The event is a workshop/seminar about using proteomic data and machine learning algorithms to predict risk of diseases. I will show that some of these models are already being used in clinical trials. One such example is the study with semaglutide which shows positive effect on cardiometabolic health on the proteome level. This can be translated into smarter and smaller trials as well as shortening the time to get drugs on the market. We will also show anyone that has an interest how they can get access to public data from different population studies around the world and can use that data to build their own predictive models or understand the underlying biology of a specific disease. The event is open to anyone who has an interest in drug development, machine learning in healthcare, epidemiology and computational biology.

Estimated participants: 15-25. Open to attend. In Cube.