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» Informative gene selection and design of regulatory networks...
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JMLR
2008
209views more  JMLR 2008»
13 years 8 months ago
Bayesian Inference and Optimal Design for the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Matthias W. Seeger
BMCBI
2006
116views more  BMCBI 2006»
13 years 8 months ago
Optimized mixed Markov models for motif identification
Background: Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challengi...
Weichun Huang, David M. Umbach, Uwe Ohler, Leping ...
BMCBI
2007
146views more  BMCBI 2007»
13 years 8 months ago
Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data
Background: Transcriptional modules (TM) consist of groups of co-regulated genes and transcription factors (TF) regulating their expression. Two high-throughput (HT) experimental ...
Xiangdong Liu, Walter J. Jessen, Siva Sivaganesan,...
BMCBI
2007
148views more  BMCBI 2007»
13 years 8 months ago
Evaluation of gene-expression clustering via mutual information distance measure
Background: The definition of a distance measure plays a key role in the evaluation of different clustering solutions of gene expression profiles. In this empirical study we compa...
Ido Priness, Oded Maimon, Irad E. Ben-Gal
BMCBI
2010
167views more  BMCBI 2010»
13 years 8 months ago
Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach
Background: Transcriptional gene regulation is one of the most important mechanisms in controlling many essential cellular processes, including cell development, cell-cycle contro...
Marc Bailly-Bechet, Alfredo Braunstein, Andrea Pag...