Ageing is a complex process that has been observed in all biological systems at every level of organisation. Some anti-ageing interventions have demonstrated life-extending effects in model organisms. However, the translation of these interventions in human clinical practice remains limited due to the absence of comprehensive ageing biomarkers. Recent studies suggest that a set of biomarkers, rather than any individual biomarker, constitute the most effective means of assessing the health status. The presentation will cover the development of comprehensive and robust biomarkers of ageing using deep learning and blood biochemistry, transcriptomics, and even imaging data, to be able to track the effectiveness of the various interventions we are developing.

Learning Objectives: 

1. Different AI Approaches to aging biomarker development
2. How AI can revolutionise aging research and how aging research can accelerate AI research