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Stochastic dynamic modeling of short gene expression time-series data

journal contribution
posted on 06.12.2017, 00:00 by Z Wang, Fuwen YangFuwen Yang, D Ho, S Swift, A Tucker, X Liu
In this paper, the expectation maximization (EM) algorithm is applied for modeling the gene regulatory network from gene time-series data. The gene regulatory network is viewed as a stochastic dynamic model, which consists of the noisy gene measurement from microarray and the gene regulation first-order autoregressive (AR) stochastic dynamic process. By using the EM algorithm, both the model parameters and the actual values of the gene expression levels can be identified simultaneously. Moreover, the algorithm can deal with the sparse parameter identification and the noisy data in an efficient way. It is also shown that the EM algorithm can handle the microarrary gene expression data with large number of variables but a small number of observations. The gene expression stochastic dynamic models for four real-world gene expression data sets are constructed to demonstrate the advantages of the introduced algorithm. Several indices are proposed to evaluate the models of inferred gene regulatory networks, and the relevant biological properties are discussed.

Funding

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

History

Volume

7

Issue

1

Start Page

44

End Page

55

Number of Pages

12

ISSN

1536-1241

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Brunel University; City University of Hong Kong; TBA Research Institute;

Era Eligible

Yes

Journal

IEEE transactions on nanobioscience.