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The total recall rate was 12.7%.
The averaged total recall rate was 0.93 for all classifiers.
The number of irrelevant papers in the total recall rate, that is the total number of studies identified, in our study was high, although in absolute terms the 398 studies initially identified is a modest number compared to many reviews in more medical areas.
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All classifiers performed in very similar ways and optimized classifiers from both data sets gave total recall rates between 91 and 95%% (Additional file 1: Table S1).
With these optimized parameters, we obtained average total recall rates of 0.94 for gene start classifiers trained on B1 data set, 0.92 for gene start classifiers trained on B2 data set, 0.94 for gene end classifiers trained on B1 data set and 0.93 for gene end classifiers trained on B2 data set (Additional file 1: Table S1).
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We calculate the recall rate as a percentage of the total simulations where all the true causal variants are detected.
Furthermore, we define the recall rate as the percentage of correctly predicted GO terms in total annotations of the query protein.
The recall rate is the number of correctly predicted promoters divided by the total number of promoters.
A total of 55,800 of them were identified as IS-related genes by the pipeline, which implies an 85% recall rate.
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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