Drug drug interaction extraction from biomedical literature using syntax convolutional neural network.
Drug drug interaction extraction from biomedical literature using syntax convolutional neural network.
复制标题
使用语法卷积神经网络从生物医学文献中提取药物相互作用
DOI：
10.1093/bioinformatics/btw486
复制
发表时间：
2016-11-15
期刊：
影响因子：
--
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Zhao Z;Yang Z;Luo L;Lin H;Wang J
动机：检测药物相互作用（DDI）已成为公共卫生安全的重要组成部分。因此，利用文本挖掘技术从生物医学文献中提取DDI受到了广泛的关注。然而，这项研究仍处于早期阶段，其性能有很大的改进空间。结果：本文提出了一种基于语法卷积神经网络的DDI提取方法。在该方法中，一种新的词嵌入，语法词嵌入，提出了利用一个句子的句法信息。然后引入位置和词性特征对每个词的嵌入进行扩展。之后，引入自动编码器将传统的词袋特征（稀疏0-1向量）编码为密集的真实的值向量。最后，基于嵌入的卷积特征和传统特征的组合被馈送到softmax分类器，以从生物医学文献中提取DDI。在DDIExtraction 2013语料库上的实验结果表明，SCNN获得了更好的性能（F值为0.686）比其他国家的最先进的方法。可用性和实现：源代码可在http://202.118.75.18:8080/DDI/SCNN-DDI.zip上供学术使用。联系方式：yangzh@dlut.edu.cn补充信息：补充数据可从生物信息学在线网站获得。
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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DOI：
10.1007/s10791-007-9027-7
发表时间：
2007-10-01
期刊：
INFORMATION RETRIEVAL
影响因子：
--
作者：
Jiang, Jing;Zhai, ChengXiang
通讯作者：
Zhai, ChengXiang
影响因子：
4.5
作者：
Kim, Sun;Liu, Haibin;Yeganova, Lana;Wilbur, W. John
通讯作者：
Wilbur, W. John
影响因子：
5.8
作者：
Fundel, Katrin;Kueffner, Robert;Zimmer, Ralf
通讯作者：
Zimmer, Ralf
影响因子：
--
作者：
Hsu, CW;Lin, CJ
通讯作者：
Lin, CJ
影响因子：
14.9
作者：
Bodenreider, O
通讯作者：
Bodenreider, O