The advent of high throughput technologies, in particular microarrays, for biological research has revived interest in clustering, resulting in a plethora of new clustering algorithms.
However, since microarray technology is thus far limited to only a few aquatic species and derivation of biological meaning from microarray data is highly dependent on statistical arguments, the full potential of microarray in aquatic species research has yet to be realized.
The microarray data from this research has been deposited in the NCBI Gene Expression Omnibus data repository under accession number GSE15614.
The microarray data from this research has been deposited in the NCBI Gene Expression Omnibus data repository under accession number GSE8377[ 57].
The microarray data from this research has been deposited in the NCBI Gene Expression Omnibus data repository under accession numbers GSE8093 [ 33].
The microarray data from this research has been deposited in the NCBI Gene Expression Omnibus data repository under accession number GSE26924.
Microarray use in pharmaceutical research has expanded with applications in basic research for drug target discovery, biomarker determination and validation, and toxicogenomics as well as development of prognostic tests and disease-subclass determinations.
Today, Cy3 and Cy5 are the most widely used dyes in microarray experiments and much research has been done on these dyes [ 4, 8, 11, 15, 16], although Alexa 555 and Alexa 647 have been suggested by Cox et al [ 6].
This is due to the fact that microarray application in miRNome research has several limitations, including hybridization and cross-hybridization artifacts, dye-based detection issues and design constraints that preclude or seriously limit the detection of newly discovered miRNAs or previously unmapped, novel miRNAs [ 44].
Perhaps the most important contribution of microarrays to breast cancer research has been the identification of gene sets that are predictive of patient outcome in breast cancer [ 5, 10- 14], with an accuracy that surpasses traditional predictive factors.
