Informed machine learning for biology and medicine: A graph representation perspective
Talk by Dr. Dorina Thanou
Date: 23.11.23 Time: 12.15 - 13.45 Room: Y27H12
Medicine and biology are key domains where AI encounters a unique opportunity to make a social impact. While living systems pose immense challenges, they provide a rich playground for developing the next generation of AI algorithms that will perform well in sparse and complex data regimes. In this talk, we will highlight some of the main challenges encountered in biomedical applications, and we will illustrate some showcases of how appropriately designed AI solutions can provide significant benefit. In particular, we will show how graphs and graph representation learning algorithms can be key in modelling complex structure in biological systems. Finally, we will conclude by identifying some key open directions and approaches towards fostering the adoption of AI in medicine.