Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-5

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p>ar model corresponding to = 1.8, as a function of the of the noise. = 0 corresponds to the noiseless case

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-8

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p> function at increasingly high values of

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-0

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p> of the parameter of the hyperbolic tangent function (continuous blue curves); the dashed red curves refer to the RMSE and average number of parents for the linear regression model. The dash-dotted green curve in (b) represents the average number of parents in the differential equation model (i.e. the average number of true parents). Further analyses showed that, for → + ∞, the RMSE saturates at 0.247, and the average number of parents saturates at 3.4

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-1

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p>, obtained using the nonlinear models corresponding to = 0.6 (a) and = 2 (b). Time is expressed in minutes

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-6

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p>model, as a function of the sampling interval (in minutes)

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-7

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p>ar model corresponding to = 0.8, as a function of the sampling interval (in minutes)

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-2

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p> of the hyperbolic tangent function (continuous blue curves). The dashed red curves refer to the recall, precision and F-measure of the linear regression model. Further analyses showed that, for → + ∞, recall saturates at 0.27, precision at 0.26, and the F-measure at 0.26

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-3

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p> to the number of parent-child relationships in the true model)

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-4

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p>model as a function of the of the noise. = 0 corresponds to the noiseless case

Methodology for gene classification by degree of reversibility upon smoking cessation

For each probeset, the relationship between gene expression in logscale (ge), age, current smoking status (x), former smoking status (x), and the interaction between former smoking status and months elapsed since quitting smoking (x) was examined with the linear regression model. Genes differentially expressed between current (C) and never (N) smokers were categorized based on their behavior in former smokers (F) relative to never smokers as a function of time since smoking cessation. Genes were classified as 'rapidly reversible' if there was not a significant difference between former and never smokers. Genes were classified as 'indeterminate' if there was a significant difference between former and never smokers, but the age-adjusted fold change between former and never smokers was not greater than or equal to 1.5. If the fold change criterion was met, genes were classified as 'slowly reversible' if there was a significant relationship between gene expression and time since quitting smoking or as 'irreversible' if there was not a significant relationship with time.<p><b>Copyright information:</b></p><p>Taken from "Reversible and permanent effects of tobacco smoke exposure on airway epithelial gene expression"</p><p>http://genomebiology.com/2007/8/9/R201</p><p>Genome Biology 2007;8(9):R201-R201.</p><p>Published online 25 Sep 2007</p><p>PMCID:PMC2375039.</p><p></p