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We choose to model the gene network with ordinary differential equations.
To study exercise effects in an experimental cultured muscle fiber model, the gene expression changes triggered by exercise in vivo and the modulation of transcriptional activity of cultured, EPS-stimulated muscle cells have to be compared.
Here, we set out to model the gene network that controls T-cell activation in humans, which is critical for the development of autoimmune diseases such as Multiple Sclerosis (MS).
In our model, the gene expression of major BMPs, such as BMP4 and BMP7 remained unchanged in the renal cortex of 12 mo-old RenTg mice before or after AT1 receptor antagonism.
Now we proceed to model the gene set information.
ACSEA successfully applied the algorithm to model the gene distributions along chromosomes (Fig. 3).
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Table 2 summarizes the researches that used SVM method to detect and model the gene-gene interactions.
Table 1 summarizes the researches that used neural network method to detect and model the gene-gene interactions.
Table 3 summarizes the researches that used random forest methods to detect and model the gene-gene interactions.
The nuID schema enables the data management system to report hybridizations, annotate microarray elements, and model the gene-to-probe bindings explicitly at the probe-level, and we believe it is ideally suited for labeling the identity of microarray probes.
To validate the model, the genes for platelet glycoprotein (GP) IIb and the coagulation protein factor VIII were targeted.
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