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» Class discovery in gene expression data
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BMCBI
2008
160views more  BMCBI 2008»
13 years 7 months ago
Cross-species and cross-platform gene expression studies with the Bioconductor-compliant R package 'annotationTools'
Background: The variety of DNA microarray formats and datasets presently available offers an unprecedented opportunity to perform insightful comparisons of heterogeneous data. Cro...
Alexandre Kuhn, Ruth Luthi-Carter, Mauro Delorenzi
BMCBI
2006
156views more  BMCBI 2006»
13 years 7 months ago
GOFFA: Gene Ontology For Functional Analysis - A FDA Gene Ontology Tool for Analysis of Genomic and Proteomic Data
Background: Gene Ontology (GO) characterizes and categorizes the functions of genes and their products according to biological processes, molecular functions and cellular componen...
Hongmei Sun, Hong Fang, Tao Chen, Roger Perkins, W...
TCBB
2010
176views more  TCBB 2010»
13 years 6 months ago
Feature Selection for Gene Expression Using Model-Based Entropy
—Gene expression data usually contain a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best...
Shenghuo Zhu, Dingding Wang, Kai Yu, Tao Li, Yihon...
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, ...
BMCBI
2004
134views more  BMCBI 2004»
13 years 7 months ago
Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)
Background: An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature ...
Ricardo Z. N. Vêncio, Helena Brentani, Diogo...