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For each of the 32 predictions, we perform a one-tailed two-sample t-test for equal means against the DME controls.
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For a set of proteins the average of all POVs over the total number of proteins N is: To evaluate the average standard deviation of our predictions, we performed a bootstrapping procedure with 100 runs over 60% of the predicted data sets.
To evaluate confidence in the predictions, we performed several qualitative assessments, such as mapping where predictions were made outside sampled covariate ranges, and comparing them with maps of sightings from a variety of sources that could not be integrated into our models.
To assess the quality of our predictions we performed a high-resolution genomic analysis of the miR expression in the underlying tumor and control cases.
For these predictions we performed GeneSV calculations twice.
To help support gene predictions, we performed comparative analysis and peptidomic investigation of C. gigas ganglia.
Online, and using the resulting predictions, we performed comparisons of calibration, accuracy, and discrimination.
In order to evaluate the robustness of created predictions we performed GeneSV calculations twice.
On those gene predictions, we performed automated functional annotation using a combined approach that includes 17 different annotation applications.
To validate the model predictions, we performed three batch fermentations at the optimal conditions identified in silico.
To test the model predictions, we performed similar lacerations on cotyledons of seedlings expressing fluorescent reporters of MTs.
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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