Sentence examples for model composition in from inspiring English sources
In fact, the sustained robustness of the TIMI STEMI score, despite its simplicity, lends considerable support to the importance of variable selection and model composition in the development of risk scores.
According to the Akaike's information criterion through Markov chain Monte Carlo simulation (AICM) [ 43] results obtained with the TRACER v1.6 program [ 44], the best model composition in both datasets was SRD06-uced-BSP.
Once the proposal is approved and LibSBML has the accompanying features, we plan to provide support for the SBML level 3 hierarchical model compositions in our Model Composition Interface.
Discussion of the architectural advantages of PathCase-SB as applied to the Model Composition Interface in terms of s upport for multiple simulation engine use.
The new features of the proposed Model Composition Tool are: All-in-one hierarchical model composition capability and details of how it works.
Therefore, the objective of our research was to examine the influence of bootstrapping and multiple imputation on model composition and stability in a shoulder pain data set with missing values.
PathCase-SB is designed to support model composition for models designed in other formats, namely, CellML.
Indicating on a large variability in model composition within the bootstrap replicate data sets.
The idea is to allow modelers see the listings of models that capture biological networks within the same compartment (e.g., say, liver cytosol) to help them with model composition operations, as summarized in the Model Composition Interface section.
Differences in model composition between the statistical and clinical models were expected and since the statistical model was modelled for the chosen dataset, predictive performance was expected to be better for the statistical model.
When missing data does not depend on observed or unobserved measurements (Missing Completely At Random, MCAR), this leads to loss of costly gathered information, decreased statistical power, altered associations between predictors and therefore differences in model composition [ 12, 13, 38- 41].
