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Their main characteristics are summarized in Table 1. Figure 1 shows the datasets with 3 and 10 clusters, together with the optimal solutions achieved by DAN. Figure 1 Second and third datasets of TS/1, together with the solutions achieved by DAN using log 10 = 3 (each cluster has been colored with a different color).
Figure 4 figure supplement 1 shows the datasets used to calculate R for WT, bam3, bam4 and isa3.
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This clearly shows the dataset is highly diverse and suitable for model development.
Supplementary Table S1 shows the dataset details and the mapping between the quaternary structures and the tertiary structures.
Figure 5 shows the test datasets.
Figure 5 shows the subsampled datasets.
Table 7 shows the bioassay datasets with the results of the best classification model highlighted.
Table 7 shows the five datasets, which yield classification improvements using the distance-based features.
Table shows the four datasets used in this study in chronological order and permuted samples.
Subsequently, a 3D viewer shows the embedded dataset, and enables the user to explore the dataset and its properties.
The figure also shows the "integrated dataset" that can be constructed by de-pseudonymizing the dataset.
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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