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The RT-MLPA assay and SVM classifiers were validated in a separate validation cohort.
Also, if a publication used more than one method to validate a diagnosis, each method was considered a separate validation.
Compilation of the data was verified against the sources in two separate validation stages.
Separate validation experiment at predicted optimal condition ascertained the high predictive ability of the model equation.
The predictive abilities of both models were compared using separate validation datasets.
The application of this systematic approach allows the separate validation of submodels and reliable parameter determination by small scale investigations.
Separate validation experiments at the derived optimal conditions ascertained the predictive ability of the developed model equations.
Separate validation experiments were conducted at optimum conditions to verify predictions and adequacy of the SOP models.
Model consistency was assessed by comparing simulated and observed safety performance based on a separate validation sample of vehicle tracking data.
We then evaluated our predictions on a separate validation set (St_B), combining 471 S. aureus samples (St_B1, Supplementary Data 3), and 221 CoNS (St_B2, Supplementary Data 4) and show the results in Fig. 2a.
A total of 1399 samples from five plant species were studied, with 650 included in a calibration set and 50 in a separate validation set.
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