Sentence examples for multiple expression levels from inspiring English sources

On the technical side, multiple expression levels in single cells comprising a sample can be measured by the novel methods of single cell arrays, phosphocytometry and cellomics [28], [29].

To assess the relationship between age at draw and multiple expression levels, we employed the LASSO algorithm of Tibshirani (Tibshirani, 1996; Efron et al., 2004) to build a linear model of age at draw as a function of multiple expression levels.

This necessity motivated us to jointly correlate multiple transcript expression levels for a gene with multiple SNPs in the same gene.

One way around these limitations is to examine and analyze gene expression from many individual genotypes that each contains multiple mutations and to study the covariance in phenotypes and multiple gene expression levels to hypothesize which of the gene expression changes might cause phenotypic deviations [ 38- 41].

Moreover, assessment of multiple miRNA expression levels can accurately predict prognosis and survival in lung cancer [ 21].

This study has attempted to move beyond single gene expression to a more comprehensive investigation of multiple gene expression levels in predicting tumor response following fluoropyrimidine-based CCRT.

The well-established differential screening technology, that allows for the simultaneous comparison of multiple gene expression levels between two samples differing in tissue type and pathological state, has been the more extensively applied.

This information was gathered from multiple sources: Expression level and breadth: We used human gene expression data from Su et al. (2004) (U133A+ GNF1H data set, normalized using the MAS5 algorithm), which contain gene expression measures for 79 different tissues (or organs) with two replicates each.

A comparative analysis of the kidney transcriptomes revealed multiple genes, with expression levels that were statistically different.

However, these models have so far only supported binary expression dynamics, which are not general enough to capture expression dynamics with multiple steady-state expression levels.

Therefore, it is desirable to adopt a "multimarker panel" concept and nontrivial computational methods that can integrate microarray measurement of multiple differential gene expression levels between disease and controls to achieve good performance for clinical genomic development of biomarkers [ 9].