Detecting novel associations in large data sets.

Science
Authors
Keywords
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
Journal
Science
Volume
334
Issue
6062
Pages
1518-24
Date Published
2011 Dec 16
ISSN
1095-9203
URL
DOI
10.1126/science.1205438
PubMed ID
22174245
PubMed Central ID
PMC3325791
Links
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