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Table 7 Results of the re-sampling test on randomized subsets of the data with varying numbers of characters, and the resulting rankings for all datasets for the respective analysis.
Figure 7 shows the cumulative distribution of the classification error difference on the two datasets for the respective methods.
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A general prerequisite for this approach, though, is the availability of a more or less complete genomic dataset for the respective species.
After the final data lock, entire datasets from the respective countries were sent for final analysis.
The matching was done separately for the five imputed datasets in the respective countries so that the matched samples cover between 102 (in Germany) to 1184 (in Russia) students.
For this, we chose the 29 species shared between the two datasets (for the tree topology of the respective nuclear dataset see additional file 5).
R 2 marg was 0.45 and 0.60, and TP rate of the validation dataset 46 and 73%% for the respective models.
The samples were then limited to these periods for the respective datasets for the present analyses.
The parameters of the model were estimated by cross-validation, and the ability of the prognostic signature to predict OS or DFS was evaluated for the respective datasets.
The best models in the present amino acid substitution models for the respective datasets are cpREV64 for cpProt-55, mtREV for mammal-mtProt, and FLU for HA_Human-Flu-A-H1N1.
The two tree topologies (Tree1a and Tree1b) used in the age calibrations were modified from the ML trees of Aln-2 and Aln-3 datasets, respectively; they have the ML topology of Aln-1 (= Figure 1) for microhylids, natatanurans, and afrobatrachians (see Additional File 2), while all other relationships were as inferred for the respective datasets.
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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.

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