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The bar graph in this figure clearly shows difference and similarity of model predictions with each other.
Algorithm names are listed in the order of the median fold change for their top predictions, with each name colored using the color used for its cumulative distribution.
Separately we use the point estimates from each method to predict the expected phenotype of the new individuals in each cell and compare these predictions with each of the 1000 subsequently observed data sets.
The number of predictions with each method that was also validated by these studies is shown in Table 2. Here, we defined true positive calls if their quality scores (see Methods section) were more than 0.9.
Hence, instead of producing predictions only with the most accurate method (edge-HIE), we produce predictions with each of the methods, except node-HIE, which shows random-like behavior.
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Table 6 indicates the computing time required to perform the prediction with each model.
Mann Whitney U-tests were run on the ranked lists of SMAP predictions with respect to each template protein structure, yielding inconsistently statistically significant p-values for some compounds.
The model's predictions agree with each other and with experiment, however.
For each datapoint in either the test set or in the hold-out fold in the case of cross-validation, we calculated the standard deviation of the predictions generated with each model conforming the model ensemble.
We also observed that many of our predictions overlapped with each other.
This allowed the Mean Absolute Percentage Error (MAPE) in % [ 33] for all the predictions performed with each different method to be computed.
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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