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A series of cutoff-p-values (between 10−7 and 10−1) is applied and for each of these, the fraction of significantly regulated genes from the full dataset that are also significantly regulated to the given cut-off p-value in the leave-one-out datasets is determined.
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The relative weights among the datasets were determined so that the data fit for each dataset is satisfactory.
Also the datasets were determined and normalized to the interval [ -1,1)] to phase the problem of prevalence of features with wider range over the ones with a narrower range, without being more important [ -1,1
From the correlations between recurrence period and accumulated rainfall data regarding to the hazard class, the range and lower bound of 1, 6 h, and 3 day accumulated rainfall datasets were determined (Table 3).
Reasons for differences in outbreak size between the two datasets were determined in collaboration between the 19 LPHAs and the Hessian SPHA.
The accuracy rate of training datasets was determined by subtracting the LOOCV-error rate from 100%.
Normality of datasets was determined using the Student's t-Test.
Both for the cell line datasets C1 and C2, and the human brain datasets B1 and B2, reproducibility between the replicated datasets was determined as follows.
Now, additional seven small RNA transcriptome datasets were used and target sites for all nine datasets were determined with the TAIR8 genome release [ 25].
The model of evolution that best fit the individual datasets was determined by MODELTEST 3.7 [ 106] and these parameters were applied to our molecular dataset.
The number of identical sequences between these datasets was determined using a Perl script and divided by the sum of number of complete transcripts in both datasets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com