Survey on Gene Expression Data Analysis Based on Datamining

Resumen

DNA chips is also called as DNA microarray technology. It is a potential tool which aids
researchers to monitor the level of gene expression in an organism. The analysis of
microarray data affords productive results that contributes in solving the profile problems
related to gene expression. Emergence of DNA (Deoxyribonucleic Acid) microarray
technology assist the researcher to examine the expression of 1000 of genes simultaneously.
Analysis of microarray data is process of identifying relevant and irrelevant genes. For this
purpose there is a need of data mining technique. In upcoming year, the development of data
mining methods in research industries makes easy mining of useful data from large dataset.
Better knowledge of data mining methods is needed by the researcher to make use of
upcoming data mining techniques to enhance quality and efficiency of their findings. The
most common data mining techniques are classification and clustering. Where clustering is
unsupervised learning technique which groups data depending on similarity and distance
between them. Whereas classification allocate data items to targeted classes with intension of
detecting target class for every sample of data from dataset accurately. This survey
concentrates on recent data mining methods utilized in clustering and classification of gene
expression from micro array dataset. Main focus is done on unsupervised and supervised
technique which is utilized currently to categorize gene express for prognosis and diagnosis
of frightening disease. Here a comparison is made between existing and proposed classifiers
of data mining technique in terms of accuracy.