Unraveling the Immunological Code Classic and Explainable AI Methods in Vaccine Development

Discover how Machine Learning and Deep Learning have transformed vaccinology in this UA-cam video. Explore classic ML methods and cutting-edge AI explainability techniques in vaccine development.
Dive into the role of protein language models like ESM in understanding proteins and predicting vaccine properties. Learn about the significance of AI explainability in ensuring transparency, trust, and safety in vaccine design. Join us on a journey to understand the power of AI in decoding the immunological code and shaping the future of vaccines.
About Francesco Patanè
Francesco Patanè is an enthusiastic biotech scientist. He earned a Bachelor's degree in Biotechnology (Pharmaceutical Focus) from the University of Padova, where he investigated metabolic engineering in cannabis to enhance cannabinoid production. Over the past ten months, he has been engaged in reverse vaccinology, utilizing Machine Learning and Deep Learning at Professor Filippini's lab, where they pioneered the development of the world's first vaccinology software, NERVE. Presently pursuing a Master's in Industrial Biotechnology focusing on Immunomolecular studies, Francesco is deeply passionate about combining computational and wet lab methodologies to drive significant healthcare advancements.