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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is Professor at Weihenstephan-Triesdorf University of Applied-Sciences and is heading the Bioinformatics Group at the Technical University of Munich, TUM Campus Straubing for Biotechnology and Sustainability. Dominik did his postdoc at the Machine Learning and Computational Biology Lab at the ETH Zürich and completed his Ph.D. at the Max Planck Institutes in Tübingen.
More details can be found here: https://bit.cs.tum.de.
Karsten Borgwardt is Full Professor of Data Mining in the Department of Biosystems at ETH Zurich. He received the Krupp Award for Young Professors 2013 and a Starting Grant in 2014. From 2013 to 2016, he headed a Marie Curie Initial Training Network on “Machine Learning for Personalized Medicine”, and will coordinate a new Marie Curie Initial Training Network for Machine Learning in Medicine from 2019-2022. He is currently coordinating the “Personalized Swiss Sepsis Study”, a project for biomarker discovery in Sepsis, including all Swiss university hospitals and ETH Zürich (2018-2021).
For his full CV and publication record, see his homepage at https://www.bsse.ethz.ch/mlcb
Matteo Togninalli is a PhD student in the Machine Learning and Computational Biology Lab at ETH Zurich since 2017. Prior to that, he obtained a MSc in Bioengineering from EPFL. His research focuses on machine learning methods that successfully combine genome-wide association results with other data sources for downstream tasks such as phenotype prediction and disease gene discovery.