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An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series

journal contribution
posted on 06.12.2017, 00:00 authored by Z Wang, Yurong LiuYurong Liu, J Liang, V Vinciotti
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.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

6

Issue

3

Start Page

410

End Page

419

Number of Pages

10

ISSN

1545-5963

Location

Piscataway, NJ

Publisher

Institute of Electrical and Electronics Engineers Inc.

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Brunel University; Dong nan da xue; Institute for Resource Industries and Sustainability (IRIS); Yangzhou da xue;

Era Eligible

Yes

Journal

IEEE