Inference of Noisy Nonlinear Differential Equation Models for Gene Regulatory Networks Using Genetic Programming and Kalman Filtering

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@Article{Qian:2008:ieeeTSP,
  author =       "Lijun Qian and Haixin Wang and Edward R. Dougherty",
  title =        "Inference of Noisy Nonlinear Differential Equation
                 Models for Gene Regulatory Networks Using Genetic
                 Programming and Kalman Filtering",
  journal =      "IEEE Transactions on Signal Processing",
  year =         "2008",
  month =        jul,
  volume =       "56",
  number =       "7",
  pages =        "3327--3339",
  keywords =     "genetic algorithms, genetic programming, Kalman
                 filtering, biological regulation, evolutionary
                 modeling, gene regulatory networks, genetic-based
                 diseases, genomic signal processing, intrinsic noise,
                 iterative algorithm, model identification, noisy
                 nonlinear differential equation model, phenotypic
                 determination, random noise parameters, time-series
                 microarray measurement, Kalman filters, nonlinear
                 differential equations, signal processing",
  DOI =          "doi:10.1109/TSP.2008.919638",
  ISSN =         "1053-587X",
  size =         "13 pages",
  abstract =     "A key issue in genomic signal processing is the
                 inference of gene regulatory networks. These are used
                 both to understand the role of biological regulation in
                 phenotypic determination and to derive therapeutic
                 strategies for genetic-based diseases. In this paper,
                 gene regulatory networks are inferred via evolutionary
                 modeling based on time-series microarray measurements.
                 A nonlinear differential equation model is adopted. It
                 includes random noise parameters for intrinsic noise
                 arising from stochasticity in transcription and
                 translation and for external noise arising from factors
                 such as the amount of RNA polymerase, levels of
                 regulatory proteins, and the effects of mRNA and
                 protein degradation. An iterative algorithm is proposed
                 for model identification. Genetic programming is
                 applied to identify the structure of the model and
                 Kalman filtering is used to estimate the parameters in
                 each iteration. Both standard and robust Kalman
                 filtering are considered. The effectiveness of the
                 proposed scheme is demonstrated by using synthetic data
                 and by using microarray measurements pertaining to
                 yeast protein synthesis.",
  notes =        "fig 10: Interactions among the 12 genes of yeast. time
                 series microarray. Also known as \cite{4531193}
                 \cite{journals/tsp/QianWD08}",
}

Genetic Programming entries for Lijun Qian Haixin Wang Edward R Dougherty

Citations