Sentence examples for model gene responsiveness from inspiring English sources

Column 1 reports the explanatory variables used to model gene responsiveness.

Simple and multiple linear regression models used to predict gene responsiveness as a function of various structural parameters were done in R. We used simple models of the form: Y ~ α X + β, where Y, the response variable, is the gene responsiveness and X is the value of the structural feature under evaluation.

We used a linear model of the form: Y ~ αX + β, where Y was the observed gene responsiveness of all genes and X was the structural feature under evaluation (e.g. presence of TATA-box, cis-acting binding sites in the promoter or gene body methylation).

For multiple linear regressions, we used models of the form: Y ~ α X + β Z + γ W... where Y was the gene responsiveness and X, Z, W, etc. corresponded to different features to evaluate.

IFN-α primed naive B-cells for IFN-γ production and increased IFN-γ gene responsiveness to IL-12.

However, gene responsiveness varied.

We also evaluated the relationship between the presence of modified histones and gene responsiveness.

We found a weak correlation between the frequency of H3K27me3 gene targets and gene responsiveness, with an R2 of 0.12.

We defined "gene responsiveness" as the number of comparisons in which a gene changed its expression significantly.

The effect of DNA methylation on gene responsiveness could be explained by a simple transcriptional gene silencing effect [ 22, 23].

Thus, gene body methylation and, to a lesser extent, TATA-box presence explained gene responsiveness on a global scale.