In a study to evaluate the rapidness at which this array could identify and detect these genes, Naas and coworkers found that Check-Point's ESBL/KPC DNA microarray could identify them in 7-8 hours as compared to conventional susceptibility testing that took a mean time of 54 hours.
Based on adjusted t tests, Significance Analysis of Microarrays (SAM) method could identify the gene set with statistically significance changes in two states (e.g. disease vs. control) [ 42].
Previous studies also described that MLST, AFLP, PFGE and DNA microarrays could not identify GBS-specific genetic markers by comparing the genomes of C. jejuni strains [16], [21], [25], [29].
Previous studies that employed MLST and DNA microarrays could not identify GBS- specific genetic markers by comparing the genomes of C. jejuni strains from various geographical locations, serotypes, and disease outcomes [39], [55].
We found EST sequences for 34,838 (86%) of the clones on the microarrays, of which we could identify best-matching genes for 32,402 (93%).
We first determined whether the antibody microarray SPRi sensor chip could identify purified exosomes.
We then checked whether the antibody microarray SPRi sensor chip could identify exosomes in CCS directly without enrichment or purification.
To fully understand Id-1-mediated phenotypes, a DNA microarray analysis should be performed that could identify more Id-1 downstream targets.
For example, an unbiased microarray analysis of peripheral blood cells could identify a set of genes with expression that is closely correlated to disease activity in AS; one of these genes codes for LIGHT and serum levels of LIGHT have been confirmed to correlate with CRP and erythrocyte sedimentation rate (ESR) [ 45].
We compared the ovary capture array data with data generated from tiled genomic microarrays to determine whether our capture methodology could identify a similar expressed gene set.
