In recent years, the integration of Machine Learning (ML) and Deep Learning (DL) techniques has revolutionized the field of vaccinology, enabling the development of more effective and tailored vaccines. This tech talk aims to explore the dual approach of utilizing classic ML methods and cutting-edge AI explainability techniques in the context of vaccine development. We will delve into the pivotal role of protein language models, such as ESM ones, in deciphering the intricate language of proteins and predicting vaccine-likeness
properties. Furthermore, we will discuss the importance of AI explainability in interpreting complex ML/DL models, ensuring transparency, trust, and safety in vaccine design. Join us as we embark on a journey to comprehend the power of AI in deciphering the immunological code and shaping the future of vaccines.
Francesco Patanè is a passionate biotechnology researcher. He holds a Bachelor’s degree in Biotechnology (Pharmaceutical Curriculum) from the University of Padova, where he explored metabolic engineering in cannabis to optimize cannabinoid production. For the last 10 months, he researched reverse vaccinology, utilizing Machine Learning and Deep Learning at Professor Filippini’s lab, where the world’s first software for vaccinology (NERVE) was developed. Currently pursuing a Master’s in Industrial Biotechnology, Immunomolecular Curriculum, Francesco is passionate about merging computational and wet lab techniques for impactful healthcare advancements.