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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.
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.
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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.
The correlation between REGγ and every other gene in these datasets were calculated and evaluated statistically.
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.
For each metabolic state, errors caused by KIEs on the three isotopic datasets were calculated and are summarized in Fig. 7.
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.
Frequencies of each eGene across dataset were calculated for housekeeping and non-housekeeping genes and compared by Student's t-test.
For cross-referencing of RNA-seq and ChIP-seq datasets, FPKM values for each RNAseq dataset were calculated for each gene and then log normalized to a 0 to 10 point scale, with 10 representing the smallest value that captured 95% of the data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com