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Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R(2)) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.

Original publication




Journal article


Science (New York, N.Y.)

Publication Date





1518 - 1524


Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.


Intestines, Animals, Humans, Mice, Saccharomyces cerevisiae, Obesity, Data Interpretation, Statistical, Genomics, Gene Expression, Genes, Fungal, Algorithms, Baseball, Female, Male, Metagenome