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Ulf Leser ist Professor für Wissensmana- Ulf Leser is Professor of Knowledge Managegement
in der Bioinformatik. Er hat Informament in Bioinformatics. He studied computer
tik an der Technischen Universität München science at Technische Universität Munich and
studiert und arbeitete danach am Max- later worked at the Max Planck Institute for
Planck-Institut für Molekulare Genetik. Sei- Molecular Genetics. He was awarded his PhD
nen Doktortitel hat er an der Technischen at Technische Universität Berlin in the »Distri-
Universität Berlin im Graduiertenkolleg buted Information Systems« research trai-
»Verteilte Informationssysteme« erlangt. ning group.
Leser ist seit 2002 Professor an der Hum- Since 2002 Leser has been a professor at
boldt-Universität und Mitglied verschiedener Humboldt-Universität and a member of seve-
number of diff erent spellings and syn- HU-Einrichtungen, darunter sind das Zentrum ral HU institutions, including the Centre for
onyms in literature,« Leser explains. »This für Biophysik und Bioinformatik, das Zentrum Biophysics and Bioinformatics, the Centre for
can be used to rank publications by their
relevance or to fi nd connections with other
für sprachliche Bedeutung und das Zentrum
für Ubiquitäre Informationssysteme.
Linguistic Meaning and the Centre for Ubiquitous
Information Systems.
objects.«
leser@informatik.hu-berlin.de
Leser is involved in a number of biomedical
research projects, including Colonet,
a project funded by the Federal Minis-
Tel 030 · 2093-3902
try of Education and Research (BMBF) and hosted by »There are about 80 genes that play an important role in the
Charité-Universitätsmedizin Berlin. One of Colonet’s aims is to Colonet project. Our job is not only to fi nd them in publications;
fi nd biomarkers that could make it possible to individually cus- another challenge is to rank them in terms of their potential imtomize
a therapy for colorectal cancer. The choice of therapy in this portance as biomarkers, so that we can distinguish between rele-
context is o� en based on a knowledge of specifi c mutations in key vant and insignifi cant references,« Leser stresses. Another problem
genes, information that is »hidden« in a large number of publica- that the HU researcher and many other bioinformaticians are trytions.
One thing that makes the electronic search diffi cult is the ing to solve is how to generate connections with other »objects«
fact that genes are o� en made up of fi ve to ten individual words. such as proteins during a search. In the human body there are
Sometimes the same name applies to diff erent objects; sometimes about 2,000 genes that regulate other genes and are also relevant
the same protein has several diff erent names. It’s also diffi cult to in the search for a biomarker. In order to automatically recognize
search across species boundaries, because scientifi c knowledge which genes regulate other genes, it’s necessary to recognize not
about tumours is o� en based on experiments with mice. Even biologists
disagree about whether a distinction should be made between
mice and men in gene searches.
How does text mining work? Computer scientists work on systems
which, for example, make it possible to recognize words from
sentence structures and to »see through« grammatical structures
using methods taken from language processing. This is followed
up by machine learning processes derived from known examples of
general rules which can then be applied to new texts. In the meantime,
the HU researcher and his working group can now fi nd all the
human genes in PubMed’s 19 million abstracts in a single day.
only the genes themselves in sentences but also their interrelation-
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ships – a major issue in the global bio-text mining community.
And for Ulf Leser.
M
chrome hrom o e c
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