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AI methods for precision therapies

Developing AI methods for heterogeneity-aware precision therapies.


Our hope is that building tumor ecosystem representations and understanding the associated heterogeneity will allow us to predict disease progression and treatment response.


PROMETEX: metabolically-instructed therapy selection for prostate cancer

Together with Prof. Marianna Kruithof-de Julio from the Urogenus lab of the University of Bern and Prof. Theodore Alexandrov, Team Leader at EMBL Heidelberg, we are working on combining patient-derived organoids with single-cell metabolomics towards developing metabolically-instructed personalized prostate cancer therapies. Our project PROMETEX was recently awarded a Sinergia grant from the Swiss National Science Foundation.


Related projects

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Quantifying biological heterogeneity from single-cell data

Understanding, modeling and quantifying different sources of heterogeneity from single-cell measurements.
AI methods for precision therapies

AI for single-cell research

Understanding spatiotemporal heterogeneity across different scales of biological organization.
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Modeling the 4D genome

Modeling the 4D genome with deep learning and stochastic simulations.
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Modeling spatial heterogeneity of the tumor microenvironment

Combining spatial single-cell omics with AI to model the complexity of the tumor micorenvironment and enable novel spatial biomarker discovery.