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Database (Oxford). 2016 Mar 19;2016. pii: baw032. doi: 10.1093/database/baw032. Print 2016.

Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task.

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National Center for Biotechnology Information, Bethesda, MD 20894, USA.
National Center for Biotechnology Information, Bethesda, MD 20894, USA Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.
Department of Biological Sciences and the Center for Human Health and the Environment, North Carolina State University, Raleigh, NC 27695, USA and.
Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100700, China.
National Center for Biotechnology Information, Bethesda, MD 20894, USA


Manually curating chemicals, diseases and their relationships is significantly important to biomedical research, but it is plagued by its high cost and the rapid growth of the biomedical literature. In recent years, there has been a growing interest in developing computational approaches for automatic chemical-disease relation (CDR) extraction. Despite these attempts, the lack of a comprehensive benchmarking dataset has limited the comparison of different techniques in order to assess and advance the current state-of-the-art. To this end, we organized a challenge task through BioCreative V to automatically extract CDRs from the literature. We designed two challenge tasks: disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. To assist system development and assessment, we created a large annotated text corpus that consisted of human annotations of chemicals, diseases and their interactions from 1500 PubMed articles. 34 teams worldwide participated in the CDR task: 16 (DNER) and 18 (CID). The best systems achieved an F-score of 86.46% for the DNER task--a result that approaches the human inter-annotator agreement (0.8875)--and an F-score of 57.03% for the CID task, the highest results ever reported for such tasks. When combining team results via machine learning, the ensemble system was able to further improve over the best team results by achieving 88.89% and 62.80% in F-score for the DNER and CID task, respectively. Additionally, another novel aspect of our evaluation is to test each participating system's ability to return real-time results: the average response time for each team's DNER and CID web service systems were 5.6 and 9.3 s, respectively. Most teams used hybrid systems for their submissions based on machining learning. Given the level of participation and results, we found our task to be successful in engaging the text-mining research community, producing a large annotated corpus and improving the results of automatic disease recognition and CDR extraction. Database URL:

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