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» Evaluation of clustering algorithms for gene expression data
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101
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BMCBI
2010
153views more  BMCBI 2010»
14 years 9 months ago
Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering
Background: Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or su...
Eva Freyhult, Mattias Landfors, Jenny Önskog,...
GECCO
2007
Springer
162views Optimization» more  GECCO 2007»
15 years 3 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
79
Voted
BMCBI
2008
121views more  BMCBI 2008»
14 years 9 months ago
GeneTrailExpress: a web-based pipeline for the statistical evaluation of microarray experiments
Background: High-throughput methods that allow for measuring the expression of thousands of genes or proteins simultaneously have opened new avenues for studying biochemical proce...
Andreas Keller, Christina Backes, Maher Al-Awadhi,...
78
Voted
NIPS
2003
14 years 11 months ago
Gene Expression Clustering with Functional Mixture Models
We propose a functional mixture model for simultaneous clustering and alignment of sets of curves measured on a discrete time grid. The model is specifically tailored to gene exp...
Darya Chudova, Christopher E. Hart, Eric Mjolsness...
96
Voted
BMCBI
2007
143views more  BMCBI 2007»
14 years 9 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