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Reference: Reshef DN, et al. (2011) Detecting novel associations in large data sets. Science 334(6062):1518-24

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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.

Reference Type
Journal Article | Research Support, Non-U.S. Gov't | Research Support, N.I.H., Extramural | Research Support, U.S. Gov't, Non-P.H.S.
Authors
Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC
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