Clustering-based approaches to SAGE data mining.

Haiying Wang, Huiru Zheng, Francisco Azuaje

Research output: Contribution to journalArticle

Abstract

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.
LanguageEnglish
Pages5
JournalBioData Mining
Volume1
Issue number1
Publication statusPublished - 2008

Fingerprint

Data Mining
Gene Expression Data
Gene expression
Gene Expression
Cluster Analysis
Data mining
Clustering
Biomedical Applications
Data visualization
Data Visualization
Biomarkers
Gene Expression Profiling
Profiling
Cancer
Genes
Gene
Prediction
Research
Neoplasms
Review

Cite this

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Clustering-based approaches to SAGE data mining. / Wang, Haiying; Zheng, Huiru; Azuaje, Francisco.

In: BioData Mining, Vol. 1, No. 1, 2008, p. 5.

Research output: Contribution to journalArticle

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