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dc.contributor.authorWu, Chang-Jiunen_US
dc.contributor.authorKasif, Simonen_US
dc.date.accessioned2012-01-11T00:39:15Z
dc.date.available2012-01-11T00:39:15Z
dc.date.issued2005-06-27
dc.identifier.citationWu, Chang-Jiun, Simon Kasif. "GEMS: a web server for biclustering analysis of expression data" Nucleic Acids Research 33(Web Server issue): W596-W599. (2005)
dc.identifier.issn1362-4962
dc.identifier.urihttps://hdl.handle.net/2144/3025
dc.description.abstractThe advent of microarray technology has revolutionized the search for genes that are differentially expressed across a range of cell types or experimental conditions. Traditional clustering methods, such as hierarchical clustering, are often difficult to deploy effectively since genes rarely exhibit similar expression pattern across a wide range of conditions. Biclustering of gene expression data (also called co-clustering or two-way clustering) is a non-trivial but promising methodology for the identification of gene groups that show a coherent expression profile across a subset of conditions. Thus, biclustering is a natural methodology as a screen for genes that are functionally related, participate in the same pathways, affected by the same drug or pathological condition, or genes that form modules that are potentially co-regulated by a small group of transcription factors. We have developed a web-enabled service called GEMS (Gene Expression Mining Server) for biclustering microarray data. Users may upload expression data and specify a set of criteria. GEMS then performs bicluster mining based on a Gibbs sampling paradigm. The web server provides a flexible and an useful platform for the discovery of co-expressed and potentially co-regulated gene modules. GEMS is an open source software and is available at http://genomics10.bu.edu/terrence/gems/.en_US
dc.description.sponsorshipNational Science Foundation (DBI-0239435, ITR-048715); National Human Genome Research Institute (1R33HG002850-01A1); National Institutes of Health (U54 LM00874)en_US
dc.language.isoen
dc.publisherOxford University Pressen_US
dc.titleGEMS: A Web Server for Biclustering Analysis of Expression Dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1093/nar/gki469
dc.identifier.pmid15980544
dc.identifier.pmcid1160230


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