School of Computing and Mathematics, University of Ulster, Newtownabbey, BT37 0QB, Co, Antrim, Northern Ireland, UK.
BioData Min. 2008 Jul 17;1(1):5. doi: 10.1186/1756-0381-1-5.
Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation.
基因表达系列分析（SAGE）是进行全基因表达谱分析最强大的工具之一。它已促成了一些生物学发现和生物医学应用，如预测新的基因功能和鉴定人类癌症研究中的生物标志物。聚类技术已成为这些应用中的基本方法。本文回顾了专门为此类数据设计的相关聚类技术。它特别强调了当前在支持具有生物学意义的数据挖掘和可视化方面的局限性和机会。
