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The correlation between REGγ and every other gene in these datasets were calculated and evaluated statistically.
For each metabolic state, errors caused by KIEs on the three isotopic datasets were calculated and are summarized in Fig. 7.
The mean and 95% CI of AUCs produced from the 1,000 datasets were calculated, and comparisons between parameters were made by the Student t test.
The mean and standard deviation of the accuracies on 30 training and independent test datasets were calculated and shown in Additional file 1.
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Next, the coefficient of variation (CV) for the percentage of under processing based on 2 500 generated datasets was calculated and used as a criterion to determine that 100 was an acceptable minimum number of datasets to estimate a recommended thermal process (F110°C = 9.6 min) considering the reported variability of the parameters DT and No.
Separate sequence read datasets were used as inputs into the DESeq package where size factors for each dataset were calculated and overall means and variances were determined based on a negative binomial distribution model.
Using the median expression as a cut-off for high and low expression, the ORs for each dataset were calculated and a pooled analysis performed according to a random-effects model.
At first, the mean of the dataset is calculated and subtracted from each of the data dimensions.
The rank for a given dataset is calculated and given inside parenthesis.
For correlation analysis, all datasets were calculated for correlation efficiency and were considered significant at p<0.05.
Diversity estimations for both pooled and individual population datasets were calculated in DnaSP, including the number of haplotypes (n), haplotype diversity (h), and nucleotide diversity.
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