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Background: Biomarkers and target-specific phenotypes are important to targeted drug design and individualized
medicine. More and more, the discovery of relevant biomarkers is aided by in silico techniques based on applying
data mining and computational chemistry on large molecular databases. However, there is an even larger source
of valuable information available that can potentially be tapped for such discoveries: repositories constituted by
Results: This paper reports on a pilot experiment to discover potential novel biomarkers and phenotypes for
diabetes and obesity by self-organized text mining of about 120,000 PubMed abstracts, public clinical trial
summaries, and internal Merck research documents. These documents were directly analyzed by the InfoCodex
semantic engine, without prior human manipulations such as parsing. Retrieval of known entities missed by
other traditional approaches could be demonstrated. Moreover, the InfoCodex semantic engine was shown to
discover new diabetes and obesity biomarkers and phenotypes. Amongst these were many interesting candidates
with a high potential, although noticeable noise (uninteresting or obvious terms) was generated.
Conclusions: The reported approach of employing autonomous self-organising semantic engines to aid
biomarker discovery, supplemented by appropriate manual curation processes, shows promise and has potential
to impact pharmaceutical research, for example to shorten time-to-market of novel drugs, or speed up early
recognition of dead ends and adverse reactions.
Carlo A. Trugenberger earned his Ph.D. in Theoretical Physics in 1988 at the Swiss Federal Institute of Technology, Z?rich and
his Master in Economics in 1997 at Bocconi University, Milano. An international academic career in theoretical physics (MIT, Los
Alamos Nat. Lab., CERN Geneva, Max Planck Institut M?nich) lead him to the position of associate professor of theoretical physics
at Geneva University. In 2001 he decided to quit academia and to exploit his expertise in information theory, neural networks and
machine intelligence to design an innovative semantic technology and to co-found the company InfoCodex Semantic Technologies
AG. His scientific work has been recognized in the press and the semantic technology he co-designed has won International
benchmarks and awards.
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