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Open Access Technical advance

Mining biomarker information in biomedical literature

Erfan Younesi12, Luca Toldo3, Bernd Müller1, Christoph M Friedrich15, Natalia Novac3, Alexander Scheer4, Martin Hofmann-Apitius12 and Juliane Fluck1*

Author Affiliations

1 Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53754, Germany

2 Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany

3 Knowledge Management, Operational Excellence & Site Coordination, Merck Serono, Merck KGaA, Darmstadt, Germany

4 Informatics & Knowledge Management, Merck Serono, Merck KGaA, Geneva, Switzerland

5 Department of Computer Science, University of Applied Science and Arts, Dortmund, Germany

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BMC Medical Informatics and Decision Making 2012, 12:148  doi:10.1186/1472-6947-12-148

Published: 18 December 2012

Abstract

Background

For selection and evaluation of potential biomarkers, inclusion of already published information is of utmost importance. In spite of significant advancements in text- and data-mining techniques, the vast knowledge space of biomarkers in biomedical text has remained unexplored. Existing named entity recognition approaches are not sufficiently selective for the retrieval of biomarker information from the literature. The purpose of this study was to identify textual features that enhance the effectiveness of biomarker information retrieval for different indication areas and diverse end user perspectives.

Methods

A biomarker terminology was created and further organized into six concept classes. Performance of this terminology was optimized towards balanced selectivity and specificity. The information retrieval performance using the biomarker terminology was evaluated based on various combinations of the terminology's six classes. Further validation of these results was performed on two independent corpora representing two different neurodegenerative diseases.

Results

The current state of the biomarker terminology contains 119 entity classes supported by 1890 different synonyms. The result of information retrieval shows improved retrieval rate of informative abstracts, which is achieved by including clinical management terms and evidence of gene/protein alterations (e.g. gene/protein expression status or certain polymorphisms) in combination with disease and gene name recognition. When additional filtering through other classes (e.g. diagnostic or prognostic methods) is applied, the typical high number of unspecific search results is significantly reduced. The evaluation results suggest that this approach enables the automated identification of biomarker information in the literature. A demo version of the search engine SCAIView, including the biomarker retrieval, is made available to the public through http://www.scaiview.com/scaiview-academia.html webcite.

Conclusions

The approach presented in this paper demonstrates that using a dedicated biomarker terminology for automated analysis of the scientific literature maybe helpful as an aid to finding biomarker information in text. Successful extraction of candidate biomarkers information from published resources can be considered as the first step towards developing novel hypotheses. These hypotheses will be valuable for the early decision-making in the drug discovery and development process.

Keywords:
Text-mining; Biomarker discovery; Information retrieval; Terminology