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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).   

 

GitHub

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

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

MLCB news

25.11.2016

Karsten chosen as one of the "Top 40 under 40" in 2016

Karsten was again included in the ranking "Top 40 under 40" in State & Society in Germany. Read more 

21.11.2016

Krupp Symposium

On October 21, Karsten hosted the 2016 Krupp Symposium on "From Machine Learning for Personalized Medicine" at the Max Planck Institute of Psychiatry in Munich. Read more 

01.11.2016

New group member

Thomas Gumbsch joins the group. Read more 

25.10.2016

MLCB at GitHub

We have consolidated the software and data from more than 20 research projects in one GitHub site. Read more 

04.10.2016

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 

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04.12.2016
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