Sentence examples for predictive potential of the model from inspiring English sources

We wished to compare the in silico essentiality predictions with experimental data to assess the predictive potential of the model.

To assess the predictive potential of the model, we compared the in silico essential genes predicted on SCFM with experimental essentiality data for P. aeruginosa PAO1 and P. aeruginosa PA14[6969, 70] since there is no experimental gene essentiality data available for B. cenocepacia.

In order to effectively validate the PLS model, compounds that were not included in the data set to obtain the model were placed into a separate test set to assess the predictive potential of the model.

In order to assess the predictive potential of the model, we simulated the in silico model based on minimal media consumption, as shown in Table  4.

We also decided to test the wealth of the amino acid propensities calculated in our model and check whether there is a high rate of redundancy within the training set, which could hamper the predictive potential of the model.

To validate the predictive potential of the model the determined high risk cut-off level of CRS (CRS > 6.82) was used to classify a test cohort for prognostic risk stratification.

Hopefully, as the RTT field moves into clinical testing of therapeutic leads, we can validate and refine the predictive potential of the models by comparing outcomes in mice with results obtained from human clinical trials.

The results revealed that the external predictive potential of the ETA model was superior to the non-ETA model.

Here, we report the results obtained when we use yeast derivatives as adjuvants for the immune response in cattle, the analysis of the predictive potential of the mouse model, and the effect of the quality of the immune response on the degree of protection.

To determine the predictive potential of the compounds, these models were evaluated using leave-one-out cross-validation to assess the predictive performance of these models and to further refine the variables included in prediction analyses.

Significant correlation coefficients (CoMFA, r2 = 0.99 and q2 = 0.85; HQSAR, r2 = 0.92 and q2 = 0.72) were obtained, indicating the predictive potential of the 2D and 3D models for untested compounds.