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The flexibility of the modelling achieved is demonstrated from legacy data integrated in the new scheme and made available using an OGC Web Feature Service.
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Using only the motif information, the model achieved an AUC = 0.72.
The model achieved an AUC = 0.65, 0.65, 0.64 for Tcfcp2l1, Nr5f2a, and Stat3, respectively.
Using imbalanced data, the model achieved AUC = 0.87 but very poor precision-recall curve (PRC) of 0.57.
The model achieved an AUC = 0.58 for phastCons, 0.63 for GC content, and 0.64 for repeat fraction (Fig. 3d).
The model achieved Forest Stewardship Council (FSC) certification in 2012 for 20 of its farmer groups, involving 1,217 farmers.
By combining the GC content and phastCons the model achieved an AUC = 0.71, and by combining GC content and repeat fraction it achieved an AUC = 0.76.
Therefore, when we tested the combinatorial predictive power of the Mediator sub-unit Med12, Cohesin sub-unit Smc1, and RNA Pol II, the model achieved an AUC = 0.91.
The model achieved mean absolute percentage errors ranging from 3%to6%6% for the day ahead prediction.
The model achieved reasonable prediction (R2 = 0.939; Figure 27), but some inaccuracies occur.
The model achieved a mean square error ranging from 5.7% to 15.33%.
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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