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APBC
2004
138views Bioinformatics» more  APBC 2004»
13 years 9 months ago
Whole-Genome Functional Classification of Genes by Latent Semantic Analysis on Microarray Data
Quantitative simultaneous monitoring of the expression levels of thousands of genes under various experimental conditions is now possible using microarray experiments. The resulti...
See-Kiong Ng, Zexuan Zhu, Yew-Soon Ong
BMCBI
2011
12 years 11 months ago
Clustering gene expression data with a penalized graph-based metric
Background: The search for cluster structure in microarray datasets is a base problem for the so-called “-omic sciences”. A difficult problem in clustering is how to handle da...
Ariel E. Bayá, Pablo M. Granitto
BMCBI
2004
150views more  BMCBI 2004»
13 years 7 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
2005
212views more  BMCBI 2005»
13 years 7 months ago
PAGE: Parametric Analysis of Gene Set Enrichment
Background: Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes i...
Seon-Young Kim, David J. Volsky
JCB
2002
160views more  JCB 2002»
13 years 7 months ago
Inference from Clustering with Application to Gene-Expression Microarrays
There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different u...
Edward R. Dougherty, Junior Barrera, Marcel Brun, ...