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    Gene set enrichment analysis: performance evaluation and usage guidelines

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    Authors
    Hung, Jui-Hung
    Yang, Tun-Hsiang
    Hu, Zhenjun
    Weng, Zhiping
    Delisi, Charles
    UMass Chan Affiliations
    Program in Bioinformatics and Integrative Biology
    Department of Biochemistry and Molecular Pharmacology
    Document Type
    Journal Article
    Publication Date
    2012-05-01
    Keywords
    Algorithms
    Computational Biology
    Databases, Genetic
    Gene Expression
    Guidelines as Topic
    Humans
    Bioinformatics
    Computational Biology
    Molecular Biology
    Systems Biology
    
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    Link to Full Text
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3357488/
    Abstract
    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.
    Source

    Brief Bioinform. 2012 May;13(3):281-91. doi: 10.1093/bib/bbr049. Epub 2011 Sep 7. Link to article on publisher's site

    DOI
    10.1093/bib/bbr049
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/25880
    PubMed ID
    21900207
    Related Resources

    Link to Article in PubMed

    ae974a485f413a2113503eed53cd6c53
    10.1093/bib/bbr049
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    Program in Bioinformatics and Integrative Biology Publications

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