This article is part of the supplement: The BioCreative II - Critical Assessment for Information Extraction in Biology Challenge

Open Access Open Badges Research

Text mining for biology - the way forward: opinions from leading scientists

Russ B Altman1, Casey M Bergman2, Judith Blake3, Christian Blaschke4, Aaron Cohen5, Frank Gannon6, Les Grivell7, Udo Hahn8, William Hersh5, Lynette Hirschman9*, Lars Juhl Jensen1011, Martin Krallinger12, Barend Mons13, Seán I O'Donoghue10, Manuel C Peitsch14, Dietrich Rebholz-Schuhmann15, Hagit Shatkay16 and Alfonso Valencia12

Author affiliations

1 Stanford University, 318 Campus Drive, Stanford, California, 94305-5444, USA

2 University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT UK

3 Jackson Laboratory, 600 Main Street, Bar Harbor, Maine, 04609, USA

4 Bioalma, Ronda de Poniente 4, Bajo C, 28760 Tres Cantos, Madrid, Spain

5 Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, Oregon, 97239 USA

6 Science Foundation Ireland, Dublin, Ireland

7 EMBO, Postfach 1022.40, Heidelberg, D-69117 Germany

8 Jena University, Fuerstengraben 30, Jena, D-07743, Germany

9 MITRE, 202 Burlington Road, Bedford, Massachusetts, 01730 USA

10 European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg, D-69117 Germany

11 NNF Center for Protein Research, Panum Institute, Copenhagen, Denmark

12 CNIO, C/Melchor Fernandez Almagro, 3, Madrid, E-28029 Spain

13 Erasmus Medical Center and Leiden University Medical Center, Leiden, Bldg. 2, Einthovenweg 20, Leiden, 2300 RC, The Netherlands

14 Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Genopode,, Lausanne, 1015 Switzerland

15 EBI, 1, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK

16 School of Computing, Goodwin Hall, Queen's University, Kingston, Ontario, K7L 3N6, Canada

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Citation and License

Genome Biology 2008, 9(Suppl 2):S7  doi:10.1186/gb-2008-9-s2-s7

Published: 1 September 2008


This article collects opinions from leading scientists about how text mining can provide better access to the biological literature, how the scientific community can help with this process, what the next steps are, and what role future BioCreative evaluations can play. The responses identify several broad themes, including the possibility of fusing literature and biological databases through text mining; the need for user interfaces tailored to different classes of users and supporting community-based annotation; the importance of scaling text mining technology and inserting it into larger workflows; and suggestions for additional challenge evaluations, new applications, and additional resources needed to make progress.