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We plot the maximum accuracy found against number of features for various datasets as shown in Fig. 2. From the plotted graph, we observe that the maximum accuracy is obtained for a subset of features that contains 3 7 features.
The maximum accuracy found across windows was used as the decoding accuracy for that particular patient).
The resulted maximum accuracy found was 73.685%% [Please see Table 6].
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In case a cut-off of 2.5%% was used, the maximum accuracy level found was 35%%.
A previous study investigating its completeness and accuracy found loss to follow-up to be uniform from 1980 to 1993 and estimated the resulting overestimation of relative survival to be a maximum of 2% [ 23].
Here these kernels produce the same subset of SNPs and maximum accuracy [Please see Table 6].
For all of the reduced dimensions, we always get the maximum accuracy of 73.805 %.
We experimentally found that λ = 0.55 results in the maximum accuracy rate in VAD decisions over the TIMIT training set.
As hypothesized, the maximum classification accuracy was found at channels located over the primary visual cortex (channels 26, 25 and 15 for high-, medium- and low-contrast intensity, respectively) as previously identified using the functional localizer (see section Task description and procedures).
We were interested to find out which features the classifier was exploiting in order to reach maximum accuracy.
In the specific application to SRSF1, we found that the prediction power is still comparable with MEME-based although, altheugh the AUC and maximum accuracy were both reduced when RNA secondary structure information was not incorporated.
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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