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Clustering-based approaches to SAGE data mining.

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

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 > Artificial Intelligence and Applications
Computer Science Research Institute > Smart Environments
ID Code:9081
Deposited By:Dr Huiru Zheng
Deposited On:03 Feb 2010 10:33
Last Modified:03 Feb 2010 10:33

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