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BMCBI
2007
143views more  BMCBI 2007»
13 years 8 months ago
Gene selection for classification of microarray data based on the Bayes error
Background: With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, ...
Ji-Gang Zhang, Hong-Wen Deng
CBMS
2005
IEEE
14 years 1 months ago
An Ontology-Driven Clustering Method for Supporting Gene Expression Analysis
The Gene Ontology (GO) is an important knowledge resource for biologists and bioinformaticians. This paper explores the integration of similarity information derived from GO into ...
Haiying Wang, Francisco Azuaje, Olivier Bodenreide...
BMCBI
2008
102views more  BMCBI 2008»
13 years 8 months ago
Response projected clustering for direct association with physiological and clinical response data
Background: Microarray gene expression data are often analyzed together with corresponding physiological response and clinical metadata of biological subjects, e.g. patients'...
Sung-Gon Yi, Taesung Park, Jae K. Lee
AIIA
2009
Springer
14 years 2 months ago
Ontology-Driven Co-clustering of Gene Expression Data
Abstract. The huge volume of gene expression data produced by microarrays and other high-throughput techniques has encouraged the development of new computational techniques to eva...
Francesca Cordero, Ruggero G. Pensa, Alessia Visco...
IJDMB
2008
117views more  IJDMB 2008»
13 years 8 months ago
Scoring and summarising gene product clusters using the Gene Ontology
: We propose an approach for quantifying the biological relatedness between gene products, based on their properties, and measure their similarities using exclusively statistical N...
Spiridon C. Denaxas, Christos Tjortjis