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For estimating PK measures such as AUC, Cmax, tmax, and ke, a variety of metrics including absolute average fold error (AAFE), root mean squared error (RMSE), mean ratio obs/pred), and proportion of estimates falling within a specified fold-error (i.e. 2-fold, 3-fold etc)., have been utilized.
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For each of these 5 modified Vss-target datasets, we ran again the Bagging M5P regression algorithm and measured its geometric mean fold error (GMFE), averaging the results over the 5 runs.
This is a reasonable accuracy when considering the mean fold error reported in the literature for the interspecies scaling of 1.56 – 2.78 [52] and an animal to human extrapolation of Vss for a small range of drugs showing an average error of 1.82 [21.82
Average normalized relative quantity (NRQ) of mRNA expression in the anterior and posterior region of the neocortex (±standard error of the mean) and average fold changes were calculated by qBase.
The average fold induction values were calculated after considering the standard error, where n = 3 (n represents the number of biological replicates, each replicate obtained by 50 individual roots pooled together).
Thereafter, the average ΔCt value for the set of three genes was determined and average fold change was calculated and plotted along with standard error (SE).
Data from each treatment group's four individual chips were combined in Resolver to achieve average fold changes and p-values according to Resolver's Error Model (Weng et al. 2006).
Error bars represent the S.E.M. for the average fold changes.
The height of the bars in Fig. 7a represent average fold change, as described above, and do not contain standard error bars.
Average fold expression was compared between groups.
Second, to estimate the average error 5-fold cross-validations with 10 runs is accomplished during the training and the final results are evaluated on a separate dataset that is used for testing purposes only.
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