Gene expression microarray技术的迅速进步使得发现了用于癌症诊断、预后和预测的分子标记。使用microarray数据分析创建癌症分类器的一个计算难题是如何有效地处理由高维属性（p）和低维实例（n）组成的microarray数据。基因选择和分类器构建是与此议题相关的两个关键问题。在本文中，我们回顾了计算识别癌症标记基因的主要方法。我们得出结论，应该首选简单的方法，因为它们具有可解释性和适用性。

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. We concluded that simple methods should be preferred to complicated ones for their interpretability and applicability.