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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE Symposium on Computer-Based Medical Systems |
| Pages | 544-549 |
| Number of pages | 6 |
| Volume | 2006 |
| DOIs | |
| Publication status | Published - 2006 |
| Event | 19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006 - Salt Lake City, UT, United States Duration: Jun 22 2006 → Jun 23 2006 |
Other
| Other | 19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006 |
|---|---|
| Country | United States |
| City | Salt Lake City, UT |
| Period | 6/22/06 → 6/23/06 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Dynamic Bayesian networks in modelling cellular systems : A critical appraisal on simulated data. / Ferrazzi, Fulvia; Sebastiani, Paola; Kohane, Isaac S.; Ramoni, Marco F.; Bellazzi, Riccardo.
Proceedings - IEEE Symposium on Computer-Based Medical Systems. Vol. 2006 2006. p. 544-549 1647627.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Dynamic Bayesian networks in modelling cellular systems
T2 - A critical appraisal on simulated data
AU - Ferrazzi, Fulvia
AU - Sebastiani, Paola
AU - Kohane, Isaac S.
AU - Ramoni, Marco F.
AU - Bellazzi, Riccardo
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33845584323&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845584323&partnerID=8YFLogxK
U2 - 10.1109/CBMS.2006.81
DO - 10.1109/CBMS.2006.81
M3 - Conference contribution
SN - 0769525172
SN - 9780769525174
VL - 2006
SP - 544
EP - 549
BT - Proceedings - IEEE Symposium on Computer-Based Medical Systems
ER -