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 2020
- The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium
Pan-cancer analysis of whole genomes
Nature, 10.1038/s41586-020-1969-6 - Hyland S L et al.
Early prediction of circulatory failure in the intensive care unit using machine learning
Nature Medicine, 10.1038/s41591-020-0789-4 - Fortuin V et al.
Gp-vae: Deep probabilistic time series imputation
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:1651-1661
