Your English writing platform
Discover LudwigSuggestions(3)
Exact(1)
To be these results in perspective, increasing the observed interactions from the MIST2 dataset by a factor of 2.5 resulted in the effect size of the largest interaction being increased to almost 60 (approximately 3 times larger than the residual standard deviation).
Similar(59)
Therefore we reduced the size of datasets by a factor of four.
Increasing the observed interactions in the PBC and AUGIB datasets by a factor of 7.5 resulted in the odds ratio or hazard ratio of the largest interactions being increased to almost 399 and 162 respectively.
This problem was solved by scaling the metabolic rate dataset up by a factor of 1,000, and by lowering the ratedev parameter to 0.002 (Mark Pagel, personal communication).
We observe that the MASCOT score of the non-preprocessed data (586 for BSA, 224 for ADH and 588 for TRF; see rows with n = 0 and s = 0%) is considerably smaller than that of the cleaned datasets (often, by a factor of 2-5) regardless of the severity of data pre-processing.
It is worth mentioning, that the average P-Frame size in Dataset 2 is larger by a factor of ≈ 14 in comparison with the P-Frames in Dataset 1, as shown in Table 1.
Most MHCBN allele-specific datasets are unbalanced, i.e., the numbers of binding peptides in the datasets are larger (typically by a factor of 2 to 4) than the corresponding numbers of non-binding peptides (see Table 2).
As the datasets were down-sampled by a factor of 2, an alignment error of one voxel is unavoidable during alignment.
A pollution assessment based on the surficial sediment dataset by Enrichment Factor (EF) showed the surficial sediment was moderately contaminated.
In none of the studied datasets of this group, normalization by a factor of N1/2 produced significantly worse results.
Since the factor scores generated by a factor analysis are specific to their dataset of origin and are not readily generalizable to other datasets, mean scale scores for the resulting factors were calculated in the final model.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com