What we do

We specialize in Information Extraction from biomedically relevant textual sources, such as the scientific literature, clinical records, or social media. We focus in particular on the extraction of domain-specific entities (such as genes, drugs, diseases), and their semantic relationships (e.g. gene-disease associations). Our tools are often evaluated through participation in community-run evaluation challenges (e.g. BioCreAtIvE). We also provide an environment for Assisted Curation (ODIN), which is used in the curation pipeline of the RegulonDB database, in an NIH-funded project.

Highlights 2020

In 2020 we successfully completed the transfer of the group from the University of Zurich to the Swiss AI Lab IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale) in Lugano.

Another major highlight were our activities related to the COVID-19 pandemic, in particular:

- Monitoring Twitter conversations about COVID-19. More information

- Collaborating at a repository of COVID-19 literature with classification into clinically relevant-categories and translations in Spanish. More information

- Processing biomedical literature about COVID-19. Link More information

Find out more about the Group’s activities

Main publications 2020

  • Colic N et al.
    Annotating the Pandemic: Named Entity Recognition and Normalisation in COVID-19 Literature
    Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, 10.18653/v1/2020.nlpcovid19-2.27
  • Zühlke I et al.
    Factors associated with cattle necropsy submissions in Switzerland, and their importance for surveillance
    Preventive Veterinary Medicine, Volume 187, 2021, 105235, ISSN 0167-587, 10.1016/j.prevetmed.2020.105235
  • Colic N et al.
    Automated detection of adverse drug events from older patients’ electronic medical records using text mining
    The international Workshop on Artificial Intelligence for Healthcare applications, 10.1007/978-3-030-68763-2_15