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

Fulvia Ferrazzi, Roberta Amici, Paola Sebastiani, Isaac S. Kohane, Marco F. Ramoni, Riccardo Bellazzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006
Pages13-14
Number of pages2
DOIs
Publication statusPublished - 2006
Event2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006 - College Station, TX, United States
Duration: May 28 2006May 30 2006

Other

Other2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006
CountryUnited States
CityCollege Station, TX
Period5/28/065/30/06

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Statistics and Probability

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    Ferrazzi, F., Amici, R., Sebastiani, P., Kohane, I. S., Ramoni, M. F., & Bellazzi, R. (2006). Can we use linear Gaussian networks to model dynamic interactions among genes? Results from a simulation study. In 2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006 (pp. 13-14). [4161753] https://doi.org/10.1109/GENSIPS.2006.353132