Identifying differentially expressed subnetworks with MMG. Export

@article{citeulike:3371274, 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/.}, author = {Noirel, J. and Sanguinetti, G. and Wright, P. C.}, citeulike-article-id = {3371274}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btn499}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/24/23/2792}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/18819939}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=18819939}, day = {1}, doi = {10.1093/bioinformatics/btn499}, issn = {1460-2059}, journal = {Bioinformatics (Oxford, England)}, keywords = {expression, gene, network, paper}, month = {December}, number = {23}, pages = {2792--2793}, posted-at = {2008-11-21 16:08:04}, priority = {2}, title = {Identifying differentially expressed subnetworks with MMG.}, url = {http://dx.doi.org/10.1093/bioinformatics/btn499}, volume = {24}, year = {2008} }

TY - JOUR ID - citeulike:3371274 L3 - citeulike-article-id:3371274 N2 - 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/. IS - 23 JF - Bioinformatics (Oxford, England) SN - 1460-2059 EP - 2793 TI - Identifying differentially expressed subnetworks with MMG. VL - 24 SP - 2792 KW - expression KW - gene KW - network KW - paper AU - Noirel, J AU - Sanguinetti, G AU - Wright, PC PY - 2008/12/01/ UR - http://dx.doi.org/10.1093/bioinformatics/btn499 ER -