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Picture of Prof. Dr. Karsten Borgwardt
Prof. Dr. Karsten Borgwardt

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 try to reach two grand 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.

Our lab receives significant external funding from the European Union through a Marie Curie Initial Training Network (2013-2016), from the Krupp Foundation through the Alfried-Krupp Award (2013-2018), and from 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 GitHub repository. More information on the individual projects can be found here.


easyGWAS Our online platform for computing, storing, sharing, analyzing and comparing the results of genome-wide association studies.

MLCB news


Karsten at Fraunhofer Institute for Industrial Mathematics ITWM

Karsten visited the Fraunhofer Institute for Industrial Mathematics ITWM in Kaiserslautern last week, and talked about "Machine Learning for Personalized Medicine" and "Significant Pattern Mining". Read more 


Karsten keynote speaker at the ECCB workshop in The Hague

Karsten was keynote speaker at the ECCB workshop on "Complex Network Analysis for Precision Medicine" in The Hague on September 3. Read more 


Paper accepted for NIPS 2016

Our most recent work in Significant Pattern Mining by Laetitia, Felipe, Dean and Karsten was accepted at NIPS 2016! Read more 


"Halting in Random Walk Kernels" by M. Sugiyama and K. Borgwardt ranked as one of the top 5 papers in 2015

The premier machine learning meeting in Japan ranked the NIPS paper "Halting in Random Walk Kernels" by Mahito Sugiyama and Karsten Borgwardt as one of the top 5 papers in 2015 (IEICE TC-IBISML Research Award Finalist)! Read more 


Dominik and Karsten contributed to the 1001 Genomes Project Flagship Paper

How do plants that, unlike humans and animals, cannot simply relocate adapt to environmental changes? How did plants spread over the continent and develop variations?  Read more 

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