Patrick Ruch
Text Mining
HES-SO - Geneva School of Business Administration (HEG)
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What do we do?

In the SIB Text Mining Group, we carry out activities in semantic and text analytics applied to the health and life sciences. Previously hosted by the Radiology and Medical Informatics Department of the University Hospitals of Geneva, our group moved to the University of Applied Sciences Geneva (HES-SO – HEG Geneva) in 2008. We develop text mining solutions to support both the annotation of SIB databases and the work of a wide range of biomedical professionals from drug designers to clinicians. We are thus designing, developing and maintaining data and web analytic instruments, such as custom search engines, automatic text classifiers and information extraction systems, to help domain experts “make sense” out of biomedical data.

Highlights 2016

Over the course of 2016, our team developed a new curation service, nextA5, which prioritizes the literature for specific curation requirements. This service has been shown to significantly improve the search effectiveness of curators along three important curation axes: diseases (+231%), molecular functions (+236%), and biological processes (+3,153%). In parallel, user-friendly interfaces powered with a set of JSON web services are currently being implemented into the neXtProt annotation pipeline.
Regarding scientific communication and publication, the group organized a workshop during the Biocuration 2016 conference in Geneva (10-14 April). The workshop gathered different presentations related to advances and challenges in the field of Text Mining applied to Biocuration.

Main publications 2016

  • Luc Mottin, Julien Gobeill, Emilie Pasche, Pierre-André Michel, Isabelle Cusin, Pascale Gaudet, Patrick Ruch: neXtA5: accelerating annotation of articles via automated approaches in neXtProt. Database 2016 (2016).
  • Luc Mottin, Julien Gobeill, Anaïs Mottaz, Emilie Pasche, Arnaud Gaudinat, Patrick Ruch: BiTeM at CLEF eHealth Evaluation Lab 2016 Task 2: Multilingual Information Extraction. CLEF (Working Notes) 2016: 94-102.

Our research topics: