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Inference of gene regulatory networks using genetic programming and Kalman filter

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3 Author(s)
Haixin Wang ; Dept. of Electr. Eng., Prairie View A&M Univ., Prairie View, TX ; Lijun Qian ; Edward Dougherty

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.

Published in:

2006 IEEE International Workshop on Genomic Signal Processing and Statistics

Date of Conference:

28-30 May 2006