Sentence examples for methodologies of expression from inspiring English sources

Recently, Hosack et al. [ 35] have shown that different selection and comparison methodologies of expression data can result in gene lists that differ in quality and quantity of genes, but they also show that in spite of this variation the top five most represented biological categories in which differentially expressed genes are classified remain constant.

In earlier publications exploring this methodology independent validation of expression profiles confirmed oscillation pattern for multiple genes that did not pass the periodicity test, with or without FDR adjustment [ 36].

Second, to apply this methodology on a set of expression data identifying gene pairs whose high values of synergy cannot be explained by pure chance, suggesting biological significance.

The lack of statistical significance may be an outcome of relative small sample size as well as the different methodologies of evaluating ATM protein expression and defining cut-points to divide ATM low and high groups.

Selected targets can be optimised through construct design (e.g. to minimise protein disorder), switching to a homologous protein, and selection of experimental methodology (e.g. choice of expression system) to prime for efficient progress through the structural proteomics pipeline.

We are proposing a novel methodology for obtaining appropriate levels of expression of genes of interest, based on the prediction of the relative strength of selected synthetic promoters combined with an optimized promoter knock-in strategy.

In order to assess the use of ENIGMA for analyzing real data, we applied our methodology to the Rosetta compendium of expression profiles, representing data on 300 different experimental perturbations of S. cerevisiae [ 4].

Approaches to the elucidation of gene regulatory networks have often relied on the use of clustering methodologies, grouping genes on the basis of expression patterns over time, treatments and/or tissues.

In between there are a variety of protein detection methodologies, gene expression measurements, mRNA (transcriptome) profiling, and biochemical metabolite (pathway) assays.

A novel methodology was generated to establish two types of expression profiles and narrow the scopes of candidate resistance genes.

Our approach provides an alternative to the current methodology of identifying gene expression markers for cancer prognosis and drug responses using the whole genome gene expression data.