Sentence examples for use of microarray gene from inspiring English sources

This chapter discusses the use of microarray gene expression data to construct diagnostic classifiers for neuropsychiatric disorders.

Several recent publications have investigated the use of microarray gene expression data to improve accuracy in estimating patient risk.

Some recent publications have investigated the use of microarray gene expression data alone or in combination with the clinical covariate variables [ 21- 23] as an improvement over the standard approach of using only clinical variables in estimating patient survival.

Because microarray studies have shown an association between patient survival and gene expression profiles [ 8- 10], some recent papers have investigated the use of microarray gene expression data alone or in combination with clinical covariates [ 11- 14] as an improvement to estimate patient survival risk.

Using a compendium of microarray gene expression data from the M3D database [30], networks were constructed for the 27 genes contained in the ROS pathway as defined by the EcoCyc database [1] (Figure 2).

Using a compendium of microarray gene expression data obtained from Escherichia coli, we constructed a series of Bayesian network models for the reactive oxygen species (ROS) pathway as defined by EcoCyc.

The TM4 MultiExperiment Viewer (MeV) application [ 46] within the Gaggle environment was used for statistical analysis of microarray gene expression datasets.

Similar methods have been used previously in the analysis of microarray gene expression data (Efron et al., 2001; Storey et al., 2005).

Systematic identification of candidate genes and new pathways related to intramuscular fat deposition in chicken breast tissues has also been made using gene expression profiles of two distinct breeds [ 27] showing the wide use of microarray in gene expression studies and identification of important pathways and biological processes in chicken.

We can now analyse the expression patterns of multiple genes by use of microarrays, rapid gene sequencing, and proteomics, and so need to reassess the way we design clinical trials to take full advantage of these new opportunities.

The use of microarray-based gene expression analysis can provide better insights of the underlying mechanisms involved in As-induced diseases and could help to identify target genes that can be modulated to prevent disease.