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The accuracy of the model prediction is evaluated by comparing the distance between the predicted miRNA starting nucleotide from experimentally validated starting nucleotide on the 5′ arm; such distance is defined as position deviation (D x)), where 0 means perfect prediction.
An example of the high quality of the model prediction is shown in Figure 4, which illustrates the observed versus predicted response for histologic evidence of lung inflammation.
Finally, the model prediction is better for low carbon atoms.
In doing so, a closed-loop model prediction is performed.
The model prediction is well verified by scratching experiments.
The model prediction is in good agreement with experimental observations.
Thus, it can be seen that the model prediction is in agreement with the experimental results.
A narrower confidence bound is preferred as it indicates the associated model prediction is more reliable.
The GRNN model prediction is more precise than the other previously mentioned literature correlation methods.
ANN model prediction is found to be matched with the experimental data (Fig. 11d).
Finally, the model prediction is done by adding up all individual contributions.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com