This article is part of the supplement: 2008 International Workshop on Biomedical and Health Informatics

Research

BioDEAL: community generation of biological annotations

Paul Breimyer^1,2†, Nathan Green^1,2†, Vinay Kumar^1,2 and Nagiza F Samatova^1,2*

• * Corresponding author: Nagiza F Samatova samatovan@ornl.gov

• † Equal contributors

Author Affiliations

¹ North Carolina State University, Raleigh, C 27695, USA

² Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA

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BMC Medical Informatics and Decision Making 2009, 9(Suppl 1):S5 doi:10.1186/1472-6947-9-S1-S5

Published: 3 November 2009

Abstract

Background

Publication databases in biomedicine (e.g., PubMed, MEDLINE) are growing rapidly in size every year, as are public databases of experimental biological data and annotations derived from the data. Publications often contain evidence that confirm or disprove annotations, such as putative protein functions, however, it is increasingly difficult for biologists to identify and process published evidence due to the volume of papers and the lack of a systematic approach to associate published evidence with experimental data and annotations. Natural Language Processing (NLP) tools can help address the growing divide by providing automatic high-throughput detection of simple terms in publication text. However, NLP tools are not mature enough to identify complex terms, relationships, or events.

Results

In this paper we present and extend BioDEAL, a community evidence annotation system that introduces a feedback loop into the database-publication cycle to allow scientists to connect data-driven biological concepts to publications.

Conclusion

BioDEAL may change the way biologists relate published evidence with experimental data. Instead of biologists or research groups searching and managing evidence independently, the community can collectively build and share this knowledge.