Sentence examples for microarray to distinguish from inspiring English sources

We demonstrate a high degree of reproducibility in detecting transcriptional differences among biologically replicated experiments and the ability of the microarray to distinguish between the expression of closely related gene family members.

Thus, we present the experiment utilising the H5N1 infected ducks as a test of the validity of using a chicken microarray to distinguish between two different immune states in ducks.

Because the general low conservation in 3' UTRs, and genome duplication events that have occurred in teleosts [ 2], we focused on 3' sequencing to design specific oligos for a microarray to distinguish between potential paralogues arising from gene duplications.

In addition, the inability of cDNA microarrays to distinguish between splice isoforms or similar transcripts produced from paralogous loci may also have resulted in lack of the expected differential expression for some of the candidate genes.

The TRANSBIG trial will assess the ability of microarray data to distinguish between these two groups by testing 5,000 women for the expression patterns of 70 genes.

The use of this microarray method to distinguish undifferentiated and differentiated hESC cultures has been demonstrated [ 27].

This study on human acute leukemia demonstrated that it was possible to use microarray data to distinguish acute myeloid leukemia from acute lymphoblastic leukemia without any previous knowledge.

This effort was to generate gene expression profiles for rat lung tissue using high-throughput microarray technologies to distinguish levels of injury based on the differential expression of specific groups of genes thought to be involved in this process.

As the chronic EBV infection poses as one of the causative risk factors for NPC, the application of the microarray platform to distinguish transcriptome of the EBV− and EBV+ NPC cells has enabled us to gain more understanding of EBV-specific signals for NPC tumorigenesis [ 33].

The two-factor ANOVA used in this study to determine the influence of AtNHX1 is considered a powerful tool for the analysis of microarray experiments with multiple factors [ 11], as it utilized all 48 microarray data points to distinguish between an effect of genotypes across all treatments (main effect) and a treatment-dependent effect of lines (genotype × treatment interaction).

This also validated the use of microarray GSC similarity to distinguish stem-like gliomas more faithfully than CD133 expression (Fig. S1 and legend).