Transfer entropy is a directed and non-parametric measure which estimates how well the information contained in the past of one time series, the source, can be used to predict the future of another, the destination. Consequently, transfer entropy has proved to be an important metric for detecting the presence and magnitude of causality within dynamical networks. Since the amount of information contained within a dynamical system differs between systems, it is important to normalize the transfer entropy by representing it as the proportion of the maximum amount of information that can be reduced in order to accurately compare different values. Using the normalized transfer entropy, we can construct causal networks to visualize the important interactions between different nodes in a system. Particularly, this is applied to the lac and gal protein-DNA complexes to elucidate the sources and directions of the interaction within protein-DNA complexes.

Derivation and applications of a normalized KSG transfer entropy

ACS Spring Meeting 2021

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