Sentence examples for predictive robustness from inspiring English sources

Previous research suggests, for example, that biomarkers discovered under this traditional framework may: a) be difficult to reproduce using independent datasets, b) lack predictive robustness when evaluated with different computational prediction model and datasets, c) be more difficult to interpret in the context of previous and emerging evidence [9], [10], [11].

In addition, regression models based on this data set are well-characterized in terms of predictive robustness [25, 28] and with respect to variations in how training subsets are selected [39].

We used the following procedures to evaluate the predictive robustness of our modeling.

To select variables for the final multivariate model, we resorted to the bootstrapping resampling method (500 repetitions), which determines the predictive robustness of candidate variables.

This would allow assessment of predictive robustness on a larger scale, determination of potential associations with treatment response as well as facilitate studies of relevant subpopulations of tumour cells with the possibility of correlating results to clinical parameters.

Under excessive feature space, predictive models lose robustness.

The IMC algorithms are evaluated using different scenarios in the UVA/Padova metabolic simulator for validation, comparison with a fully-automatic zone model predictive controller and robustness analysis.

Furthermore, high leave-one-out cross-validation coefficients (q2) of 0.907, 0.821, and 0.897 for these datasets, respectively, indicated enhanced predictive ability and robustness of the model.

The predictive ability and robustness of the proposed model was validated using various methods, including cross-validation and y-randomization experiments.

Following optimization simulations, the PBPK models were quantitatively validated by previously observed time-course datasets of BA and HA plasma concentrations after administration of different amounts of benzoate salts and/or BA, demonstrating the predictive strength and robustness of our computational approach.

We selected and compared the models of the highest predictive power and robustness for both species to define where M. faya could inhabit areas currently occupied by P. undulatum.