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Clustering of incomplete, high dimensional and dependent biological data with SpaCEM3

Matthieu Vignes 1 Juliette Blanchet 2 Damien Leroux 1 Florence Forbes 3 
3 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : The SpaCEM3 software makes the most of variational estimates in Markov Random Field (MRF) models to cluster (i) high-dimensional (ii) dependent and (iii) incomplete data. This methodology has been applied to draw meaningful modules of genes from high-throughput data. To download the latest version, visit http://spacem3.gforge.inria.fr/
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Conference papers
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https://hal.inria.fr/hal-00780725
Contributor : Florence Forbes Connect in order to contact the contributor
Submitted on : Thursday, January 24, 2013 - 4:36:49 PM
Last modification on : Thursday, January 20, 2022 - 5:30:14 PM

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  • HAL Id : hal-00780725, version 1

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Matthieu Vignes, Juliette Blanchet, Damien Leroux, Florence Forbes. Clustering of incomplete, high dimensional and dependent biological data with SpaCEM3. Journée Satellite JOBIM MODGRAPH 2010, Sep 2010, Montpellier, France. ⟨hal-00780725⟩

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