What we do
We are interested in modern machine-learning techniques suitable for the analysis of problems that arise in medicine and biology.
In particular, we develop new learning techniques that are capable of dealing with large amounts of genomic data and medical data.
These techniques aim to provide accurate predictions on the phenomenon at hand and to comprehensively provide reasons for their prognoses, and thereby assist in gaining new biomedical insights.
Highlights 2018
Publication of a large collaborative study about Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients where our group was involved in the analysis of the The Cancer Genome Atlas (TCGA) data, was a highlight in 2018. We have discovered new cancer-specific molecular changes that could potentially inform the development of cancer treatments. Another highlight is the growth of our group to now 25 members and counting!
Find out more about the Group’s activities
Main publications 2018
- Kahles A et al.
Comprehensive analysis of alternative splicing across tumors from 8,705 patients.
Cancer Cell, doi: 10.1016/j.ccell.2018.07.001 - Mustafa H et al.
Dynamic compression schemes for graph coloring.
Bioinformatics, doi: 10.1093/bioinformatics/bty632 - Locatello F et al.
Boosting black box variational inference.
NIPS spotlight, arXiv:1806.02185
