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- W2501863942 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." @default.
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- W2501863942 date "2010-01-01" @default.
- W2501863942 modified "2023-10-16" @default.
- W2501863942 title "Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model" @default.
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- W2501863942 doi "https://doi.org/10.4018/978-1-60566-685-3.ch005" @default.
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