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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...

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Detalles Bibliográficos
Autores principales: Zhao, Zhehuan, Yang, Zhihao, Luo, Ling, Lin, Hongfei, Wang, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
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.
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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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