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DL performed similarly to SP, but with much higher recall rate.
They found that DORIF annotation provided a much higher recall rate when compared to OMIM data for validation gene sets.
In contrast to the HMM application to maize, the rice HMMs achieved a much higher recall rate, suggesting that rice helitrons may share extensive sequence similarity.
In contrast, their method that combines sequence and structural information attains a much higher recall of about 65% at about the same precision.
Compared with lexical-based NERs, both machine learning-based NERs score slightly higher on precision, but have a much higher recall (more than twice as much).
However, the integrated approach, SWC+DECOMP+SSS, is able to predict both large and small complexes, and achieves much higher recall as well as precision.
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For a small number of targets, however, the performance is much higher, with recall values up to ~60% and precision values up to 100%.
Figure 3 demonstrates that for most of the cases the proposed method gets much higher both recall and precision values in comparison with those of MetaCluster 5.0 and AbundanceBin.
Moreover, we perform comparably well or better against FS-Weighted Averaging, mostly achieving much higher precision at higher recall.
Participants who viewed the funny videos had much higher improvement in recall abilities, 43.6percentt, compared with 20.3percentt in the non-humor group.
Although all of the syntax tree-based methods are outperformed by the Keyword 5-threshold approach, they provide much higher precision than recall.
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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