Sentence examples for model development model from inspiring English sources

In this study, we followed the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) statement [33] to report the prediction model including model development, model performance and model validation.

An important part of the evolution of SP has been the elaboration of architecture for interrogation of non-clinical models in terms of model development, model validation and model implementation.

In this review we introduce methodology to resolve metabolic fluxes from stable isotope labeling and relevant techniques in model development, model simplification, flux uncertainty analysis and experimental design that together is termed metabolic flux analysis.

The success of computational methods in systems biology and systems engineering relies on the availability of mathematical models which represent the biological system adequately The process of model development, model analysis and model invalidation is, however, often limited by the availability of suitable experimental data leading to impaired significances of the models.

Prediction model studies can be broadly categorized as model development, model validation (with or without updating) or a combination of both.

The same process of model development, model deployment, and model revision is repeated for the animal cell as described in the vignette above.

Environmental modeling frameworks support scientific model development by providing model developers with domain specific software libraries which are used to aid model implementation.

Due to the method's model-driven development, conceptual modelling is subdivided in an enterprise-modelling phase and a functionality-modelling phase.

The grand vision of model-driven development and model-driven architecture (MDA) is to generate automatically an implementation from a high-level model of the application.

TJM designed model development workflow and developed models; TJM, BMB, HBP, and CDM analyzed the data; TJM and CDM wrote the paper.

But a lack of machine learning-specific support in the existing engineering infrastructure can create a disconnect between models in development and models in production — normal code is updated much less frequently.