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


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.

Modeling the 4D Genome

Modeling the 4D genome with deep learning and stochastic simulations.

Quantifying Biological Heterogeneity from Single-cell Data

Understanding, modeling and quantifying different sources of heterogeneity from single-cell measurements.

AI for Single-cell Research

Understanding spatiotemporal heterogeneity across different scales of biological organization.