Modeling the time accumulated risk associated with hospital stay has allowed the development of a class of predictive models that, when combined with simple disease data appears to substantially outperform many disease specific models alone.
Therefore, many specific features regarding these models have been studied in many works such as the presence of bifurcations [2, 3], the construction of approximate solutions [4, 5], the fractional dynamics [6], the existence of equilibrium points and oscillatory behavior [7], and the presence of waves [8 10], to cite but a few.
A multicondition model would also be more practically useful compared to a spectrum of disease specific models, as many patients have multiple comorbidities.
Unlike many other site-specific models, our model does not require a database of material properties which form obstacles in a scenario as the material properties are replaced by a few probabilistic parameters which are optimised by means of a measurement campaign.
While a metabolic reconstruction is unique to the target organism one can derive many different condition-specific models from a single reconstruction.
Because the branch models average the ω ratio over all sites and were unable to detect a positive signal in many cases, site-specific models allowing ω to vary among sites [ 50, 51] were subsequently used to determine whether particular amino-acid residues within FDS gene families have been subject to positive selection.
Neither specific models nor configurations were divulged.
Numerous studies have shown the ability of the site-specific and the branch-site specific models to detect positive selection in cases where the branch-specific models did not, indicating that averaging over sites is generally a more serious problem than averaging over lineages and that in many cases using a branch-site specific model increases the power to detect positive selection [ 17- 22].
Many issues of experimental design, including specific model, strain, gender, atherogenic stimulus, duration of study, group size, and statistical analysis may influence the outcome and interpretation of atherosclerosis studies.
