NDLI: Dynamic Bayesian Networks in Modelling Cellular Systems: a Critical Appraisal on Simulated Data

Content Provider	IEEE Xplore Digital Library
Author	Ferrazzi, F. Sebastiani, P. Kohane, I.S. Ramoni, M.F. Bellazzi, R.
Copyright Year	2006
Description	Author affiliation: Dipartimento di Informatica e Sistemistica, Universita degli Studi di Pavia (Ferrazzi, F.)
Abstract	Dynamic Bayesian networks offer a powerful modelling tool to unravel cellular mechanisms. In particular, Gaussian networks have recently been used to model gene expression data, thanks to their capability to avoid information loss associated with discretization and their good computational efficiency. Gaussian networks typically describe the conditional mean of a node as a linear regression of the parent variables. Such model can be generalized by using a linear regression of nonlinear transformations of the parent values. In this paper we investigate the use of both models and evaluate the performance of Gaussian networks in learning the complex dynamic interactions among genes and proteins. To this aim, we analyzed simulated data produced by a mathematical model of cell cycle control in budding yeast. The results obtained allowed us to appraise the performance of the different models and confirmed the suitability of dynamic Bayesian networks for a first level, genome-wide analysis of high throughput dynamic data
Starting Page	544
Ending Page	549
File Size	241482
Page Count	6
File Format	PDF
ISBN	0769525171
ISSN	10637125
DOI	10.1109/CBMS.2006.81
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Cellular networks Appraisal Power system modeling Linear regression Computational modeling Gene expression Computational efficiency Proteins Data analysis
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

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