Digital frukost: Intelligent systems for disease risk prediction

Lifestyle diseases such as cardiovascular disease or diabetes result from malfunctioning processes in multiple organs developing over a long period of time. How can we learn to diagnose these processes at an early stage from indirect measurements of proteins in the bloodstream?

Register for the meeting

Deadline for registration: 25 March

Organs across the human body communicate by secreting proteins in the bloodstream. Using omics technologies, the concentrations of thousands of blood proteins can be measured simultaneously, vastly expanding medical diagnostic capabilities. However, for most of these proteins we do not know the processes that regulate them, nor their effects on disease. In other words, we can intercept the communication signals between organs, but we do not know the language they are written in. Machine learning is a branch of artificial intelligence that can learn to decode complex signals, provided it is given a large amount of training data. Can we use machine learning to distinguish causation from correlation in omics data and reverse-engineer the status of organ-specific disease processes from their effects on blood protein concentrations?

Message from the speaker Tom Michoel, UiB: In this talk I will present algorithmic developments in my group as well as results from analysing a unique resource of multi-omics data from cardiovascular disease patients undergoing surgical intervention in the STARNET study.

About the seminar series

"Digital Frukost" is an open breakfast seminar series focusing on research activities at the interface between the biological sciences and that of mathematics, computer science, physics, engineering or social sciences. Examples of such research activities could be mathematical or computational modeling of biological systems, application of engineering/control systems theory on biological systems or inspired by biological systems, application of mathematics/statistics/machine learning to analyze big data in health or marine sector; from sensor systems, imaging, omics technologies, policy making based on scientific models etc.

We look forward to your participation!


Ragnhild Inderberg Vestrum,

Published Feb. 18, 2021 9:16 AM - Last modified Feb. 18, 2021 9:16 AM