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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.
For each window the percentage GC content and the mean coverage of the Illumina and PacBio datasets was plotted using RStudio 2.13.1 and a Pearson correlation coefficient was computed for both datasets against percentage GC content using custom scripts (Additional file 1).
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The four datasets are plotted inFig.
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).
The skew and the proportion of datasets are plotted together.
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.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com