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| Publication Type | Journal Article |
| Authors | Reshef, DN, Reshef YA, Finucane HK, Grossman SR, McVean G., Turnbaugh PJ, Lander E. S., Mitzenmacher M., and Sabeti PC |
| 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 (New York, N.Y.) |
| Volume | 334 |
| Issue | 6062 |
| Pages | 1518-24 |
| Date Published (YYYY/MM/DD) | 2011/12/16 |
| ISSN Number | 0036-8075 |
| DOI | 10.1126/science.1205438 |
| PubMed | http://www.ncbi.nlm.nih.gov/pubmed/22174245?dopt=Abstract |
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