Modeling Genetic Regulatory Networks by Sigmoidal Functions: A Joint Genetic Algorithm and Kalman Filtering Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Wang:2007:ICNC,
  author =       "Haixin Wang and Lijun Qian and E. Dougherty",
  title =        "Modeling Genetic Regulatory Networks by Sigmoidal
                 Functions: A Joint Genetic Algorithm and Kalman
                 Filtering Approach",
  booktitle =    "Third International Conference on Natural Computation,
                 ICNC 2007",
  year =         "2007",
  volume =       "2",
  pages =        "324--328",
  address =      "Haikou",
  month =        "24-27 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-7695-2875-5",
  URL =          "http://old.pvamu.edu/edir/lijun/files/papers/ICNC2007.pdf",
  DOI =          "doi:10.1109/ICNC.2007.478",
  size =         "5 pages",
  abstract =     "In this paper, the problem of genetic regulatory
                 network inference from time series microarray
                 experiment data is considered. A noisy sigmoidal model
                 is proposed to include both system noise and
                 measurement noise. In order to solve this nonlinear
                 identification problem (with noise), a joint genetic
                 algorithm and Kalman filtering approach is proposed.
                 Genetic algorithm is applied to minimise the fitness
                 function and Kalman filter is employed to estimate the
                 weight parameters in each iteration. The effectiveness
                 of the proposed method is demonstrated by using both
                 synthetic data and microarray measurements.",
  notes =        "

                 INSPEC Accession Number: 9873877 Prairie View A&M
                 Univ., Prairie View;",
}

Genetic Programming entries for Haixin Wang Lijun Qian Edward R Dougherty

Citations