Rapid advances in gene expression microarray technology have enabled to discover molecular markers used for
cancer diagnosis, prognosis, and prediction. One computational challenge with using microarray data analysis to create
cancer classifiers is how to effectively deal with microarray data which are composed of high-dimensional attributes (p)
and low-dimensional instances (n). Gene selection and classifier construction are two key issues concerned with this
topics. In this article, we reviewed major methods for computational identification of cancer marker genes based on
microarray gene expression data. We concluded that simple methods should be preferred to complicated ones for their
interpretability and applicability.