Inference of Genetic Regulatory Networks by Evolutionary Algorithm and $H_\infty$ Filtering

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@InProceedings{Qian:2007:SSP,
  author =       "Lijun Qian and Haixin Wang",
  title =        "Inference of Genetic Regulatory Networks by
                 Evolutionary Algorithm and {$H{_\infty}$} Filtering",
  booktitle =    "14th IEEE/SP Workshop on Statistical Signal
                 Processing, SSP '07",
  year =         "2007",
  pages =        "21--25",
  address =      "Madison, WI, USA",
  month =        "26-29 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-1198-6",
  URL =          "http://old.pvamu.edu/edir/lijun/files/papers/SSP2007.pdf",
  DOI =          "doi:10.1109/SSP.2007.4301210",
  size =         "5 pages",
  abstract =     "The correct inference of genetic regulatory networks
                 plays a critical role in understanding biological
                 regulation in phenotypic determination and it can
                 affect advanced genome-based therapeutics. In this
                 study, we propose a joint evolutionary algorithm and
                 Hinfinity filtering approach to infer genetic
                 regulatory networks using noisy time series data from
                 microarray measurements. Specifically, an iterative
                 algorithm is proposed where genetic programming is
                 applied to identify the structure of the model and H
                 infinity filtering is used to estimate the parameters
                 in each iteration. The proposed method can obtain
                 accurate dynamic nonlinear ordinary differential
                 equation (ODE) model of genetic regulatory networks
                 even when the noise statistics is unknown. Both
                 synthetic data and experimental data from microarray
                 measurements are used to demonstrate the effectiveness
                 of the proposed method. With the increasing
                 availability of time series microarray data, the
                 algorithm developed in this paper could be applied to
                 construct models to characterise cancer evolution and
                 serve as the basis for developing new regulatory
                 therapies.",
  notes =        "Department of Electrical and Computer Engineering,
                 Prairie View A&M University, Prairie View, Texas
                 77446.",
}

Genetic Programming entries for Lijun Qian Haixin Wang

Citations