Sentence examples for microarray measurements in from inspiring English sources

We carried out 304 microarray measurements in 46 experimental manipulations (detailed in Table S2).

For example, microarray measurements in of relative transcript abundance are subject to errors caused by differences in hybridization efficiencies.

This approach is of course applicable to any dataset containing two (or more) microarray measurements in differing conditions.

This indicates that in newborn brain, transcripts sharing the last Blcap exon cumulatively originate most often from the maternal allele, consistent with the microarray measurements in whole newborn head.

However, recent microarray measurements in humans have revealed abnormal peri-ictal electrophysiological neural firing patterns and preictal changes in the heterogeneity of neuronal spiking patterns beyond the ictal onset zone [ 14].

From previous and new microarray measurements in Saccharomyces cerevisiae following gene deletions and overexpressions, we identify a core gene regulatory network (GRN) of functional interactions between 328 genes and the transfer functions of each gene.

These weights were used in a linear combination to produce a single value for each gene, mRNA ‒ eigen = 0.8972 × mRNA ‒ Array + 0.4417 × mRNA ‒ Seq where mRNA-array is the microarray measurement in log-fold change and RNA-Seq is the sequencing measurement in log RPKM) for a particular gene.

This "high expression" of the argT could possibly be caused by random noise in microarray measurements and/or in biological samples, since the mean expression levels of other genes within the same operon were close to 0 (those for genes hisP, hisM, hisQ, hisJ were 0.00, -0.05, 0.03, and 0.05, respectively).

We conclude from this that for those orthologous pairs for which microarray measurements indicate expression in barley to be systematically lower than in wheat (i.e. the lower boxed portion in Figure 4A) the differences are likely to be due to a deficient signal obtained from the barley microarray, and not due to enhanced expression in wheat.

However, DNA microarray measurements are limited in dynamic range, specificity and reproducibility, leading to high false positive and false negative biomarker discovery rates.

In case the transcription levels of these genes would vary randomly over the microarray measurements, frequent changes in the cluster tree topology would be expected when adding more genes to the dataset.