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

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 minimize 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. 2007 IEEE.

name of conference

• Third International Conference on Natural Computation (ICNC 2007)

authors

• Dougherty, Edward

published proceedings

• Third International Conference on Natural Computation (ICNC 2007)

author list (cited authors)

• Wang, H., Qian, L., & Dougherty, E.

citation count

• 5

complete list of authors

• Wang, Haixin||Qian, Lijun||Dougherty, Edward

publication date

• August 2007

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Bioengineering
• Genetics

Digital Object Identifier (DOI)

• 10.1109/icnc.2007.478

International Standard Book Number (ISBN) 10

• 0769528759

International Standard Book Number (ISBN) 13

• 9780769528755

start page

• 324

end page

• 328

volume

• 2

URL

• http://dx.doi.org/10.1109/icnc.2007.478