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Table 1 and Fig. 3 present the sizes of the datasets and the execution times of the different servers in which ConTra was installed.
Each bar depicts mean ± SEM, and the sizes of the datasets are the same as in Figures 2, 4 6.
Firstly, the analyses are sensitive to the sizes of the datasets, and the variation in TIN-estimates increased with sample numbers (data not shown).
In Figures S10, S11 and S12 in Appendix E (Additional file 2) for each investigated preparation step we plotted the dataset-specific means of the CVIIM-estimates over all respective settings against the sample sizes of the datasets.
It can be seen that there is significant variation in the sizes of the datasets, the lengths of protein domains they contain, and the distribution of the SCOP classifications.
The second important consideration is the robustness of SLEA with regard to changes in the cohort and how it is affected by the sizes of the datasets (that is, the number of samples included).
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This analysis takes into account the sizes of the dataset, map or ontology group.
Both the time cost for MongoDB and Redis demonstrates an sharper increment in terms of the size of the datasets.
The number and the size of the datasets require an information system to process, manage and distribute data.
In addition to this the use of proxies increases the size of the datasets that we are working with.
We believe this is due to the smaller size of the datasets they used and the type of promoters these datasets contained.
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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.
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CEO of Professional Science Editing for Scientists @ prosciediting.com