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The spreading concentration is thus not suitable for this kind of dataset.
But, it is a natural characteristics in this kind of dataset.
As valuable as this kind of dataset might be, Deep Root's carelessness shows that when the race has come and gone, keeping all of that aggregate data safe must not be quite as lucrative as scooping it all up in the first place.
Given the biological variability and high level of noise in this kind of dataset we have developed probabilistic models, namely the gamma Models for Oligonuceotide Signals (gMOS), that provide a robust estimate of expression level along with a measure of uncertainty for each gene [10].
This kind of dataset is rare in developing countries.
However, this kind of dataset does not satisfy the above network-constructing algorithm since there are only 10 pair candidates with their P-values below 0.05 (91.7% of the total pairs with correlation statistical significance above 0.05), see the additional Figure 5-B in Additional file 6.
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This kind of datasets can be referred to as time series microarray datasets.
This kind of datasets was randomly sampled in a ratio of 1/500 from the entire datasets of the NHI in the year.
The results presented in those contributions are encouraging and pave the way to a further investigation of GP for this kind of datasets, which is the goal of this paper.
Another contribution of this paper is the kind of dataset used in the analysis: instead of using administrative records, I use survey data.
In our opinion, traditional Bonferroni correction for multiple tests is too stringent for that kind of dataset, and would lead to passing over of important results.
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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