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» Smoothing Gene Expression Using Biological Networks
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BMCBI
2010
133views more  BMCBI 2010»
13 years 7 months ago
New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data
Background: Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant info...
Anup Parikh, Eryong Huang, Christopher Dinh, Blaz ...
BMCBI
2010
178views more  BMCBI 2010»
13 years 7 months ago
Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests
Background: Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example K...
David Edwards, Gabriel C. G. de Abreu, Rodrigo Lab...
BIOINFORMATICS
2002
146views more  BIOINFORMATICS 2002»
13 years 7 months ago
A duplication growth model of gene expression networks
Motivation: There has been considerable interest in developing computational techniques for inferring genetic regulatory networks from whole-genome expression profiles. When expre...
Ashish Bhan, David J. Galas, T. Gregory Dewey
BMCBI
2007
164views more  BMCBI 2007»
13 years 7 months ago
Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks
Background: The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a...
Peng Li, Chaoyang Zhang, Edward J. Perkins, Ping G...
BIBE
2006
IEEE
139views Bioinformatics» more  BIBE 2006»
14 years 1 months ago
A Computational Inference Framework for analyzing Gene Regulation Pathway using Microarray Data
Microarray experiments produce gene expression data at such a high speed and volume that it is imperative to use highly specialized computational tools for their analyses. One grou...
Dong-Guk Shin, John Bluis, Yoo Ah Kim, Winfried Kr...