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¡¾À´Ô´¡¿Havard University
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¡¾ÌâÄ¿¡¿Detecting Novel Associations in Large Data Sets
¡¾Ò³Âë¡¿ÆÚ¾íÒ³£º 12/16/2011 µÚ334¾í µÚ6062ÆÚ 1518~1524Ò³
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¡¾Á´½Ó¡¿http://www.sciencemag.org/content/334/6062/1518
¡¾DOI¡¿ 10.1126/science.1205438

¡¾ÕªÒª¡¿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.
×÷ ÕߣºReshef, David N.; Reshef, Yakir A.; Finucane, Hilary K.; Grossman, Sharon R.; McVean, Gilean; Turnbaugh, Peter J.; Lander, Eric S.; Mitzenmacher, Michael; Sabeti, Pardis C.

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