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.

基因表达微阵列技术的快速发展使人们能够发现用于癌症诊断、预后和预测的分子标记。使用微阵列数据分析创建癌症分类器的一项计算挑战是如何有效处理由高维属性 (p) 和低维实例 (n) 组成的微阵列数据。基因选择和分类器构建是与该主题相关的两个关键问题。在本文中，我们回顾了基于微阵列基因表达数据计算识别癌症标记基因的主要方法。我们得出的结论是，简单方法的可解释性和适用性应该优于复杂的方法。