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. 2018 Mar 1;34(5):828-835.
doi: 10.1093/bioinformatics/btx659.

Drug-drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths

Yijia Zhang  1   2 , Wei Zheng  1   3 , Hongfei Lin  1 , Jian Wang  1 , Zhihao Yang  1 , Michel Dumontier  4
Affiliations

Drug-drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths

Yijia Zhang et al. Bioinformatics. .

Abstract

Motivation: Adverse events resulting from drug-drug interactions (DDI) pose a serious health issue. The ability to automatically extract DDIs described in the biomedical literature could further efforts for ongoing pharmacovigilance. Most of neural networks-based methods typically focus on sentence sequence to identify these DDIs, however the shortest dependency path (SDP) between the two entities contains valuable syntactic and semantic information. Effectively exploiting such information may improve DDI extraction.

Results: In this article, we present a hierarchical recurrent neural networks (RNNs)-based method to integrate the SDP and sentence sequence for DDI extraction task. Firstly, the sentence sequence is divided into three subsequences. Then, the bottom RNNs model is employed to learn the feature representation of the subsequences and SDP, and the top RNNs model is employed to learn the feature representation of both sentence sequence and SDP. Furthermore, we introduce the embedding attention mechanism to identify and enhance keywords for the DDI extraction task. We evaluate our approach using the DDI extraction 2013 corpus. Our method is competitive or superior in performance as compared with other state-of-the-art methods. Experimental results show that the sentence sequence and SDP are complementary to each other. Integrating the sentence sequence with SDP can effectively improve the DDI extraction performance.

Availability and implementation: The experimental data is available at https://github.com/zhangyijia1979/hierarchical-RNNs-model-for-DDI-extraction.

Contact: zhyj@dlut.edu.cn or michel.dumontier@maastrichtuniversity.nl.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
An illustration of SDP. The sentence example is from DDI extraction 2013 corpus. ‘Drug0’ and ‘Drug1’ denote two targeted drug entities, respectively. The Stanford parser is used to syntactic parse the sentence and generate the dependency syntactic graph. The nodes and edges on the shortest path between ‘Drug0’ and ‘Drug1’ are shown in bold. SDP between ‘Drug0’ and ‘Drug1’ can be extracted from the dependency syntactic graph. The nodes and edges on the SDP denote the tokens and dependency relations on the SDP between ‘Drug0’ and ‘Drug1’, respectively
Fig. 2.
Fig. 2.
The overview of our hierarchical RNNs model on sequence and SDP
See this image and copyright information in PMC

References

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