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dc.contributor.authorHung, Jui-Hung
dc.contributor.authorYang, Tun-Hsiang
dc.contributor.authorHu, Zhenjun
dc.contributor.authorWeng, Zhiping
dc.contributor.authorDelisi, Charles
dc.date2022-08-11T08:07:59.000
dc.date.accessioned2022-08-23T15:38:10Z
dc.date.available2022-08-23T15:38:10Z
dc.date.issued2012-05-01
dc.date.submitted2013-02-22
dc.identifier.citation<p>Brief Bioinform. 2012 May;13(3):281-91. doi: 10.1093/bib/bbr049. Epub 2011 Sep 7. <a href="http://dx.doi.org/10.1093/bib/bbr049">Link to article on publisher's site</a></p>
dc.identifier.issn1467-5463 (Linking)
dc.identifier.doi10.1093/bib/bbr049
dc.identifier.pmid21900207
dc.identifier.urihttp://hdl.handle.net/20.500.14038/25880
dc.description.abstractA central goal of biology is understanding and describing the molecular basis of plasticity: the sets of genes that are combinatorially selected by exogenous and endogenous environmental changes, and the relations among the genes. The most viable current approach to this problem consists of determining whether sets of genes are connected by some common theme, e.g. genes from the same pathway are overrepresented among those whose differential expression in response to a perturbation is most pronounced. There are many approaches to this problem, and the results they produce show a fair amount of dispersion, but they all fall within a common framework consisting of a few basic components. We critically review these components, suggest best practices for carrying out each step, and propose a voting method for meeting the challenge of assessing different methods on a large number of experimental data sets in the absence of a gold standard.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=21900207&dopt=Abstract">Link to Article in PubMed</a></p>
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3357488/
dc.subjectAlgorithms
dc.subjectComputational Biology
dc.subjectDatabases, Genetic
dc.subjectGene Expression
dc.subjectGuidelines as Topic
dc.subjectHumans
dc.subjectBioinformatics
dc.subjectComputational Biology
dc.subjectMolecular Biology
dc.subjectSystems Biology
dc.titleGene set enrichment analysis: performance evaluation and usage guidelines
dc.typeJournal Article
dc.source.journaltitleBriefings in bioinformatics
dc.source.volume13
dc.source.issue3
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/bioinformatics_pubs/22
dc.identifier.contextkey3761403
html.description.abstract<p>A central goal of biology is understanding and describing the molecular basis of plasticity: the sets of genes that are combinatorially selected by exogenous and endogenous environmental changes, and the relations among the genes. The most viable current approach to this problem consists of determining whether sets of genes are connected by some common theme, e.g. genes from the same pathway are overrepresented among those whose differential expression in response to a perturbation is most pronounced. There are many approaches to this problem, and the results they produce show a fair amount of dispersion, but they all fall within a common framework consisting of a few basic components. We critically review these components, suggest best practices for carrying out each step, and propose a voting method for meeting the challenge of assessing different methods on a large number of experimental data sets in the absence of a gold standard.</p>
dc.identifier.submissionpathbioinformatics_pubs/22
dc.contributor.departmentProgram in Bioinformatics and Integrative Biology
dc.contributor.departmentDepartment of Biochemistry and Molecular Pharmacology
dc.source.pages281-91


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