(untitled) (International Journal of Advanced Research in Computer Science, 2017)
Microarray data are often extremely asymmetric in dimensionality, highly redundant and noisy. Most genes are believed to be uninformative with respect to studied classes. Extracting useful knowledge and information from the microarrays has attracted the attention of many biologists and computer scientists. This type of experiment allows to determine relative levels of mRNA abundance in a set of tissues or cell populations for thousands of genes simultaneously. Naturally, such an experiment requires computational and statistical analysis techniques. At the outset of the processing pipeline, the computational procedures are largely determined by the technology and experimental setup that are used. Subsequently, as more reliable intensity values for genes emerge, pattern discovery methods come into play. The most striking peculiarity of this kind of data is that one usually obtains measurements for thousands of genes for only a much smaller number of conditions.This article reviews the methods utilized in pre-processing of Microarray gene expression data. Keywords: mRNA, Microarray,Gene Expression.