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The connection between changes in expression of (del)5 or 5q– MDS tumor-suppressor candidate genes and defects in growth control in vivo is largely correlative and limited to associations in gene expression in HPCs from (del)5 or 5q– patients.
In order to find possible connection between changes in expression of SF2/ASF and splicing of SF2/ASF-regulated genes we performed Pearson correlation analysis between the expression of splicing factors and the target exon splicing (Fig. 6).
There was, however, a significant correlation between changes in expression of TGF-beta 2 and response (P = 0.018).
We used a generalized additive model to identify relationships between changes in expression of genes encoding trans-acting factors relevant to the RNA life cycle and intron retention.
Pearson correlation coefficients between changes in expression of the IG and its up- and downstream genes were calculated ('Bioinformatics TOOLBOX' in 'Tools').
Subdividing tumours by clinical response revealed no significant association between changes in expression of TGF-beta 1 and TGF-beta 3.
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The enzymes that have a direct link between change in expression and change in flux might be good targets for over-expression in order to direct the flux toward i.e. a desired product, since there is a coupling between expression and flux.
While we attempted to use information about association with breast cancer risk at the loci to determine which splicing QTLs were likely to be connected to breast cancer, functional studies are necessary to confirm the link between change in expression pattern and cancer risk.
To benefit fully from the opportunities offered by gene expression profiling, we must first understand the relationships between changes in gene expression and alterations in traditional toxicology parameters.
Similar alterations in GLUT isoform expression may also contribute to the observed dissociation between changes in GLUT4 expression and insulin sensitivity in other models such as exercise detraining [ 33].
Coexpression networks devised from correlated gene expression is a powerful approach to find functional relationships between changes in gene expression and phenotypic response.
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