Abstract
Cancer is one of the dreadful diseases, which causes a considerable death rate in humans. Cancer is featured by an irregular, unmanageable growth that may demolish and attack neighboring healthy body tissues or somewhere else in the body. Microarray based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. In recent years, DNA microarray technique has gained more attraction in both scientific and in industrial fields. It showed great importance in determining the informative genes that can cause the cancer. This led to improvements in early cancer diagnosis and in giving effective chemotherapy treatment. Studding cancer microarray gene expression data is a challenging task because microarray is high dimensional-low sample dataset with a lot of noisy or irrelevant genes and missing data. In this paper, we conduct a comprehensive study that focuses on exploring the main objectives and approaches that have been applied using cancer microarray gene expression profile. We proceed by making a classification for all approaches, and then conclude by investigating the most efficient approaches that can be used in this field.