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The first and second order derivatives of f(x) in the case of the SCL, WOMBAT and PubChem MLSMR datasets are shown on Additional file 17: Fig. S13, Additional file 18: Fig. S14, Additional file 19: Fig. S15.
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The distribution of cluster sizes in these two pseudo-reference clustering datasets is shown on Fig. 3.
The 38 regulators in the dataset are shown on the x- and y-axes of the heat map, and the highest scores for every pair are depicted in the intersecting cell on a color scale from light (minimum score in the heat map) to dark (highest score in the heat map).
Additionally, combining datasets was shown to increase performance on a test dataset, especially when using integrated-within dataset normalisation, with best data fit obtained by Loess_XPN.
The DDHs of the fused scores based on the corresponding trained SVR model on three datasets are shown in Figures 19,20,21.
results on real-life datasets are shown in Table 6, and are in line with those obtained on the simulated datasets.
Validation curves on both datasets are shown in Fig. 7.
The contributing teams and datasets are shown in Table 1 with data on the number of cases and controls for each polymorphism and for each type of cancer.
Tracks based on individual datasets are shown separately, as well as the consensus HapMap track.
The ROCs for the predictions on both datasets are shown in Figure 2.
The prediction results of SRC prediction models with substitution matrix representation based description of protein sequence on three datasets are shown in Tables 1– 3. When using the proposed method to predict PPIs of Yeast dataset, we obtained the results of average accuracy, precision, sensitivity, and MCC of 96.28%, 99.92%, 92.64%, and 92.82%.
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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