@article{ReshefEtAl2011,
	author = {Reshef, David N. and
			Reshef, Yakir A. and
			Finucane, Hilary K. and
			Grossman, Sharon R. and
			McVean, Gilean and
			Turnbaugh, Peter J. and
			Lander, Eric S. and
			Mitzenmacher, Michael and
			Sabeti, Pardis C.},
	title = {Detecting Novel Associations in Large Data Sets},
	volume = {334},
	number = {6062},
	pages = {1518-1524},
	year = {2011},
	doi = {10.1126/science.1205438},
	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 (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.},
	URL = {http://www.sciencemag.org/content/334/6062/1518.abstract},
	eprint = {http://www.sciencemag.org/content/334/6062/1518.full.pdf},
	journal = {Science}
}