Information theory is increasingly adopted in many areas of computational biology and biomedical research. The concepts and ideas pioneered in Shannon's work are the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. Thanks to the availability of increasingly enriched datasets and the development of domain-specific expertise, the information theory has increasingly proven useful in particular as a component of machine learning and deep learning techniques, to mine information obtained from different modalities for enhancing data understanding and perception, realizing cross-domain applications.T Presentations are expected to explore the multiple methodological intersections of the information theory in biology, physiology and health of current and future interest and Lectures will provide a broad overview and deep insights in established areas of research. Emerging problems in the field will be highlighted. The event is also part of the series "Challenges in Data Science: a Complex Systems perspective" regularly organized by the Politecnico di Torino.