Wang, Haiying, Zheng, Huiru and Azuaje, Francisco (2008) Clustering-based approaches to SAGE data mining. BioData mining, 1 (1). p. 5. [Journal article]
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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.
|Item Type:||Journal article|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Mathematics
|Research Institutes and Groups:||Computer Science Research Institute > Smart Environments|
Computer Science Research Institute > Artificial Intelligence and Applications
|Deposited By:||Dr Huiru Zheng|
|Deposited On:||03 Feb 2010 10:33|
|Last Modified:||09 May 2016 10:55|
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