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In the present work, we apply this method to gene expression time profiles of 112 genes from rat spinal cord development experiments.
To evaluate the applicability of this method to gene targeting in another insect, we mutagenized the Bombyx mori epidermal color marker gene BmBLOS2, which controls the formation of uric acid granules in the larval epidermis.
In summary, this is the first report of application of the SVM method to gene expression data from DF and DHF patients to better understand the role of the genes in DENV infection pathway.
We then apply the method to gene expression data analysis.
Here, we apply this method to gene expression data from abdominal skin tissues from 647 samples.
The application of our method to gene mapping shows a noticeable improvement.
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We applied an integrative functional ad-hoc analysis method to gene-level summary signals (median core probe sets or PLIER signal estimates [10], [20]).
However, applying that method to gene-level networks is computationally challenging when a target data covers a lot of genes, thus considering a set of genes as a functional module in the target network is a viable approach.
This analysis hints to the overhangs as one of nature's methods to gene neofunctionalization.
Specifically, we have applied network-based analysis methods to gene expression data derived from microarray analyses.
SH and KL conceived the idea of applying probabilistic methods to gene modifications.
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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