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» Effective similarity measures for expression profiles
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BIBE
2004
IEEE
120views Bioinformatics» more  BIBE 2004»
13 years 9 months ago
Identifying Projected Clusters from Gene Expression Profiles
In microarray gene expression data, clusters may hide in subspaces. Traditional clustering algorithms that make use of similarity measurements in the full input space may fail to ...
Kevin Y. Yip, David W. Cheung, Michael K. Ng, Kei-...
BMCBI
2004
150views more  BMCBI 2004»
13 years 5 months ago
Rank Difference Analysis of Microarrays (RDAM), a novel approach to statistical analysis of microarray expression profiling data
Background: A key step in the analysis of microarray expression profiling data is the identification of genes that display statistically significant changes in expression signals ...
Dietmar E. Martin, Philippe Demougin, Michael N. H...
BMCBI
2008
163views more  BMCBI 2008»
13 years 5 months ago
Nuclear staining and relative distance for quantifying epidermal differentiation in biomarker expression profiling
Background: The epidermal physiology results from a complex regulated homeostasis of keratinocyte proliferation, differentiation and death and is tightly regulated by a specific p...
Thora Pommerencke, Thorsten Steinberg, Hartmut Dic...
ICDE
2006
IEEE
167views Database» more  ICDE 2006»
14 years 7 months ago
Mining Shifting-and-Scaling Co-Regulation Patterns on Gene Expression Profiles
In this paper, we propose a new model for coherent clustering of gene expression data called reg-cluster. The proposed model allows (1) the expression profiles of genes in a clust...
Xin Xu, Ying Lu, Anthony K. H. Tung, Wei Wang 0010
BMCBI
2007
134views more  BMCBI 2007»
13 years 5 months ago
Nearest Neighbor Networks: clustering expression data based on gene neighborhoods
Background: The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both indiv...
Curtis Huttenhower, Avi I. Flamholz, Jessica N. La...