Abstract

For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets. Owing to the increasing body of text and the open-access policies of many journals, literature mining is also becoming useful for both hypothesis generation and biological discovery. However, the latter will require the integration of literature and high-throughput data, which should encourage close collaborations between biologists and computational linguists.

Author affiliations

1. European Molecular Biology Laboratory, D-69117 Heidelberg, Germany.
2. EML Research gGmbH, D-69118 Heidelberg, Germany.
3. Max Delbrück Centre for Molecular Medicine, D-13092 Berlin, Germany.

Correspondence to: Lars Juhl Jensen¹ Email: jensen@embl.de

Correspondence to: Peer Bork^1,3 Email: bork@embl.de

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