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The use of microarrays has provided a new opportunity for studying even 20000 human genes in a single experiment.
The use of molecular tools such as microarrays has provided deep insight into the molecular stress response of corals.
The development of global gene expression profiling technologies, such as DNA microarrays, has provided additional avenues to identify the molecular features of tumors that are associated with clinical variables, such as tumor grade or outcome.
Genome-wide gene expression data using SAGE or DNA microarrays has provided a wealth of information on the regulation of genes under certain conditions or by specific transcription factors.
The wide availability of microarrays has provided disease-related research with valuable transcriptomic information on the interactions between cells and the environment in which they reside with this information being used to characterise disease states, predict disease progression and develop new therapies.
The advent of DNA microarrays has provided the science community with a tool to concurrently examine the expression of thousands of genes within a given cell or tissue type, thus providing a platform for future diagnoses and prognostic analyses of disease with gene-level specificity [ 1, 2].
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Gene expression microarrays have provided many insights into changes in gene expression patterns between different tissue types, developmental stages, and disease states.
Indeed, genome-wide measurement technologies, such as gene and single nucleotide polymorphism (SNP) microarrays, have provided an opportunity to identify genes that are mutated or differentially expressed and drive various diseases.
For over a decade, two-channel transcriptomics microarrays have provided a powerful approach to study genome-wide gene expression events.
For circadian rhythms, high-throughput microarrays have provided high-resolution time-series data of gene expression levels [ 17].
Estimates of genome-wide platelet RNA expression using microarrays have provided insights to the platelet transcriptome but were limited by the number of known transcripts.
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