A neural networks algorithm for inferring drug gene regulatory networks from microarray time-series with missing transcription factors information

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@InProceedings{Floares:2009:IJCNN,
  author =       "Alexandru George Floares",
  title =        "A neural networks algorithm for inferring drug gene
                 regulatory networks from microarray time-series with
                 missing transcription factors information",
  booktitle =    "International Joint Conference on Neural Networks,
                 IJCNN 2009",
  year =         "2009",
  month =        jun,
  pages =        "848--854",
  keywords =     "genetic algorithms, genetic programming, algebraic
                 equations, drug gene regulatory networks, feedback
                 linearization, mathematical modeling, microarray
                 time-series, missing transcription factors information,
                 neural networks algorithm, ordinary differential
                 equations, reverse engineering algorithm, algebra,
                 biology computing, data handling, differential
                 equations, neural nets, reverse engineering, time
                 series",
  DOI =          "doi:10.1109/IJCNN.2009.5179081",
  ISSN =         "1098-7576",
  abstract =     "Mathematical modeling gene regulatory networks is
                 important for understanding and controlling them, with
                 various drugs and their dosage. The ordinary
                 differential equations approach is sensible but also
                 very difficult. Our reverse engineering algorithm
                 (RODES), based on neural networks feedback
                 linearization and genetic programming, takes as inputs
                 high-throughput (e.g., microarray) time series data and
                 automatically infer an accurate ordinary differential
                 equations model. The algorithm decouples the systems of
                 differential equations, reducing the problem to that of
                 revere engineering individual algebraic equations, and
                 is able to deal with missing information,
                 reconstructing the temporal series of the transcription
                 factors or drug related compounds which are usually
                 missing in microarray experiments. It is also able to
                 incorporate 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.",
  notes =        "Also known as \cite{5179081}",
}

Genetic Programming entries for Alexandru Floares

Citations