Inference of gene regulatory networks using genetic programming and Kalman filter

Author

Wang, Haixin ; Qian, Lijun ; Dougherty, Edward

Author_Institution

Dept. of Electr. Eng., Prairie View A&M Univ., Prairie View, TX

fYear

2006

fDate

28-30 May 2006

Firstpage

27

Lastpage

28

Abstract

In this paper, gene regulatory networks are infered through evolutionary modeling and time-series microarray measurements. A nonlinear differential equation model is adopted and an iterative algorithm is proposed to identify the model, where genetic programming is applied to identify the structure of the model and Kalman filtering is employed to estimate the parameters in each iteration. Simulation results using synthetic data and microarray measurements show the effectiveness of the proposed scheme.

Keywords

Kalman filters; biology computing; genetic algorithms; genetics; iterative methods; nonlinear differential equations; parameter estimation; time series; Kalman filter; evolutionary modeling; gene regulatory network; genetic programming; iterative algorithm; nonlinear differential equation model; parameter estimation; time-series microarray measurement; Biological system modeling; DNA; Differential equations; Estimation error; Filtering; Gene expression; Genetic programming; Iterative algorithms; Kalman filters; Parameter estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on

Conference_Location

College Station, TX

Print_ISBN

1-4244-0384-7

Electronic_ISBN

1-4244-0385-5

Type

conf

DOI

10.1109/GENSIPS.2006.353139

Filename

4161760