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» Algorithms for Finding Gene Clusters
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BMCBI
2007
166views more  BMCBI 2007»
15 years 3 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
GECCO
2007
Springer
162views Optimization» more  GECCO 2007»
15 years 9 months ago
A multi-objective approach to discover biclusters in microarray data
The main motivation for using a multi–objective evolutionary algorithm for finding biclusters in gene expression data is motivated by the fact that when looking for biclusters ...
Federico Divina, Jesús S. Aguilar-Ruiz
145
Voted
BMCBI
2010
139views more  BMCBI 2010»
15 years 3 months ago
A highly efficient multi-core algorithm for clustering extremely large datasets
Background: In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput t...
Johann M. Kraus, Hans A. Kestler
145
Voted
BMCBI
2002
136views more  BMCBI 2002»
15 years 3 months ago
Making sense of EST sequences by CLOBBing them
Background: Expressed sequence tags (ESTs) are single pass reads from randomly selected cDNA clones. They provide a highly cost-effective method to access and identify expressed g...
John Parkinson, David B. Guiliano, Mark L. Blaxter
148
Voted
BMCBI
2006
170views more  BMCBI 2006»
15 years 3 months ago
Biclustering of gene expression data by non-smooth non-negative matrix factorization
Background: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of gene...
Pedro Carmona-Saez, Roberto D. Pascual-Marqui, Fra...