Köhler Lab - Methods for Digital Phenotyping

We focus on ontology-driven algorithm for precision medicine. Digital phenotype data is invaluable in this effort. Our group develops the Human Phenotype Ontology (HPO) and we work on digitial representation of phenotypes and diseases, as well as extraction of such information from texts. We develop several algorithms for similarity calcualtions over such data. We  also use machine learning algorithms to reliably connect genomics data to phenotype data.

Our group is supported by three EU grants: HIPBI-RD, Solve-RD, and EJP-RD.

Research focus

  • Digitial phenotyping
  • Phenotype ontologies
  • Rare diseases
  • Semantic web
  • Machine learning
  • Interoperability


  • Julia Peker-Vogelsang (HPO curation)
  • Jana Marie Schwarz (HPO curation)

Selected publications

Köhler, Sebastian et al. Clinical diagnostics in human genetics with semantic similarity searches in ontologies, The American Journal of Human Genetics,85,4,457-464,2009, Elsevier

Köhler, Sebastian et al. Walking the interactome for prioritization of candidate disease genes, The American Journal of Human Genetics,82,4,949-958,2008, Cell Press

Köhler, Sebastian et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data, Nucleic acids research,42,D1,D966-D974,2014,Oxford University Press

Vasilevsky, Nicole A; et al. Plain-language medical vocabulary for precision diagnosis, Nature Genetics,50,4,474,2018, Nature Publishing Group