Several sets of external RNA controls are currently available for microarray research [ 4- 9], but few probes corresponding to the controls are available on commercial microarrays and there is no consistency across platforms.
Given recent progress, however, we suggest that the major problem for current microarray research is no longer validity of expression measurements, but rather, the reliability of inferences from the data, an issue more appropriately redressed by statistical approaches than by validation with a separate method.
Microarray research has shown that much more of the genome is transcribed into RNA but not translated into amino acid sequences, called non-coding RNA (Mattick, 2004).
Solving this challenge would be a major accomplishment in microarray research potentially allowing quantification of targets in biological samples.
A potential problem with microarray research is that sequence similarity between and within these families of closely related genes may lead to cross-hybridization.
In this sense, class imbalance is ubiquitous and prevalent in several applications such as microarray research [6], medical diagnosis [7], oil-bearing of reservoir recognition [8], or intrusion detection systems [9].
Two different tissue microarrays were obtained from two different sources (Tissue microarray research program at the National Institutes of Health and Clinomics Inc).
Recently whole genome microarray research tried to make neurons to make their own defense against Japanese encephalitis viral infection.
The trend in microarray research is towards generating larger sets of data for analyzing complex biological problems and diseases.
This limitation is particularly true for microarray research in psychiatry, where gene expression from different brain areas has been assessed comparing all affected subjects to non-affected subjects.
