Details

Autor(en) / Beteiligte

• Reshef, David N.
• Reshef, Yakir A.
• Finucane, Hilary K.
• Grossman, Sharon R.
• McVean, Gilean
• Turnbaugh, Peter J.
• Lander, Eric S.
• Mitzenmacher, Michael
• Sabeti, Pardis C.

Titel

Detecting Novel Associations in Large Data Sets

Ist Teil von

• Science (American Association for the Advancement of Science), 2011-12, Vol.334 (6062), p.1518-1524

Ort / Verlag

Washington, DC: American Association for the Advancement of Science

Erscheinungsjahr

2011

Link zum Volltext

Quelle

American Association for the Advancement of Science

Beschreibungen/Notizen

• 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 (R²) 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.