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BMCBI
2010
178views more  BMCBI 2010»
13 years 5 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...
BMCBI
2007
148views more  BMCBI 2007»
13 years 5 months ago
p53FamTaG: a database resource of human p53, p63 and p73 direct target genes combining in silico prediction and microarray data
Background: The p53 gene family consists of the three genes p53, p63 and p73, which have polyhedral non-overlapping functions in pivotal cellular processes such as DNA synthesis a...
Elisabetta Sbisà, Domenico Catalano, Giorgi...
CSB
2005
IEEE
143views Bioinformatics» more  CSB 2005»
13 years 11 months ago
Multivariate gene selection: Does it help
When building predictors of disease state based on gene expression data, gene selection is performed in order to achieve a good performance and to identify a relevant subset of ge...
Carmen Lai, Marcel J. T. Reinders
BIRD
2008
Springer
113views Bioinformatics» more  BIRD 2008»
13 years 7 months ago
Matching Spatial Regions with Combinations of Interacting Gene Expression Patterns
The Edinburgh Mouse Atlas aims to capture in-situ gene expression patterns in a common spatial framework. In this study, we construct a grammar to define spatial regions by combina...
Jano I. van Hemert, Richard A. Baldock
BMCBI
2006
122views more  BMCBI 2006»
13 years 5 months ago
A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets
Background: Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifi...
Carmen Lai, Marcel J. T. Reinders, Laura J. van't ...