Sentence examples for microarray data alone from inspiring English sources

In practice, it is difficult, if not impossible, to identify, by microarray data alone, which specific cases of apparent co-expression are solely due to cross-hybridization.

Thus, the precise role of PAR2 activation in proliferation at least in HEK293 cells remains obscure based on microarray data alone.

Thus, an analysis of predicted metabolic changes based on microarray data alone does not provide the full picture, especially when changes in physiology are subtle or appear later in life.

Are the confidence estimates different in prior (combined literature and microarray data) versus no prior (microarray data alone)?

Thus from computational analysis of microarray data alone, we are able to suggest roles for these poorly characterized genes.

Traditional methods [ 1- 8] are largely developed based on microarray data alone, with the assumption that each individual gene contributes independently to clinical outcomes.

The above calculations indicate that the presence of technical noise poses no serious problem for the statistical inference of correlation measures from microarray data, let alone two-sample comparisons.

As we have previously demonstrated for either platform alone, integration of microarray data should only be performed with appropriately similar datasets [ 3- 5], although exactly where the similarity threshold lies is an important consideration that is still to be determined.

Inference of transcriptional control mechanisms from microarray expression profiling data alone has been attempted in the past using bioinformatic tools that typically search promoter regions or collections of evolutionarily conserved sequence blocks for overrepresented sequence motifs.

Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets.

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.