What we do

Our lab acts as the bridge between big data analysis and biomedical research. We develop novel data mining algorithms to detect patterns and statistical dependencies in large datasets from the fields of biology and medicine. Our major goals are twofold:

  1. to enable the automatic generation of new knowledge from big data through machine learning, and
  2. to gain an understanding of the relationship between biological systems and their molecular properties. Such an understanding is of fundamental importance for personalized medicine, which tailors medical treatment to the molecular properties of a person.

Find out more about the Group’s activities

Main publications 2020

  • Hyland S L et al.
    Early prediction of circulatory failure in the intensive care unit using machine learning
    Nature Medicine, 10.1038/s41591-020-0789-4
  • Höllerer S et al.
    Large-​scale DNA-​based phenotypic recording and deep learning enable highly accurate sequence-​function mapping
    Nat Commun, 10.1038/s41467-020-17222-4
  • Moor M et al.
    Topological Autoencoders
    PMLR, 119:7045-7054


eth zurich

Karsten Borgwardt
Machine Learning and Computational Biology Lab
Group Webpage

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Domain(s) of activity:

  • Text mining and machine learning
  • Systems biology
  • Biomarkers
  • Biostatistics
  • Data mining
  • GWAS
  • Machine learning
  • Personalised medicine
  • Software engineering

Domain(s) of application:

  • Medicine and health