Sentence examples for implementation of gene expression from inspiring English sources

In our previous studies, we demonstrated that the implementation of gene expression analysis in the EST may further improve the identification of developmental toxicants.

The implementation of gene expression data into a model of the apoptosis pathway obtained by protein interaction databases and protein interaction prediction showed a consistent pattern of higher-level defects in the intrinsic pathway and on the level of cell death receptors that can potentially result in the phenotype of apoptosis resistance in pancreatic cancer.

Subsequent activation of signal transduction cascades culminates in the implementation of gene expression patterns characteristic of the immune response.

The implementation of gene expression studies in isolated cell populations to identify pathways triggered in these different models will be instrumental in determining the molecular events that define prion pathogenesis in vivo.

Here we report the required step in clinical implementation of gene-expression signatures; the generation of a 12-gene signature for disease progression using qRT PCR.

The first one (CaMK3X) simulates the experimentally observed phenotype and the second one (CaMK3X*) a theoretical situation, where there are no compensations at the level of gene expression (implementation scheme can be found in Methods).

Many methods have had specific implementations written for the analysis of gene expression data, such as various forms of clustering, self ordered maps, singular value decomposition and significance analysis [ 8- 13].

In this paper, we described the implementation of the Association Rule Mining methodology for the meta-analysis of gene expression data.

Our implementation introduces these controlled fold changes homogenously over the entire range of gene expression levels.

Opportunities in the field of corneal gene therapy lie in expanding the array of corneal diseases investigated and in the implementation of recent designs of safer vectors with reduced immunogenicity and longer duration of gene expression.

Second, most Bayesian network implementations assume standard binomial, multinomial probability or single Gaussian probability density functions of gene expression across a wide range of conditions.