Drug drug interaction extraction from the literature using a recursive neural network
- Authors
- Lim, Sangrak; Lee, Kyubum; Kang, Jaewoo
- Issue Date
- 26-1월-2018
- Publisher
- PUBLIC LIBRARY SCIENCE
- Citation
- PLOS ONE, v.13, no.1
- Indexed
- SCIE
SCOPUS
- Journal Title
- PLOS ONE
- Volume
- 13
- Number
- 1
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/77934
- DOI
- 10.1371/journal.pone.0190926
- ISSN
- 1932-6203
- Abstract
- Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI information from the literature is a laborious task. However, text mining can be used to find DDIs in the biomedical literature. Among the recently developed neural networks, we use a Recursive Neural Network to improve the performance of DDI extraction. Our recursive neural network model uses a position feature, a subtree containment feature, and an ensemble method to improve the performance of DDI extraction. Compared with the state-of-the-art models, the DDI detection and type classifiers of our model performed 4.4% and 2.8% better, respectively, on the DDIExtraction Challenge' 13 test data. We also validated our model on the PK DDI corpus that consists of two types of DDIs data: in vivo DDI and in vitro DDI. Compared with the existing model, our detection classifier performed 2.3% and 6.7% better on in vivo and in vitro data respectively. The results of our validation demonstrate that our model can automatically extract DDIs better than existing models.
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Collections - Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
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