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 Oral and poster 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. Posters will highlight emerging problems in the field. The event is also part of the series "Challenges in Data Science: a Complex Systems perspective" regularly organized by the Politecnico di Torino.