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Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarray Gene Expression Measurements

Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarray Gene Expression Measurements

Yang Dai, Eyad Almasri, Peter Larsen, Guanrao Chen
Copyright: © 2010 |Pages: 21
ISBN13: 9781605666853|ISBN10: 1605666858|EISBN13: 9781605666860
DOI: 10.4018/978-1-60566-685-3.ch012
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MLA

Dai, Yang, et al. "Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarray Gene Expression Measurements." Handbook of Research on Computational Methodologies in Gene Regulatory Networks, edited by Sanjoy Das, et al., IGI Global, 2010, pp. 289-309. https://doi.org/10.4018/978-1-60566-685-3.ch012

APA

Dai, Y., Almasri, E., Larsen, P., & Chen, G. (2010). Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarray Gene Expression Measurements. In S. Das, D. Caragea, S. Welch, & W. Hsu (Eds.), Handbook of Research on Computational Methodologies in Gene Regulatory Networks (pp. 289-309). IGI Global. https://doi.org/10.4018/978-1-60566-685-3.ch012

Chicago

Dai, Yang, et al. "Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarray Gene Expression Measurements." In Handbook of Research on Computational Methodologies in Gene Regulatory Networks, edited by Sanjoy Das, et al., 289-309. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-685-3.ch012

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Abstract

The reconstruction of genetic regulatory networks from microarray gene expression measurements has been a challenging problem in bioinformatics. Various methods have been proposed for this problem including the Bayesian Network (BN) approach. In this chapter, we provide a comprehensive survey of the current development of using structure priors derived from high-throughput experimental results such as protein-protein interactions, transcription factor binding location data, evolutionary relationships, and literature database in learning regulatory networks.

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