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Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model

Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model

Svetlana Bulashevska
Copyright: © 2010 |Pages: 31
ISBN13: 9781605666853|ISBN10: 1605666858|EISBN13: 9781605666860
DOI: 10.4018/978-1-60566-685-3.ch005
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MLA

Bulashevska, Svetlana. "Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model." Handbook of Research on Computational Methodologies in Gene Regulatory Networks, edited by Sanjoy Das, et al., IGI Global, 2010, pp. 108-138. https://doi.org/10.4018/978-1-60566-685-3.ch005

APA

Bulashevska, S. (2010). Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model. In S. Das, D. Caragea, S. Welch, & W. Hsu (Eds.), Handbook of Research on Computational Methodologies in Gene Regulatory Networks (pp. 108-138). IGI Global. https://doi.org/10.4018/978-1-60566-685-3.ch005

Chicago

Bulashevska, Svetlana. "Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model." In Handbook of Research on Computational Methodologies in Gene Regulatory Networks, edited by Sanjoy Das, et al., 108-138. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-685-3.ch005

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Abstract

This chapter describes the model of genetic regulatory interactions. The model has a Boolean logic semantics representing the cooperative influence of regulators (activators and inhibitors) on the expression of a gene. The model is a probabilistic one, hence allowing for the statistical learning to infer the genetic interactions from microarray gene expression data. Bayesian approach to model inference is employed enabling flexible definitions of a priori probability distributions of the model parameters. Markov Chain Monte Carlo (MCMC) simulation technique Gibbs sampling is used to facilitate Bayesian inference. The problem of identifying actual regulators of a gene from a high number of potential regulators is considered as a Bayesian variable selection task. Strategies for the definition of parameters reducing the parameter space and efficient MCMC sampling methods are the matter of the current research.

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