An Extended Kalman Filtering Approach to Modeling Nonlinear Dynamic Gene Regulatory Networks via Short Gene Expression Time Series

作者：Wang Zidong^*; Liu Xiaohui; Liu Yurong; Liang Jinling; Vinciotti Veronica

来源：IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2009, 6(3): 410-419.

DOI：10.1109/TCBB.2009.5

• Modeling
• clustering
• DNA microarray technology
• extended Kalman filtering
• gene expression
• time series data

In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.

• 出版日期2009-9
• 单位扬州大学; 东南大学