Sentence examples for model refinement process from inspiring English sources

The fact that the same kind of formalism is used during the whole model refinement process, helps the creation of more straightforward methods for automatic mapping and validation of the models.

Additional attributes and details can be added during the iterative model refinement process to reflect new biological information or in vitro context.

The model, on the other hand, following step-wise methods determines the parameters through an optimization procedure (with respect to the residual sum of squares at each step in the model) and the model refinement process takes care of possible combinations of parameter inputs thereby choosing the most influential parameters and corresponding functional forms.

In steps 2 and 3 of the model refinement process, we assessed and improved the model by comparing its predictions to experimentally determined gene essentialities, derived from the ADP1 mutant collection [ 8] (see Figure 1).

On the other hand, a key advantage of the approach built into ISEA and its framework are their adaptability for inclusion of additional attributes and details through an iterative model refinement process [ 8].

Having a new general framework was needed in part to reduce unnecessary, cross-model redundancies between different candidate CELLS during analogue refinement, and facilitate an iterative model refinement process capable of automated cross-model validation.

The modeling and model validation itself was conducted within several modeling phases, focusing on the goodness of fit regarding the model refinement processes.

MDD is an approach for software development that puts a particular emphasis upon making models the primary development artifacts and upon subjecting such models to a refinement process by using automatic transformations until a running system is obtained.

Figure 4 shows the difference between the initial GDT-TS scores of the models before refinement and the GDT-TS scores of the final models after the refinement process on 42 CASP11 human targets.

This paper presents techniques for developing interfaces between abstract performance models and detailed behavioral models to enable this refinement process.

The ideal starting model (e.g. one with least model bias) is difficult to obtain a priori, however it is possible to test multiple sieved models and assess the refinement process using statistically robust validation, providing a generally applicable method for model bias reduction.