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.
Main publications 2017
- Grimm D et al. easyGWAS: A Cloud-based Platform for Comparing the Results of Genome-wide Association Studies. Plant Cell 2017; 29(1):5
- Llinares-Lopez F et al. Genome-wide genetic heterogeneity discovery with categorical covariates. Bioinformatics 2017; 33 (12):1820
- Sugiyama M et al. graphkernels: R and Python packages for graph comparison. Bioinformatics 2017.
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