ArticleDatabase : the journal of biological databases and curation2016
HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V.
Article in Database : the journal of biological databases and curation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- A review on machine learning approaches and trends in drug discovery.Computational and structural biotechnology journal · 2021Review
- Disease named entity recognition from biomedical literature using a novel convolutional neural network.BMC medical genomics · 2017Article
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7 authors.
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Abstract
In this article, an end-to-end system was proposed for the challenge task of disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction in BioCreative V, where DNER includes disease mention recognition (DMR) and normalization (DN). Evaluation on the challenge corpus showed that our system achieved the highest F1-scores 86.93% on DMR, 84.11% on DN, 43.04% on CID relation extraction, respectively. The F1-score on DMR is higher than our previous one reported by the challenge organizers (86.76%), the highest F1-score of the challenge.Database URL: http://database.oxfordjournals.org/content/2016/baw077.
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