title: Identifying differentially expressed subnetworks with MMG.
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.
  
  
  BACKGROUND
  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/.
authors.affiliation: Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University 10 of Sheffield, Mappin Street, Sheffield, UK. j.noirel@sheffield.ac.uk
authors.forename: Josselin
authors.initials: J
authors.lastname: Noirel
authors.forename: Guido
authors.initials: G
authors.lastname: Sanguinetti
authors.forename: Phillip C
authors.initials: PC
authors.lastname: Wright
mesh_terms: D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D015233:Models, Statistical; D040901:Proteomics; D012984:Software