Actionable and responsible AI in Medicine: a geometric deep learning approach
Tuesday, January 16, 2024
In this talk I will introduce geometric deep learning techniques.
I will focus on how to integrate Computational Biology and Deep Learning to build a digital patient twin using graph and hypergraph representation learning and considering physiological, clinical and molecular variables (multi omics and genetics).
I will discuss explainable and interpretable methodologies that will help the clinicians to master the diagnostic procedure.
Finally, I will show some results from applying diffusion models on protein design which could have impact on future biomarkers.
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Speaker/s
Pietro Liò is Full Professor at the department of Computer Science and Technology of the University of Cambridge and he is a member of the Artificial Intelligence group. His research interest focuses on developing Artificial Intelligence and Computational Biology models to understand diseases complexity and address personalised and precision medicine. Current focus is on Graph Neural Network modeling. He has a MA from Cambridge, a PhD in Complex Systems and Non Linear Dynamics (School of Informatics, dept of Engineering of the University of Firenze, Italy) and a PhD in (Theoretical) Genetics (University of Pavia, Italy). He is member of CAMBRIDGE CENTRE FOR AI IN MEDICINE and of Ellis, the European Lab for Learning & Intelligent Systems and the Academia Europaea.
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