is a web-application for performing, analysing and visualising genome-wide association studies, especially in model organisms. The web-application was originally developed at the Machine Learning and Computational Biology Research Group at the Max Planck Institute for Developmental Biology and Max Planck Institute for Intelligent Systems in Tübingen. In 2015 we moved to the ETH Zürich and continued the development of easyGWAS.
easyGWAS is designed to be a central resource for the genetics community that frees the user from the tedious tasks of implementing statistical software and managing data and computing resources.
It serves the community at large through easy data access, validation and reproduction of GWAS findings.
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I am a postdoctoral researcher at the Machine Learning and Computational Biology Lab at the ETH Zürich. I completed my Ph.D. at the Max Planck Institutes in Tübingen. My background is in bioinformatics and machine learning. I am interested in genome-wide association studies and how to make them easily accessible to a broad audience. Furthermore, I am interested in developing new machine learning methods for genome-wide association studies, pathogenicity prediction and structural variant discovery.
For my full CV, see my homepage.
is Associate Professor of Data Mining in the Department of Biosystems at ETH Zurich since 2014. He is the recipient of the Krupp Award for Young Professors 2013. Since 2013, he is heading a Marie Curie Initial Training Network on "Machine Learning for Personalized Medicine" as Scientific Coordinator with 12 partner nodes across 8 countries.
For his full CV and publication record, see his homepage at https://www.bsse.ethz.ch/mlcb