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For the 279 differentially expressed genes, our integrative model predicted gene expression accurately with a mean Spearman correlation of 0.28 (median = 0.35), while the TF-only model obtained a mean Spearman correlation of 0.16 (median = 0.31).
The corrected R 2 value and the Schwartz Criterion [24] were reported along with the measured and predicted gene expression values.
SNPs were evaluated for their influence on gene expression in both diagnostic leukemia cells and normal leukocytes independently, and these cis-SNPs that predicted gene expression in both leukemia cells and normal leukocytes were further examined.
Akashi and Gojobori [2] used a modified version of this approach to show in E. coli and Bacillus subtilis that predicted gene expression levels based on codon usage bias show a negative correlation with average protein cost.
The alterations in predicted gene expression patterns reflected possible responses to stress.
Furthermore, the alterations of predicted gene expression patterns reflected possible responses of these fish to the virus infection.
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The prediction results submitted to DREAM were evaluated based on the Spearman correlation between the real and predicted gene expressions.
> -wrap-foot> Gene: Top scoring genes ranked by the predictive power of methylation to predict gene expression variation.
It can then be used to predict gene expression levels of other mutants.
As an example, the EES can predict gene expression levels in double knockout mutants.
This model was validated by assessing its ability to predict gene expression under novel conditions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com