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Then the input dataset is tested and trained.
During Jackknife Cross-Validation, each ORF in the dataset is tested in turn by the translation rate predictor, which is trained by the other ORFs in the data set.
This process repeated until each sequence in the dataset is tested exactly once.
However, trees 2 and 3 are also not supported by PP when the whole dataset is tested.
For each combination of conditions in which the seed gene is DE, the size of the overlap between the top-ranked co-expressed genes in each dataset is tested statistically (Fig. 1 shows an example).
In our experiments, each dataset is tested using seven methods: GRAPPA-TP (TP), GRAPPA using inversion median (INV), GRAPPA using breakpoint median (BP), MGR, NJ using transposition distances (TP-NJ), NJ using inversion distances (INV-NJ) and NJ using breakpoint distances (BP-NJ).
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The robustness of the dataset was tested by eliminating incrementally higher numbers of fast-evolving sites using six levels of stringency in Gblocks 091b56.
Predicted interactions within this dataset were tested by co-immunoprecipitation, resulting in a high rate of validation, suggesting the high quality of networks produced.
Also, the dataset was tested for asymmetry using skewness.
The different acceptable SH coefficients from the whole dataset were tested individually.
Thus, each sample in the dataset was tested once, using a model that was not fitted with that sample.
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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