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¡¾À´Ô´¡¿Havard University ¡¾ÄÚÈÝ¡¿±¾ÔÂ16ÈÕScience³ö°æµÄÈçºÎÔÚ´óÊý¾Ý¼¯ÖÐѰÕÒ¹ØÏµµÄÂÛÎÄ£¬·Ç³£ÓÐÒâ˼£¬¿ÉÒÔ½è¼ø¡£ÎÒÒѾÏò×÷ÕßÒªÁËÏà¹ØµÄ´úÂ룬µÈ´ý»Ø¸´¡£ ¡¾ÌâÄ¿¡¿Detecting Novel Associations in Large Data Sets ¡¾Ò³Âë¡¿ÆÚ¾íÒ³£º 12/16/2011 µÚ334¾í µÚ6062ÆÚ 1518~1524Ò³ ¡¾ÁìÓò¡¿ÐÅÏ¢¿ÆÑ§ » ¼ÆËã»ú¿ÆÑ§ » ¼ÆËã»ú¿ÆÑ§µÄ»ù´¡ÀíÂÛ ¡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. [ À´×Ô¿ÆÑмÒ×å ¿ìÀÖ¼Ò×å ] |
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