Journal Article

Identifying differentially expressed subnetworks with MMG

Josselin Noirel, Guido Sanguinetti and Phillip C. Wright

in Bioinformatics

Volume 24, issue 23, pages 2792-2793
Published in print December 2008 | ISSN: 1367-4803
Published online September 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn499
Identifying differentially expressed subnetworks with MMG

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Background: Mixture model on graphs (MMG) is a probabilistic model that integrates network topology with (gene, protein) expression data to predict the regulation state of genes and proteins. It is remarkably robust to missing data, a feature particularly important for its use in quantitative proteomics. A new implementation in C and interfaced with R makes MMG extremely fast and easy to use and to extend.

Availability: The original implementation (Matlab) is still available from http://www.dcs.shef.ac.uk/~guido/; the new implementation is available from http://wrightlab.group.shef.ac.uk/people_noirel.htm, from CRAN, and has been submitted to BioConductor, http://www.bioconductor.org/.

Contact: j.noirel@sheffield.ac.uk

Journal Article.  1109 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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