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In purpose of evaluating the generalization the data distribution between the modeling data and the testing data, the 10-fold cross-validation is performed.
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The model with the best fit between the model data and crude data was finally accepted.
If the model perfectly captures the signal, the difference between the model data and the real data is simply the noise in the system.
Finally, if sums of the scores within a histogram amounted to <0.5, then there was close similarity between the modeled data.
This causes a mismatch between the modeling and data source scales.
Based on each selected GOMs, the expression pattern similarity between the animal model data and the chemicals data in the cMap database was calculated.
Given this fact, the similarity of findings between the animal model data and the human studies of mechanically ventilated diaphragms is all the more remarkable.
A maximum likelihood method is used to determine the set of model parameters, P, that provide the best fit of each model to the data (i.e. that minimizes the difference between the model-generated data and the observed data).
There are differences between the model of data in the source system and the model of data in the archive.
There is little overlap (<1%) between the animal models' data, in terms of genes, diseases and associations.
The model is compared to test data from an existing publication; there was good agreement between the model and data.
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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