Bayesian methods for elucidating genetic regulatory networks Amature free no email webchat

@MISC{Young_elucidatinggenetic, author = {Richard A. To demonstrate the utility of this framework for the elucidation of genetic regulatory networks, we apply these methods in the context of the Saccharomyces cerevisiae galactose regulatory system. We apply our scoring framework to validate models of regulatory networks in comparison with one another. We derive principled methods for scoring these annotated Bayesian networks.

These methods are well-suited to this problem owing to their ability to model more than pair-wise relationships between variables, their ability to guard against over-fitting, and their robustness in the face of noisy data.

These models can be scored in a principled manner in the presence of genomic expression data.

Jaakkola}, title = {Elucidating Genetic Regulatory Networks Using Graphical Models and Genomic Expression Data}, year = {}} We demonstrate how graphical models, and Bayesian networks in particular, can be used to model genetic regulatory networks.

IEEE intelligent systems & their applications ISSN 1094-7167 CODEN IISYF7 2002, vol.

; Bayesian network methods are useful for elucidating genetic regulatory networks because they can represent more than pair-wise relationships between variables, are resistant to overfitting, and remain robust in the face of noisy data.

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A key aim of systems biology is to unravel the regulatory interactions among genes and gene products in a cell.

Here we investigate a graphical model that treats the observed gene expression over time as realizations of random curves.

This approach is centered around an estimator of dynamical pairw ..." A key aim of systems biology is to unravel the regulatory interactions among genes and gene products in a cell.

This approach is centered around an estimator of dynamical pairwise correlation that takes account of the functional nature of the observed data.