Identifying differentially expressed subnetworks with MMG

Josselin Noirel; Guido Sanguinetti; Phillip C Wright

doi:10.1093/bioinformatics/btn499

Identifying differentially expressed subnetworks with MMG

Bioinformatics. 2008 Dec 1;24(23):2792-3. doi: 10.1093/bioinformatics/btn499. Epub 2008 Sep 25.

Authors

Josselin Noirel¹, Guido Sanguinetti, Phillip C Wright

Affiliation

¹ Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University 10 of Sheffield, Mappin Street, Sheffield, UK. j.noirel@sheffield.ac.uk

PMID: 18819939
DOI: 10.1093/bioinformatics/btn499

Abstract

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/.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology / methods*
Gene Expression Profiling / methods
Gene Expression Regulation*
Gene Regulatory Networks
Models, Statistical*
Proteomics
Software