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Detecting novel associations in large data sets

szh123 添加于 2012-3-19 22:52 | 2328 次阅读 | 0 个评论

作者
Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC
摘要
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.
详细资料
- 关键词: Algorithms; Animals; Baseball/statistics & numerical data; *Data Interpretation, Statistical; Female; Gene Expression; Genes, Fungal; Genomics/methods; Humans; Intestines/microbiology; Male; Metagenome; Mice; Obesity; Saccharomyces cerevisiae/genetics
- 文献种类: Journal Article
- 期刊名称: Science (New York, N.Y.)
- 期刊缩写: Science
- 期卷页: 2011年第334卷第6062期 1518-1524页
- 地址: Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. dnreshef@mit.edu
- ISBN: 0036-8075
学科领域信息系统 » 理论信息学
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