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» Gene Expression Clustering with Functional Mixture Models
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BMCBI
2006
127views more  BMCBI 2006»
13 years 9 months ago
Using local gene expression similarities to discover regulatory binding site modules
Background: We present an approach designed to identify gene regulation patterns using sequence and expression data collected for Saccharomyces cerevisae. Our main goal is to rela...
Bartek Wilczynski, Torgeir R. Hvidsten, Andriy Kry...
BMCBI
2002
188views more  BMCBI 2002»
13 years 9 months ago
The limit fold change model: A practical approach for selecting differentially expressed genes from microarray data
Background: The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic an...
David M. Mutch, Alvin Berger, Robert Mansourian, A...
BMCBI
2008
179views more  BMCBI 2008»
13 years 9 months ago
Building pathway clusters from Random Forests classification using class votes
Background: Recent years have seen the development of various pathway-based methods for the analysis of microarray gene expression data. These approaches have the potential to bri...
Herbert Pang, Hongyu Zhao
CSB
2005
IEEE
124views Bioinformatics» more  CSB 2005»
14 years 2 months ago
Discovering Functional Transcription Factor Binding from Superimposed Gene Networks
The availability of entire genome sequences, coupled with genome-wide studies of gene expression, offers promise for discovering new pathways along with their regulatory programs....
Matthew T. Weirauch, Joshua M. Stuart
BMCBI
2010
153views more  BMCBI 2010»
13 years 9 months ago
GOAL: A software tool for assessing biological significance of genes groups
Background: Modern high throughput experimental techniques such as DNA microarrays often result in large lists of genes. Computational biology tools such as clustering are then us...
Alain B. Tchagang, Alexander Gawronski, Hugo B&eac...