                 to reducing the reversing of an ordinary differential
                 equations system to that of individual algebraic
                 equations, and to the possibility of incorporating
                 common a priori knowledge. To our knowledge, this is
                 the first realistic reverse engineering algorithm,
                 based on genetic programming and neural networks,
                 applicable to large gene networks.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET. Also known as \cite{4634233}",
}

Genetic Programming entries for Alexandru Floares

Citations