Lecture
This lecture covers the concept of mutual information applied to biological data, focusing on quantifying randomness and information in data, statistical dependence, and inferring probability distributions. It delves into the relationship between covariance, correlation, and mutual information, highlighting the ability of mutual information to detect nonlinear forms of statistical dependence. The lecture also explores the use of mutual information in analyzing protein sequence data, emphasizing its independence from specific values. Additionally, it discusses the application of mutual information in understanding positional information in development and the rewiring of two-component signaling systems in biological contexts.