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The four datasets are plotted inFig.
The skew and the proportion of datasets are plotted together.
In Fig. 1, the CoERs obtained from the eleven human datasets are plotted as a function of CD: Fig. 1(A),1(B) and 1(C) show the results of the HC, ZC and NC pairs, respectively.
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To find out if PADIF scoring performance correlates with the diversity of a dataset, the PADIF AUC values of the different protein datasets were plotted versus the respective percentage of pairwise similarities ≥ 0.5 and ≥ 0.7 (Additional file 1: S4).
Standard deviations were calculated for each taxonomic group considered and comparisons between observed and expected datasets were plotted (Figure S1).
Four additional breast cancer datasets were plotted in Supplement Figure 2 to support our results.
Results from the analysis of all seven datasets were plotted on the main tree.
To illustrate this in detail, CCSphase scores of several models for both datasets were plotted as shown in Fig. 3B.
To further compare the proteomic and transcriptomic datasets quantitatively, twenty-three common genes/proteins up-regulated in both transcriptomics and proteomics datasets were plotted together.
Standardised residuals and fitted EQ-5D index values from fitting the final model in both the estimation and validation datasets were plotted against one another.
A bar chart comparing adjusted risk ratios for prospective and retrospective datasets was plotted to provide a visual indication of how well the results from the two datasets agree.
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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