  We identify fundamental issues with discretization when estimating
information-theoretic quantities in the analysis of data. These difficulties
are theoretical in nature and arise with discrete datasets carrying significant
implications for the corresponding claims and results. Here we describe the
origins of the methodological problems, and provide a clear illustration of
their impact with the example of biological network reconstruction. We propose
an algorithm (shared information metric) that corrects for the biases and the
resulting improved performance of the algorithm demonstrates the need to take
due consideration of this issue in different contexts.
