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The results of our comparison of the performance of three MHC-II binding peptide prediction methods estimated using datasets of unique peptides with that obtained using their similarity-reduced counterparts shows that the former can be rather optimistic relative to the performance of the same methods on similarity-reduced counterparts of the same datasets.
These effects, however, were estimated using datasets that differed in design and in the availability of control variables.
For the small datasets (n = 177), the accuracy of the estimated heritability decreased as the magnitude of the outliers increased or as the genetic variance decreased for all the five methods based on the deviation of the simulated true heritability from the heritability estimated using datasets with or without outliers (Table S4 and Table S5, Figure 1 and Figure 2).
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Divergence date estimates using datasets, branch lengths, and/or analytical techniques that fall outside of these parameters should be interpreted with caution.
Because the length of branches for empirical datasets can be estimated most reliably in an ML framework when branches are <1 substitution/site and datasets are ≥1 kb, we suggest that divergence date estimates using datasets, branch lengths, and/or analytical techniques that fall outside of these parameters should be interpreted with caution.
In each case, the estimated performance of the prediction methods evaluated on similarity-reduced datasets is substantially worse than that estimated using the datasets of unique peptides.
MIDs are often estimated using longitudinal datasets, and difference scores based on changes over time were not available in this dataset, which was cross-sectional.
*p <.05 All models were estimated using 10 datasets containing imputed values for cases with missing data (total N = 4582 in each dataset).
On the other hand, phylogenetic trees estimated using smaller datasets can get more detailed information and can display small differences between similar sequences.
Estimated effect sizes, measurement variability, and correlations between baseline and follow-up measures were estimated using unpublished datasets from studies of physiologic measures in adult and pediatric populations.
The wider 95% Bayesian credible intervals obtained for the estimates using dataset 1 composed by the total of 29 available P1 sequences to date (Table 2) probably reflect a less heterochronous sequence data.
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Justyna Jupowicz-Kozak
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