Detecting novel associations in large data sets.
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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. |
Year of Publication | 2011
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Journal | Science
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Volume | 334
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Issue | 6062
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Pages | 1518-24
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Date Published | 2011 Dec 16
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ISSN | 1095-9203
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URL | |
DOI | 10.1126/science.1205438
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PubMed ID | 22174245
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PubMed Central ID | PMC3325791
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Grant list | P50 GM068763-09 / GM / NIGMS NIH HHS / United States
P50 GM068763 / GM / NIGMS NIH HHS / United States
T32 GM007753 / GM / NIGMS NIH HHS / United States
U54 GM088558-03 / GM / NIGMS NIH HHS / United States
U54 GM088558 / GM / NIGMS NIH HHS / United States
090532 / Wellcome Trust / United Kingdom
U54GM088558 / GM / NIGMS NIH HHS / United States
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