Sentence examples for model generalization method from inspiring English sources

If the model does not yet contain groups, it is generalized using the model generalization method, and the resulting SBML file (level 3 version 1 with groups extension) is made available to the user.

Decomposition is performed in the following way: The network is first split into compartments; then the model generalization method is applied to each compartment to detect the generalized modules.

Performance of the model generalization method.

Additional file 1: Table S1 shows the results of the application of the model generalization method to 269 metabolic models from Path2Model project [ 57].

The compression that can be achieved with the model generalization method depends on the model structure and on how well the model is annotated with the ChEBI ontology (as the metabolites lacking ChEBI annotations are not generalized).

The metabolic model generalization method [ 1], which we recall here, groups similar metabolites and reactions in the network based on its structure and the knowledge extracted from metabolite ontologies.

A new method of model generalization based on a convex hull technique is further derived to parameterize the individual-level model parameters for consistent model coefficients while maintaining satisfactory modeling accuracy for heterogeneous buildings.

Numerous experiments considering the geometrical and textural complexity of common urban building models, as well as a typical case study of complex city scene with a large number of building models, verify the effectiveness of our generalization method and the dynamic visualization of the generalized urban models.

If no similar metabolites/reactions can be detected by the generalization method (due to the model structure or to missing ChEBI metabolite annotations), this level will be missing.

The bootstrap is a powerful method offering a low variance of the model generalization error estimate.

LOO procedure gives a reliable estimate of the prediction error with less bias than other cross-validation methods and it allows good control of model generalization capacity using all available data for training.