What we do

We develop and apply modern machine learning techniques and sequence analysis methods in biology and medicine. In particular, we develop new learning approaches capable of dealing with vast amounts of genomic and medical data.

We aim to provide accurate predictions on practically relevant phenomena and comprehensively explain each phenomenon’s prognoses, thereby gaining new biomedical insights.

Find out more about the Group’s activities

Main publications 2020

  • The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium
    Pan-cancer analysis of whole genomes
    Nature, 10.1038/s41586-020-1969-6
  • 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
  • Fortuin V et al.
    Gp-vae: Deep probabilistic time series imputation
    Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:1651-1661

Members

eth zurich

Gunnar Rätsch
Biomedical Informatics
ETH Zurich
Group Webpage

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Domain(s) of activity:

  • Text mining and machine learning
  • Deep sequencing data
  • Electronic health record
  • Infectious diseases
  • Machine learning
  • Metagenomics
  • Oncology

Domain(s) of application:

  • Medicine and health

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