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The model of Shikimate Kinase is generated using a comparative molecular modeling approach and the best model is selected based on stereochemical properties.
The best model is selected on the basis of a model selection criterion, simultaneously providing the optimal number of clusters and assigning cluster membership to observations.
Model selection is considered successful when the best model is selected a posteriori from the data through the designed experiment.
The best model is selected.
When a big portion of the scene is suddenly detected as FG, the best model is selected, that is, the one that minimizes the amount of FG pixels.
To avoid local minima in training, each model is trained 20 times with random initialization and the best model is selected based on the validation subset.
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The best model was selected using model selection procedure (AICc).
Out of the models generated, the best model was selected based on Modeller's scoring function and external validation via Whatcheck (Hooft et al., 1996) and PROSA (Wiederstein and Sippl, 2007).
The best model was selected between those that perform feature selection, i.e. GLM, LASSO, SVM-RFE, RF-RFE, ENET.
The best model was selected and analyzed based on Modeller's probability density function (Discrete Optimized Protein Energy score) and the Ramachandran plot (Laskowski et al., 1993), and subsequently refined using the Loop refine module of Modeller.
A 90% confidence set of models was compiled if no best model was selected via AICc.
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