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And then for good measure, we compared those "before" photos with contrasting "after" shots.
Since CS is the best sequence variability measure, we compared WCN-CS and RSAT-CS correlations protein-by-protein.
As a primary outcome measure, we compared the before-after difference in lipid levels between both groups.
Also, because serum potassium is a continuous measure, we compared patients who were positive for the code with those with hospital encounters who were negative for the code.
Using a simple measure, we compared the composition bias of the set of all proteins, of random proteins (Section 2) and of ORFan proteins in each genome.
As an external validating measure we compared the estimated parametric curve with nonparametric estimation of the regression function and calculated its derivatives with kernel regression estimators and automatically adapted local plug-in bandwidth function.
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Many different measures can be used to compare assemblies, and we begin with a very simple measure: we compare the number of singletons in each assembly.
As a second measure we compare link-prediction accuracy across the methods.
For the behavioral and cardiovascular measures, we compared each time point between the placebo and caffeine scores using repeated analysis of variance.
In order to evaluate our secondary outcome measures, we compared hospital and ICU mortality and length of stay between ED patients at risk who developed ALI and those who did not.
To these measures we compared our own four measures: two word frequency indices (SUBTL_logW and SUBTL_logW-CD) and two character frequency indices (SUBTL_logCHR, and SUBTL_logCHR-CD).
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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