Inference of gene regulatory networks using genetic programming and Kalman filter

被引：4
|
作者：
Wang, Haixin [1 ]
Qian, Lijun [1 ]
Dougherty, Edward [2 ]
机构：
[1] Prairie View A&M Univ, Dept Elect Engn, Prairie View, TX 77446 USA
[2] Texas A&M Univ, Dept Elect Engn, College Stn, TX 77843 USA
来源：
2006 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS | 2006年
关键词：
D O I：
10.1109/GENSIPS.2006.353139
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：27 / +
页数：2
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