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Drug drug interaction extraction from biomedical literature using syntax convolutional neural network
Motivation: Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is still at an early stage and its performance has much room to...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5181565/ https://www.ncbi.nlm.nih.gov/pubmed/27466626 http://dx.doi.org/10.1093/bioinformatics/btw486 |
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author | Zhao, Zhehuan Yang, Zhihao Luo, Ling Lin, Hongfei Wang, Jian |
author_facet | Zhao, Zhehuan Yang, Zhihao Luo, Ling Lin, Hongfei Wang, Jian |
author_sort | Zhao, Zhehuan |
collection | PubMed |
description | Motivation: Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is still at an early stage and its performance has much room to improve. Results: In this article, we present a syntax convolutional neural network (SCNN) based DDI extraction method. In this method, a novel word embedding, syntax word embedding, is proposed to employ the syntactic information of a sentence. Then the position and part of speech features are introduced to extend the embedding of each word. Later, auto-encoder is introduced to encode the traditional bag-of-words feature (sparse 0–1 vector) as the dense real value vector. Finally, a combination of embedding-based convolutional features and traditional features are fed to the softmax classifier to extract DDIs from biomedical literature. Experimental results on the DDIExtraction 2013 corpus show that SCNN obtains a better performance (an F-score of 0.686) than other state-of-the-art methods. Availability and Implementation: The source code is available for academic use at http://202.118.75.18:8080/DDI/SCNN-DDI.zip. Contact: yangzh@dlut.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-5181565 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-51815652016-12-27 Drug drug interaction extraction from biomedical literature using syntax convolutional neural network Zhao, Zhehuan Yang, Zhihao Luo, Ling Lin, Hongfei Wang, Jian Bioinformatics Original Papers Motivation: Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is still at an early stage and its performance has much room to improve. Results: In this article, we present a syntax convolutional neural network (SCNN) based DDI extraction method. In this method, a novel word embedding, syntax word embedding, is proposed to employ the syntactic information of a sentence. Then the position and part of speech features are introduced to extend the embedding of each word. Later, auto-encoder is introduced to encode the traditional bag-of-words feature (sparse 0–1 vector) as the dense real value vector. Finally, a combination of embedding-based convolutional features and traditional features are fed to the softmax classifier to extract DDIs from biomedical literature. Experimental results on the DDIExtraction 2013 corpus show that SCNN obtains a better performance (an F-score of 0.686) than other state-of-the-art methods. Availability and Implementation: The source code is available for academic use at http://202.118.75.18:8080/DDI/SCNN-DDI.zip. Contact: yangzh@dlut.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2016-11-15 2016-07-27 /pmc/articles/PMC5181565/ /pubmed/27466626 http://dx.doi.org/10.1093/bioinformatics/btw486 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Zhao, Zhehuan Yang, Zhihao Luo, Ling Lin, Hongfei Wang, Jian Drug drug interaction extraction from biomedical literature using syntax convolutional neural network |
title | Drug drug interaction extraction from biomedical literature using syntax convolutional neural network |
title_full | Drug drug interaction extraction from biomedical literature using syntax convolutional neural network |
title_fullStr | Drug drug interaction extraction from biomedical literature using syntax convolutional neural network |
title_full_unstemmed | Drug drug interaction extraction from biomedical literature using syntax convolutional neural network |
title_short | Drug drug interaction extraction from biomedical literature using syntax convolutional neural network |
title_sort | drug drug interaction extraction from biomedical literature using syntax convolutional neural network |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5181565/ https://www.ncbi.nlm.nih.gov/pubmed/27466626 http://dx.doi.org/10.1093/bioinformatics/btw486 |
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