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» Bayesian Networks Learning for Gene Expression Datasets
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BMCBI
2008
122views more  BMCBI 2008»
14 years 9 months ago
Reconstructing networks of pathways via significance analysis of their intersections
Background: Significance analysis at single gene level may suffer from the limited number of samples and experimental noise that can severely limit the power of the chosen statist...
Mirko Francesconi, Daniel Remondini, Nicola Nerett...
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IJBRA
2006
59views more  IJBRA 2006»
14 years 9 months ago
Predicting altered pathways using extendable scaffolds
: Many diseases, especially solid tumors, involve the disruption or deregulation of cellular processes. Most current work using gene expression and other high-throughput data, simp...
B. M. Broom, T. J. McDonnell, D. Subramanian
BMCBI
2008
163views more  BMCBI 2008»
14 years 9 months ago
The Annotation, Mapping, Expression and Network (AMEN) suite of tools for molecular systems biology
Background: High-throughput genome biological experiments yield large and multifaceted datasets that require flexible and user-friendly analysis tools to facilitate their interpre...
Frédéric Chalmel, Michael Primig
BMCBI
2005
151views more  BMCBI 2005»
14 years 9 months ago
stam - a Bioconductor compliant R package for structured analysis of microarray data
Background: Genome wide microarray studies have the potential to unveil novel disease entities. Clinically homogeneous groups of patients can have diverse gene expression profiles...
Claudio Lottaz, Rainer Spang
BMCBI
2007
144views more  BMCBI 2007»
14 years 9 months ago
Accelerated search for biomolecular network models to interpret high-throughput experimental data
Background: The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein e...
Suman Datta, Bahrad A. Sokhansanj