Sentence examples for all models were validated from inspiring English sources

Considering that all models were validated using a fivefold cross validation approach, we were using up to 6 × 32 = 192 cores per one task simultaneously thus allowing fast processing of the data.

Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (R2cv≈0.80).

All models were validated with MOLPROBITY (Chen et al., 2010).

All models were validated by QQ plot of the residuals (data not shown).

All models were validated in Sample Set A, resulting in three combinations, REG1B + CA19.9, SYCN + REG1B + CA19.9, and AGR2 + REG1B + CA19.9, which were found to significantly improve the AUC of CA19.9 alone (p = 0.001, p = 0.030, p = 0.004, respectively) (Table  4).

All models are validated as they are built based on Monte Carlo n-fold internal cross-validation, which is also utilized to determine the number of significant model components.

All the designed models were validated using a k-fold cross-validation, and the largest mean absolute errors (MAEs) obtained for the varietal quantifications were 10% for the linear model and 2.8% for the non-linear one.

All three models were validated successfully against human data.

All four models were validated using the popular 'leave-one-out' (LOO) method and with a test set of 9 molecules.

Rather than performing split-group analysis, all multivariable models were validated and 95%CI obtained through 200 bootstrapped replications of the data [12], [13].

All pharmacophore models were validated by calculating enrichment factors (EFs) using the FXR-actives data set and the complete ChEMBL as decoy set (best models see Table 2).