What do we do?
Our research in the Computational Single Cell Biology Group aims to elucidate the composition of heterogeneous cell populations and how these implement function in the context of cancer and immune biology. To accomplish this task, we build on concepts from statistics, machine learning and mathematical optimization to develop probabilistic approaches to describe biological systems, learn these descriptions from data, and design experiments to validate hypotheses following computational analyses. Our research can be used to pinpoint therapeutic targets with a view to designing drugs.
Main publications 2017
- Arvaniti E, Claassen M. Sensitive detection of rare disease-associated cell subsets via representation learning. Nature Communications, 2017