Therefore, although forecasting models share common aims, the specific scale at which each model has been developed strongly impacts its features and the parameters that are to be considered or neglected.
The models share common principles and values, but have a different focus and are differently detailed.
These group-practice models share common characteristics of patient rostering, age-sex based capitation rates, and performance-based incentives.
These models share common features of human disease such as anti-double-stranded DNA (anti-dsDNA) antibodies and proliferative nephritis, but differ in their renal cytokine/chemokine profile, cellular infiltration and acuity/chronicity of disease [ 5].
For reaction elements, attribute information about annotation, name, reactants and products for each reaction of the first SBML model are compared and matched to those of the reactions in the second SBML model, in order to identify whether the two SBML models share common reactions.
For reaction elements, attribute information about name, reactants and products for each reaction of the first SBML model are compared and matched to those of reactions in the second SBML model in order to identify whether the two SBML models share common reactions.
