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Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarray Gene Expression Measurements
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Author(s): Yang Dai (University of Illinois at Chicago, USA), Eyad Almasri (University of Illinois at Chicago, USA), Peter Larsen (University of Illinois at Chicago, USA)and Guanrao Chen (University of Illinois at Chicago, USA)
Copyright: 2010
Pages: 21
Source title:
Handbook of Research on Computational Methodologies in Gene Regulatory Networks
Source Author(s)/Editor(s): Sanjoy Das (Kansas State University, USA), Doina Caragea (Kansas State University, USA), Stephen Welch (Kansas State University, USA)and William H. Hsu (Kansas State University, USA)
DOI: 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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