Robin Liechti

The paper introduces FACTMx, a variational framework that jointly models structured and simple patient-level data to learn interpretable representations, showing improved performance in simulations and applications to cancer and cardiovascular cohorts.

Cellohood identifies cellular neighborhoods in spatial transcriptomics data, revealing clinically relevant patterns across multiple granularity levels.

The paper presents two workflows for analyzing multiplexed imaging data from cancer tissues, applied to over 10,000 samples from 2500 patients, enabling large-scale tumor microenvironment studies.