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Drug Discov Today. 2014 Feb;19(2):140-4. doi: 10.1016/j.drudis.2013.09.012. Epub 2013 Sep 23.

Text mining for systems biology.

Author information

1
Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53754 Sankt Augustin, Germany.
2
Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53754 Sankt Augustin, Germany; Bonn-Aachen International Center for Information Technology (B-IT), Dahlmannstraβe 2, 53113 Bonn, Germany. Electronic address: martin.hofmann-apitius@scai.fraunhofer.de.

Abstract

Scientific communication in biomedicine is, by and large, still text based. Text mining technologies for the automated extraction of useful biomedical information from unstructured text that can be directly used for systems biology modelling have been substantially improved over the past few years. In this review, we underline the importance of named entity recognition and relationship extraction as fundamental approaches that are relevant to systems biology. Furthermore, we emphasize the role of publicly organized scientific benchmarking challenges that reflect the current status of text-mining technology and are important in moving the entire field forward. Given further interdisciplinary development of systems biology-orientated ontologies and training corpora, we expect a steadily increasing impact of text-mining technology on systems biology in the future.

PMID:
24070668
DOI:
10.1016/j.drudis.2013.09.012
[Indexed for MEDLINE]

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