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» Tests for gene clustering
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BMCBI
2006
165views more  BMCBI 2006»
15 years 19 days ago
Validation and functional annotation of expression-based clusters based on gene ontology
Background: The biological interpretation of large-scale gene expression data is one of the paramount challenges in current bioinformatics. In particular, placing the results in t...
Ralf Steuer, Peter Humburg, Joachim Selbig
BIOINFORMATICS
2007
195views more  BIOINFORMATICS 2007»
15 years 21 days ago
Context-dependent clustering for dynamic cellular state modeling of microarray gene expression
Motivation: High-throughput expression profiling allows researchers to study gene activities globally. Genes with similar expression profiles are likely to encode proteins that ma...
Shinsheng Yuan, Ker-Chau Li
112
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BMCBI
2007
148views more  BMCBI 2007»
15 years 17 days ago
Computation of significance scores of unweighted Gene Set Enrichment Analyses
Background: Gene Set Enrichment Analysis (GSEA) is a computational method for the statistical evaluation of sorted lists of genes or proteins. Originally GSEA was developed for in...
Andreas Keller, Christina Backes, Hans-Peter Lenho...
115
Voted
BMCBI
2011
14 years 7 months ago
A Platform for Processing Expression of Short Time Series (PESTS)
Background: Time course microarray profiles examine the expression of genes over a time domain. They are necessary in order to determine the complete set of genes that are dynamic...
Anshu Sinha, Marianthi Markatou
108
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BMCBI
2008
142views more  BMCBI 2008»
15 years 21 days ago
Genetic weighted k-means algorithm for clustering large-scale gene expression data
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...
Fang-Xiang Wu