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Detecting novel associations in large data sets.
|Publication Type||Journal Article|
|Authors||Reshef, DN, Reshef YA, Finucane HK, Grossman SR, McVean G., Turnbaugh PJ, Lander E. S., Mitzenmacher M., and Sabeti PC|
|Abstract||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.|
|Year of Publication||2011|
|Journal||Science (New York, N.Y.)|
|Date Published (YYYY/MM/DD)||2011/12/16|