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Can we use linear Gaussian networks to model dynamic interactions among genes? Results from a simulation study

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6 Author(s)
Ferrazzi, F. ; Dipt. di Inf. e Sist., Univ. degli Studi di Pavia, Pavia ; Amici, R. ; Sebastiani, P. ; Kohane, I.S.
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Dynamic Bayesian networks offer a powerful modeling tool to unravel cellular mechanisms. In particular, Linear Gaussian Networks allow researchers to avoid information loss associated with discretization and render the learning process computationally tractable even for hundreds of variables. Yet, are linear models suitable to learn the complex dynamic interactions among genes and proteins? We here present a study on simulated data produced by a mathematical model of cell cycle control in budding yeast: the results obtained confirmed the robustness of the linear model and its suitability for a first level, genome-wide analysis of high throughput dynamic data.

Published in:

Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on

Date of Conference:

28-30 May 2006