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Exact(30)
The model achieved an AUC = 0.65, 0.65, 0.64 for Tcfcp2l1, Nr5f2a, and Stat3, respectively.
Interestingly, our model achieved an equal AUC = 0.84 for H3K27ac and Cdk8.
Using only the motif information, the model achieved an AUC = 0.72.
The model achieved an AUC = 0.58 for phastCons, 0.63 for GC content, and 0.64 for repeat fraction (Fig. 3d).
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.
Similar(30)
The neural network model achieved a better performance than the linear model.
The results show that the RF model achieved a lower mean square error.
Their forecast model achieved a mean absolute percentage error (MAPE) of 9.9%.
The final consensus model achieved a precision, which was similar to the estimated experimental accuracy.
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