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Detecting Novel Associations in Large Data Sets

renchunxiao 添加于 2011/12/19 15:07:57  3549次阅读 | 2次推荐 | 4个评论

Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we presenta measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide rangeof associations both functional and not, and for functional relationships provides a score that roughly equals the coefficientof determination (R2) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametricexploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in globalhealth, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.

作 者:Reshef, David N.; Reshef, Yakir A.; Finucane, Hilary K.; Grossman, Sharon R.; McVean, Gilean; Turnbaugh, Peter J.; Lander, Eric S.; Mitzenmacher, Michael; Sabeti, Pardis C.
期刊名称: Science
期卷页: 12/16/2011 第334卷 第6062期 1518~1524页
学科领域:信息科学 » 计算机科学 » 计算机科学的基础理论
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原文链接:http://www.sciencemag.org/content/334/6062/1518
DOI: 10.1126/science.1205438
ISBN: 0036-8075
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发表评论人: [2012/2/21 13:54:26] 
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