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|a Reshef, David N.
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|a Whitaker College of Health Sciences and Technology
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|a Massachusetts Institute of Technology. Department of Biology
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Reshef, David N.
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|a Reshef, Yakir
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|a Grossman, Sharon Rachel
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|a Lander, Eric S.
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|a Reshef, Yakir
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|a Grossman, Sharon Rachel
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|a Finucane, Hilary Kiyo
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|a McVean, Gilean
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|a Turnbaugh, Peter J.
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|a Mitzenmacher, Michael
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|a Sabeti, Pardis C.
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|a Lander, Eric Steven
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|a Detecting Novel Associations in Large Data Sets
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|b American Association for the Advancement of Science (AAAS),
|c 2014-02-03T13:18:52Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/84636
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|a 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[superscript 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.
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|a National Institute of General Medical Sciences (U.S.) (Medical Scientist Training Program)
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|a en_US
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|a Article
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|t Science
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