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» Two-phase clustering strategy for gene expression data sets
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RECOMB
2007
Springer
15 years 9 months ago
Comparative Analysis of Spatial Patterns of Gene Expression in Drosophila melanogaster Imaginal Discs
Determining the precise spatial extent of expression of genes across different tissues, along with knowledge of the biochemical function of the genes is critical for understanding ...
Cyrus L. Harmon, Parvez Ahammad, Ann Hammonds, Ric...
APBC
2004
132views Bioinformatics» more  APBC 2004»
14 years 11 months ago
A Novel Feature Selection Method to Improve Classification of Gene Expression Data
This paper introduces a novel method for minimum number of gene (feature) selection for a classification problem based on gene expression data with an objective function to maximi...
Liang Goh, Qun Song, Nikola K. Kasabov
BMCBI
2005
190views more  BMCBI 2005»
14 years 9 months ago
An Entropy-based gene selection method for cancer classification using microarray data
Background: Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of n...
Xiaoxing Liu, Arun Krishnan, Adrian Mondry
AIIA
2009
Springer
15 years 4 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...
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BMCBI
2007
166views more  BMCBI 2007»
14 years 9 months ago
How to decide which are the most pertinent overly-represented features during gene set enrichment analysis
Background: The search for enriched features has become widely used to characterize a set of genes or proteins. A key aspect of this technique is its ability to identify correlati...
Roland Barriot, David J. Sherman, Isabelle Dutour