Review

Clustering-based approaches to SAGE data mining

Haiying Wang¹ , Huiru Zheng¹ and Francisco Azuaje²

¹  School of Computing and Mathematics, University of Ulster, Newtownabbey, BT37 0QB, Co. Antrim, Northern Ireland, UK

²  Research Centre for Public Health (CRP-Santé), Laboratory of Cardiovascular Research, 1A, rue Thomas Edison, L-1445, Strassen, Luxembourg

author email corresponding author email

BioData Mining 2008, 1:5doi:10.1186/1756-0381-1-5

Published:	17 July 2008

Abstract

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.