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PLoS One. 2014 Feb 19;9(2):e87357. doi: 10.1371/journal.pone.0087357. eCollection 2014.

Mutual information between discrete and continuous data sets.

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  • 1Department of Physics, University of Washington, Seattle, Washington, United States of America.


Mutual information (MI) is a powerful method for detecting relationships between data sets. There are accurate methods for estimating MI that avoid problems with "binning" when both data sets are discrete or when both data sets are continuous. We present an accurate, non-binning MI estimator for the case of one discrete data set and one continuous data set. This case applies when measuring, for example, the relationship between base sequence and gene expression level, or the effect of a cancer drug on patient survival time. We also show how our method can be adapted to calculate the Jensen-Shannon divergence of two or more data sets.

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