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David N. Reshef
Researcher at Massachusetts Institute of Technology
Publications - 18
Citations - 2914
David N. Reshef is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Maximal information coefficient & Mutual information. The author has an hindex of 11, co-authored 18 publications receiving 2301 citations. Previous affiliations of David N. Reshef include Broad Institute & University of Oxford.
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Journal ArticleDOI
Detecting Novel Associations in Large Data Sets
David N. Reshef,David N. Reshef,David N. Reshef,Yakir A. Reshef,Yakir A. Reshef,Hilary K. Finucane,Sharon R. Grossman,Sharon R. Grossman,Gilean McVean,Gilean McVean,Peter J. Turnbaugh,Eric S. Lander,Eric S. Lander,Eric S. Lander,Michael Mitzenmacher,Pardis C. Sabeti,Pardis C. Sabeti +16 more
TL;DR: A measure of dependence for two-variable relationships: the maximal information coefficient (MIC), which captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination of the data relative to the regression function.
Detecting Novel Associations in Large Data Sets
David N. Reshef,David N. Reshef,David N. Reshef,Yakir A. Reshef,Yakir A. Reshef,Hilary K. Finucane,Sharon R. Grossman,Sharon R. Grossman,Gilean McVean,Gilean McVean,Peter J. Turnbaugh,Eric S. Lander,Eric S. Lander,Eric S. Lander,Michael Mitzenmacher,Pardis C. Sabeti,Pardis C. Sabeti +16 more
TL;DR: The maximal information coefficient (MIC) as mentioned in this paper is a measure of dependence for two-variable relationships that 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.
Posted Content
Equitability Analysis of the Maximal Information Coefficient, with Comparisons
TL;DR: This work presents an intuition behind the equitability of MIC through the exploration of the maximization and normalization steps in its definition, and examines the speed and optimality of the approximation algorithm used to compute MIC.
Journal ArticleDOI
Measuring dependence powerfully and equitably
TL;DR: This paper introduces and characterize a population measure of dependence called MIC*, and introduces an efficient approach for computing MIC* from the density of a pair of random variables, and defines a new consistent estimator MICe for MIC* that is efficiently computable.
Journal ArticleDOI
Factors Related to Increasing Prevalence of Resistance to Ciprofloxacin and Other Antimicrobial Drugs in Neisseria gonorrhoeae, United States
Edward Goldstein,Robert D. Kirkcaldy,David N. Reshef,Stuart M. Berman,Hillard Weinstock,Pardis C. Sabeti,Carlos del Rio,Geraldine S. Hall,Edward W. Hook,Marc Lipsitch +9 more
TL;DR: Possible causes for the emergence of fluoroquinolone-resistant N. gonorrhoeae are investigated, especially among heterosexuals, and prevention efforts should be directed toward both populations.