What we do
Our lab acts as the bridge between big data analysis and biomedical research. We develop novel data mining algorithms to detect patterns and statistical dependencies in large datasets from the fields of biology and medicine. Our major goals are twofold:
- to enable the automatic generation of new knowledge from big data through machine learning, and
- to gain an understanding of the relationship between biological systems and their molecular properties. Such an understanding is of fundamental importance for personalized medicine, which tailors medical treatment to the molecular properties of a person.
Find out more about the Group’s activities
Main publications 2018
- Bock C et al.
Association mapping in biomedical time series via statistically significant shapelet mining.
Bioinformatics, doi: 10.1093/bioinformatics/bty246 - Togninalli M et al.
Accurate and adaptive imputation of summary statistics in mixed-ethnicity cohorts.
Bioinformatics, https://doi.org/10.1093/bioinformatics/bty596 - Llinares-Lopez F et al.
CASMAP: Detection of statistically significant combinations of SNPs in association mapping.
Bioinformatics, doi: 10.1093/bioinformatics/bty1020
