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This is the first trial to test the ability of microarray data to direct clinical decisions.
We are using microarray data to explore the pervasiveness of NMD in humans and in Drosophila, in collaboration with Don Rio2.
We propose a systematic analysis of long gene expression (LGE) with a metric termed the long gene quotient (LQ) that quantifies LGE in RNA-seq or microarray data to validate neuronal identity at the single-cell and population levels.
Users can run MAMA after pasting the signal ratio from the microarray data to the template file.
We explored the possibility of using HG-U133 microarray data to identify changes in alternative mRNA processing in several available archival datasets.
Resolution of microarray data to cellular level showed that IFN-inducible genes in active TB patients were predominantly expressed in neutrophils, and to some extent in monocytes [26].
Such inquiries have motivated the advancement of gene set analysis and the utilization of microarray data to make inferences regarding genetic networks.
The COA reduced the multidimensional microarray data to the three most informative components describing the dataset.
Furthermore, it facilitates the use of microarray data to validate deep sequencing results.
We used the microarray data to compare gene expression levels between epilepsy patients and normal individuals.
We used quantitative microarray data to test these predictions for the evolution of AS expression levels in human and chimpanzee.
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