Sentence examples for more robust microarray from inspiring English sources

Since there have been only few studies, in the available literature, which concerned miRNA profiling of eutopic endometrium (actually mostly luteal) [ 12, 13], and the technology since our last publication has greatly developed, we decided to expand our previous work and use more robust microarray technique which facilitates expression profiling of more than 2000 miRNAs.

Increasingly, more robust techniques like microarray and next-generation sequencing are finding applications in the elucidation of miRNAs in immunity [ 24, 25].

These findings suggest that 3SEQ is a more robust platform than HEEBO microarray for gene expression profiling from FFPET.

Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms.

Consequently, we developed a more robust approach that uses protein microarrays and overcomes the limitations of mass spectrometry.

In addition, sequence-based methods are more precise than microarrays and more robust across experiments because of much greater depth and the absence of the background signal and cross hybridization issues that were associated with microarrays.

Many plant studies have demonstrated the direct involvement of thiol-containing molecules (glutathione, phytochelatins, etc). in arsenic detoxification, however more robust approaches (i.e. microarrays) should help clarify how arsenic affects plant physiological processes on a global scale.

In addition, it has been suggested that SAGE studies are more robust, and require fewer replicates than microarray studies [9], [10].

The fight is still going on.As microarray technology has become more robust, there are fewer reasons for scientists to make their own.

We use "Spearman with MAS5" and "Pearson with RMA" because it has been shown that the inclusion of at least one non-parametric step based on ranks in the analyses of microarray data offers statistically more robust and more accurate estimation of expression values [7] and expression correlations [8].

The use of more robust and generalized subclasses enables the better utilization of microarray data and the construction of better classification models.