Sentence examples for model simplification procedure from inspiring English sources

It is the main goal of the presented work to derive a model simplification procedure for signal transduction pathways such that: (1) the model size is significantly reduced such that the model can be validated using available experimental data, and (2) the physical interpretation of the remaining states and parameters is retained.

We used a backward model simplification procedure by first fitting saturated models.

All statistical analyses were computed using the free software R (http://www.r-project.org/) using methods, tests and model simplification procedures as described in Crawley [35].

The additional explanatory variables and explanatory variables included in each minimal adequate model after application of the simplification procedure are presented in (Additional file 4: Table S3).

The purpose of this paper is to develop a global optimization model, simplification schemes, and a heuristic procedure for the design of a shortcut-enhanced unidirectional loop aisle-network with pick-up and drop-off stations.

We propose a systematic procedure for model simplification, which consists of the following steps: estimate the parameters of the model, create an identifiability ranking for the estimated parameters, and simplify the model based on the identifiability analysis results.

Automated procedures of model simplification, however, often make us forget that this constitutes a case of multiple hypotheses testing that will lead to high rates of type I errors (Zhang 1992; Whittingham et al. 2006; Mundry and Nunn 2009) as well as biased effect size estimates (Burnham and Anderson 2002; Lukacs et al. 2010).

Model simplification was done in a stepwise procedure and non-significant terms were excluded [33].

The final result of our simplification procedure was a model with main effects of Treatment (with 2 levels), N/H bicluster (with 3 compound levels), and an interaction term between N/H bicluster and Treatment.

Therefore, we would like to quickly summarize the procedure for the CSP-based model simplification: 1.

Model simplification was performed using a classical backward selection procedure, by removing the variable showing the highest P value (when P > 0.05) from the model to select a best-fit set of explanatory variables [ 46].