Drug drug interaction extraction from biomedical literature using syntax convolutional neural network

Abstract

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.cnSupplementary information: Supplementary data are available at Bioinformatics online.

Multichannel Convolutional Neural Network for Biological Relation Extraction

“…It is notable that CNN was also utilized by Zhao et al [32] recently; they combined traditional CNN and external features such as contexts, shortest path, and part-of-speech to classify the interaction type and achieved an overall f -scores 68.6 which was similar to our results. The differences between [32] and our model lie on two aspects: (1) feature engineering still plays an important part in [32] model, whereas our model demands no manually feature sets; (2) multichannel word embeddings in our model contain richer semantic information which has been proved to be much useful in fine-grained interaction classification task.…”

Extracting Drug-Drug Interactions with Attention CNNs

“…We mainly compare Methods P (%) R (%) F (%) No negative instance filtering CNN 75.29 60.37 67.01 MCCNN (Quan et al, 2016) --67.80 SCNN (Zhao et al, 2016) 68 (2015) --67.0 CNN 75.72 64.66 69.75 MCCNN (Quan et al, 2016) 75.99 65.25 70.21 SCNN (Zhao et al, 2016) 72.5 65.1 68.6 Joint AB-LSTM (Sahu and Anand, 2017) 73.41 69.66 71.48 Table 6: Comparison with existing models Comparison of attention mechanisms on CNN models with ranking objective function the performance without negative instance filtering, which omits some apparent negative instance pairs with rules (Chowdhury and Lavelli, 2013), since we did not incorporate it. We also show the performance of the existing models with negative instance filtering for reference.…”

“…built a CNN-based model on word embedding and word position embeddings. Zhao et al (2016) proposed Syntax CNN (SCNN) that employs syntax word embeddings with the syntactic information of a sentence as well as features of POS tags and dependency trees. tackled DDI extraction using Multi-Channel CNN (MCCNN) that enables the fusion of multiple word embeddings.…”

“…For the DDI extraction, deep neural networkbased methods have recently drawn a considerable attention Zhao et al, 2016;Sahu and Anand, 2017). Deep neural networks have been widely used in the NLP field.…”

LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network

“…Finally, this vector is used in a classification layer to assign a class label. Recently, CNN has succeeded providing the state-of-art results in some tasks of relation extraction such as the relationships between nominals (Zeng et al, 2014) or the extraction of drugdrug interactions (Zhao et al, 2016). Our aim is to explore if CNN is also a suitable method for extracting relationships between keyphrases.…”