Information Entropy of Biological Data: An Empirical Analysis
This paper presents a unified empirical analysis across six biological data modalities demonstrating that finite-sample bias severely distorts information-theoretic estimates, and argues that reporting entropy and mutual information without specifying the estimator and sample size is indefensible given that bias-corrected methods reveal distinct biological signals and correct systematic underestimation of entropy and overestimation of information content.