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g, Left, slope for every dataset from experiments 1 5 (n = 60).
These insights were gained by analyzing our gene expression dataset from experiments using the Affymetrix Soybean Genome Array GeneChip, which contains probeset sequences for 6,860 genes derived from preparasitic and parasitic H. glycines life stages.
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To test the robustness of FluReF, we augmented the dataset from Experiment 1 with human H3N2 strains sampled in 2004 from New York.
FluReF once again returned the same output as on the unaugmented dataset from Experiment 1, a reassuring result in that it was not misled by geographically isolated strains.
We augmented the dataset from Experiment 2 with the rest of the 2005 2008 human H3N2 strain sequences collected all over the United States.
We also analyzed our dataset from Experiment I for differences between the 5 embryos that were exposed to maternal diabetes, of which two exhibited defective closure of the neural tube.
Here we use gene ribosomal protein L30 (RPL30) as the test system with a dataset generated from experiments.
Although our analysis was performed on a dataset compiled from experiments collected from various laboratories and performed under different conditions, we observe a high conservation of tissue-specific expression.
The physically based SETi model can be properly calibrated using a relatively limited dataset from field experiments.
In the case of the iPSCS example, we analyzed the dataset from the experiments performed by [ 36].
On the other hand machine learning approaches can deal with process optimizations and outcome predictions by extracting useful information and correlations from huge datasets built from experiments at different length scales and in heterogeneous conditions.
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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