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» Gene Expression Clustering with Functional Mixture Models
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ICMLA
2010
13 years 7 months ago
Smoothing Gene Expression Using Biological Networks
Gene expression (microarray) data have been used widely in bioinformatics. The expression data of a large number of genes from small numbers of subjects are used to identify inform...
Yue Fan, Mark A. Kon, Shinuk Kim, Charles DeLisi
BMCBI
2007
198views more  BMCBI 2007»
13 years 9 months ago
Correlation analysis reveals the emergence of coherence in the gene expression dynamics following system perturbation
Time course gene expression experiments are a popular means to infer co-expression. Many methods have been proposed to cluster genes or to build networks based on similarity measu...
Nicola Neretti, Daniel Remondini, Marc Tatar, John...
APBC
2004
132views Bioinformatics» more  APBC 2004»
13 years 10 months ago
A Novel Feature Selection Method to Improve Classification of Gene Expression Data
This paper introduces a novel method for minimum number of gene (feature) selection for a classification problem based on gene expression data with an objective function to maximi...
Liang Goh, Qun Song, Nikola K. Kasabov
BMCBI
2006
95views more  BMCBI 2006»
13 years 9 months ago
XcisClique: analysis of regulatory bicliques
Background: Modeling of cis-elements or regulatory motifs in promoter (upstream) regions of genes is a challenging computational problem. In this work, set of regulatory motifs si...
Amrita Pati, Cecilia Vasquez-Robinet, Lenwood S. H...
CSDA
2010
208views more  CSDA 2010»
13 years 9 months ago
Bayesian density estimation and model selection using nonparametric hierarchical mixtures
We consider mixtures of parametric densities on the positive reals with a normalized generalized gamma process (Brix, 1999) as mixing measure. This class of mixtures encompasses t...
Raffaele Argiento, Alessandra Guglielmi, Antonio P...