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» Clustering gene expression patterns
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BMCBI
2006
216views more  BMCBI 2006»
14 years 9 months ago
Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data
Background: Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfu...
Haiying Wang, Huiru Zheng, David Simpson, Francisc...
BMCBI
2006
119views more  BMCBI 2006»
14 years 9 months ago
LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates
Background: Non-negative matrix factorisation (NMF), a machine learning algorithm, has been applied to the analysis of microarray data. A key feature of NMF is the ability to iden...
Guoli Wang, Andrew V. Kossenkov, Michael F. Ochs
BIODATAMINING
2008
135views more  BIODATAMINING 2008»
14 years 10 months ago
Fast Gene Ontology based clustering for microarray experiments
Background: Analysis of a microarray experiment often results in a list of hundreds of diseaseassociated genes. In order to suggest common biological processes and functions for t...
Kristian Ovaska, Marko Laakso, Sampsa Hautaniemi
BMCBI
2010
97views more  BMCBI 2010»
14 years 4 months ago
A semi-parametric Bayesian model for unsupervised differential co-expression analysis
Background: Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined ...
Johannes M. Freudenberg, Siva Sivaganesan, Michael...
BMCBI
2006
112views more  BMCBI 2006»
14 years 9 months ago
A phylogenomic gene cluster resource: the Phylogenetically Inferred Groups (PhIGs) database
Background: We present here the PhIGs database, a phylogenomic resource for sequenced genomes. Although many methods exist for clustering gene families, very few attempt to create...
Paramvir S. Dehal, Jeffrey L. Boore