Quantifying Randomness and Information in Biological Data

This lecture covers the concept of entropy in the context of biological data analysis, focusing on quantifying randomness and information. The instructor discusses the notion of entropy, its interpretation, and its application in neuroscience data analysis. The lecture also delves into quantifying statistical dependence, inferring probability distributions, and predicting protein structure from sequence data. Various examples, such as the Luria-Delbrück experiment and protein abundances across cells, are used to illustrate the concepts.