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Detecting Novel Associations in Large Data Sets
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Detecting Novel Associations in Large Data Sets
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Journal Article
Detecting Novel Associations in Large Data Sets
2011
Overview
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.
Publisher
American Association for the Advancement of Science,The American Association for the Advancement of Science
Subject
/ Animals
/ Baseball - statistics & numerical data
/ Biological and medical sciences
/ Computer science; control theory; systems
/ Data Interpretation, Statistical
/ Data processing. List processing. Character string processing
/ Datasets
/ Diet
/ Exact sciences and technology
/ Female
/ Fundamental and applied biological sciences. Psychology
/ Genes
/ Humans
/ Male
/ Memory organisation. Data processing
/ Mice
/ Mines
/ Molecular and cellular biology
/ Obesity
/ Saccharomyces cerevisiae - genetics
/ Software
/ Yeasts
