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GENESHIFT: A Nonparametric Approach for Integrating Microarray Gene Expression Data Based on the Inner Product as a Distance Measure between the Distributions of Genes
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Cosmin Lazar,Jonatan Taminau,Stijn Meganck,David Steenhoff,Alain Coletta,David Y. Weiss Solis,Colin Molter,Robin Duque,Hugues Bersini,Ann Nowe
Issue Date:March 2013
pp. 383-392
The potential of microarray gene expression (MAGE) data is only partially explored due to the limited number of samples in individual studies. This limitation can be surmounted by merging or integrating data sets originating from independent MAGE experimen...
 
Distributed learning of Multi-Agent Causal Models
Found in: Intelligent Agent Technology, IEEE / WIC / ACM International Conference on
By Stijn Meganck, Sam Maes, Bernard Manderick, Philippe Leray
Issue Date:September 2005
pp. 285-288
<p>In this paper we propose a distributed structure learning algorithm for the recently introduced Multi-Agent Causal Models (MACMs). MACMs are an extension of Causal Bayesian Networks (CBN) to a distributed domain. In this setting it is assumed that...
 
GENESHIFT: A Nonparametric Approach for Integrating Microarray Gene Expression Data Based on the Inner Product as a Distance Measure between the Distributions of Genes
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Alain Coletta, Ann Nowe, Colin Molter, Cosmin Lazar, David Steenhoff, David Y. Weiss Solis, Hugues Bersini, Jonatan Taminau, Robin Duque, Stijn Meganck
Issue Date:March 2013
pp. 383-392
The potential of microarray gene expression (MAGE) data is only partially explored due to the limited number of samples in individual studies. This limitation can be surmounted by merging or integrating data sets originating from independent MAGE experimen...
     
A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Alain Coletta, Ann Nowe, Colin Molter, Cosmin Lazar, David Steenhoff, Hugues Bersini, Jonatan Taminau, Robin Duque, Stijn Meganck, Virginie de Schaetzen
Issue Date:July 2012
pp. 1106-1119
A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas li...
     
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