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» A Repulsive Clustering Algorithm for Gene Expression Data
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BIBE
2004
IEEE
107views Bioinformatics» more  BIBE 2004»
13 years 11 months ago
Enhanced pClustering and Its Applications to Gene Expression Data
Clustering has been one of the most popular methods to discover useful biological insights from DNA microarray. An interesting paradigm is simultaneous clustering of both genes an...
Sungroh Yoon, Christine Nardini, Luca Benini, Giov...
AUSAI
2005
Springer
14 years 1 months ago
Finding Similar Patterns in Microarray Data
Abstract. In this paper we propose a clustering algorithm called sCluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight cluster...
Xiangsheng Chen, Jiuyong Li, Grant Daggard, Xiaodi...
ICANN
2009
Springer
13 years 5 months ago
Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data
Different algorithms have been proposed in the literature to cluster gene expression data, however there is no single algorithm that can be considered the best one independently on...
André C. A. Nascimento, Ricardo Bastos Cava...
ICASSP
2008
IEEE
14 years 2 months ago
Probabilistic framework for gene expression clustering validation based on gene ontology and graph theory
Based on the correlation between expression and ontologydriven gene similarity, we incorporate functional annotations into gene expression clustering validation. A probabilistic f...
Yinyin Yuan, Chang-Tsun Li
BMCBI
2006
213views more  BMCBI 2006»
13 years 7 months ago
CoXpress: differential co-expression in gene expression data
Background: Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find...
Michael Watson