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For TT2NE data set, the GM method indicates good performance in predicting secondary and pseudoknotted structures.
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The comparison of both analytic methods indicates good agreement.
The values of slopes of the regression equations of the proposed methods indicate good sensitivity.
This result indicates good robustness of our method.
High sensitivity with high specificity (i.e. low false discovery rate) indicates good performance of a method.
The high Cohen's Kappa coefficient under the (B) interpretative criteria indicates good agreement between the two methods.
That indicates good reproducibility.
Turbidity indicates good growth.
Both the calibration and the validation values obtained by using a partial least squares (PLS2) regression method indicate a good-quality model performance (slope near 1, offset near 0 and large correlation between sensors and categorised variables).
Both the calibration and the validation values obtained by using partial least squares (PLS2) regression method, indicate a good-quality model performance (slope near 1, off set near 0 and large correlation between sensors and categorized variables).
Utilizing a few random collocation points, the method indicates also a very good agreement compared to the sampling-based Monte Carlo simulations with large number of realizations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com