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有附件Detecting novel associations in large data sets

十七岁的雨季 添加于 2014-11-27 13:04 | 924 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  334 6062 1518-1524
    • 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 » 计算机科学

  • 相关链接 DOI URL 

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