ayout 1 - EMBL Grenoble

EMBL Research at a Glance 2009
Dietrich
Rebholz-
Schuhmann
Master in Medicine, 1988,
University of Düsseldorf.
PhD 1989, University of
Düsseldorf.
Master in Computer Science,
1993, Passau.
Senior scientist at gsf,
Munich, Germany and LION
bioscience AG, Heidelberg.
Facts from the literature and biomedical
semantics
Previous and current research
Text mining comprises the fast retrieval of relevant documents from the whole body of the literature
(e.g. Medline database) and the extraction of facts from the text thereafter. Text mining solutions
are now becoming mature enough to be automatically integrated into workflows for
research work.
Research in the Rebholz-Schuhmann group is focussed on fact extraction from the literature. It is
our goal to automatically connect literature content to other biomedical data resources (e.g. bioinformatics
databases) and to evaluate the results. Ongoing research targets the identification of relationships
between genes and diseases, molecular interactions and other types of information.
Over the past two years, the team has generated several public resources: a lexicon of biomedical
terms, an ontology for gene regulatory events and recently an authoring service (PaperMaker).
The work in the research group is split into different parts: 1) research work in named entity recognition
and its quality control (e.g. UKPMC project, CALBC); 2) knowledge discovery tasks, e.g.
for the identification of gene–disease associations; and 3) development of a modular IT infrastructure
for information extraction (Whatizit). All parts are tightly coupled.
Future projects and goals
Group leader at EMBL-EBI
since 2003.
The following goals are priorities for the future. Firstly we will continue our ongoing research in
term recognition and mapping to biomedical data resources to establish state-of-the-art text mining
applications. We will develop this by focussing on automatic means to measure and evaluate
existing options to identify the most promising solutions (UKPMC project, CALBC support action).
Secondly, we will invest further effort into the extraction of content from the scientific literature. Such solutions will be geared towards the
annotation of diseases and the generation of fact databases. As part of this research we will investigate workflow systems where text mining
supports bioinformatics information retrieval solutions. One solution is the integration of public biomedical data resources into the data
from the biomedical scientific literature.
Finally, we will increase the availability of information extraction solutions based on SOAP web services for the benefit of the bioinformatics
community. This requires standards in the annotation of scientific literature and will automatically lead to semantic enrichment of the scientific
literature.
Overview of the categorisation of information retrieval
tools on the basis of their input and output formats.
Selected references
Beisswanger, E. et al. (2008). Gene Regulation Ontology (Gro):
Design principles and use cases. Paper presented at Studies in
Health Technology and Informatics 2008, 136, 9-1
Jaeger, S. et al. (2008). Integrating protein-protein interactions and
text mining for protein function prediction. BMC Bioinformatics, 9,
Article S2
Kim, J.J. et al. (2008). MedEvi: Retrieving textual evidence of
relations between biomedical concepts from Medline. Bioinformatics,
2, 110-112
Kim, J.J. & Rebholz-Schuhmann, D. (2008). Categorization of
services for seeking information in biomedical literature: A typology
for improvement of practice. Brief. Bioinform., 9, 52-65
Rebholz-Schuhmann, D. et al. (2008). Text processing through web
services: Calling Whatizit. Bioinformatics, 2, 296-298
70