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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4053

Title: An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series
Authors: Wang, Z
Liu, X
Liu, Y
Liang, Y
Vinciotti, V
Keywords: Modeling
Clustering
DNA Microarray Technology
Extended Kalman Filtering
Gene Expression
Time Series Data
Publication Date: 2009
Publisher: IEEE
Citation: IEEE/ACM Transactions on Computational Biology and Bioinformatics. 6 (3): 410-419
Abstract: 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.
Description: Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
URI: http://bura.brunel.ac.uk/handle/2438/4053
DOI: http://dx.doi.org/10.1109/TCBB.2009.5
ISSN: 1545-5963
Appears in Collections:School of Information Systems, Computing and Mathematics Research Papers
Computer Science

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