What we do
We develop and apply modern machine learning techniques and sequence analysis methods in biology and medicine. In particular, we develop new learning approaches capable of dealing with vast amounts of genomic and medical data.
We aim to provide accurate predictions on practically relevant phenomena and comprehensively explain each phenomenon’s prognoses, thereby gaining new biomedical insights.
Find out more about the Group’s activities
Main publications 2021
- Irmisch A et al.
The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support
Cancer Cell, 10.1016/j.ccell.2021.01.004 - Karasikov M et al.
Lossless Indexing with Counting de Bruijn Graphs
bioRxiv, 10.1101/2021.11.09.467907 - Immer A et al.
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
PMLR
