|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Reshef, DN, Reshef, YA, Finucane, HK, Grossman, SR, McVean, G, Turnbaugh, PJ, Lander, ES, Mitzenmacher, M, Sabeti, PC|
|Journal||Science (New York, N.Y.)|
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.