Big Data Analysis and Biomedical Research meet in our lab: We develop novel Data Mining Algorithms to detect patterns and statistical dependencies in large datasets from Biology and Medicine.
We are working towards two central goals: To enable the automatic generation of new knowledge from Big Data through Machine Learning, and to gain an understanding of the relationship between the function of Biological Systems and their molecular properties. This understanding is of fundamental importance for Personalized Medicine, which tailors medical treatment to the molecular properties of each patient.
External supporters of our work include the Krupp Foundation through the Alfried-Krupp Award for Young Professors (2013-2018), and the Swiss National Science Foundation through a Starting Grant from the ERC backup scheme (2015-2020).
The source code and data sets of our research projects can be downloaded from our GitHubcall_made repository. More information on the individual projects can be found here.
Significant Pattern Mining
We develop methods that discover significant patterns in high dimensional datasets while being runtime efficient and statistically sound. Our algorithms can be applied to graphs or collections of sequences and allow to account for dependencies between objects, to control the Family-Wise Error Rate and to correct for categorical covariates. More about our work on Significant Pattern Mining can be found here.
easyGWAS
easyGWAS Our online platform for computing, storing, sharing, analyzing and comparing the results of genome-wide association studies.
MLFPM
We are coordinating the Marie Curie Initial Training Network "Machine Learning Frontiers in Precision Medicine" (MLFPMcall_made).
