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BioDEAL: Biological data-evidence-annotation linkage system

The size of publication databases in biomedicine (e.g., PubMed, MEDLINE) are growing rapidly every year, as are public databases of experimental biological data and annotations derived from the data. Publications often contain evidence that confirms or disproves 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. 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. In this paper we present 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.