The group implements computational methods for treatment response modeling and large-scale omics and clinical data integration and mining for diagnostic and prognostic prediction.

Computationally efficient and clinically applicable machine learning models are used for systematic mining of molecular features that are predictive of medical outcomes, including differences in disease risk or treatment responses, which may eventually provide predictive biomarkers for clinical translation. We also develop and apply advanced mathematical models to make the most of the high-throughput omics datasets, including transcriptomics, proteomics and metabolomics, combined with clinical information, to provide a systems view of the underlying disease mechanisms and mode-of-action of therapeutic interventions. The research group is led by Professor of Applied Mathematics and Statistics Tero Aittokallio.

Read more:

https://www.helsinki.fi/en/researchgroups/computational-systems-medicine

https://www.ous-research.no/aittokallio/