Extracting drug–drug interactions with hybrid bidirectional gated recurrent unit and graph convolutional network

Zhao, Di; Wang, Jian; Lin, Hongfei; Yang, Zhihao; Zhang, Yijia

doi:10.1016/j.jbi.2019.103295

Cited by 27 publications

(8 citation statements)

References 25 publications

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“…We extend the multihead attention method with a translation model to capture the relation representations, which are subsequently fed into softmax layers. Using all instances (the cross column in Table 3), our method shows the highest test accuracy among all methods, which is 4.8% higher than our baseline 3 . Through experimental analysis, we observe that the multihead attention mechanism concatenate knowledge graph can detect more positive examples.…”

Section: Compare With Baseline Methodsmentioning

confidence: 88%

“…The current tasks of biomedical relation extraction mainly focus on the extraction of binary relations in single sentences, such as protein-protein interaction (PPI), chemicalprotein interaction (CPI) and drug-drug interaction (DDI) [1][2][3]. It is crucial for biomedical relation extraction to automatically construct a knowledge graph, which supports a variety of downstream natural language processing (NLP) tasks such as drug discovery [4].…”

Section: Introductionmentioning

confidence: 99%

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Incorporating representation learning and multihead attention to improve biomedical cross-sentence n-ary relation extraction

et al. 2020

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Background: Most biomedical information extraction focuses on binary relations within single sentences. However, extracting n-ary relations that span multiple sentences is in huge demand. At present, in the cross-sentence n-ary relation extraction task, the mainstream method not only relies heavily on syntactic parsing but also ignores prior knowledge. Results: In this paper, we propose a novel cross-sentence n-ary relation extraction method that utilizes the multihead attention and knowledge representation that is learned from the knowledge graph. Our model is built on self-attention, which can directly capture the relations between two words regardless of their syntactic relation. In addition, our method makes use of entity and relation information from the knowledge base to impose assistance while predicting the relation. Experiments on n-ary relation extraction show that combining context and knowledge representations can significantly improve the n-ary relation extraction performance. Meanwhile, we achieve comparable results with state-of-the-art methods. Conclusions: We explored a novel method for cross-sentence n-ary relation extraction. Unlike previous approaches, our methods operate directly on the sequence and learn how to model the internal structures of sentences. In addition, we introduce the knowledge representations learned from the knowledge graph into the cross-sentence n-ary relation extraction. Experiments based on knowledge representation learning show that entities and relations can be extracted in the knowledge graph, and coding this knowledge can provide consistent benefits.

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Section: Compare With Baseline Methodsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Incorporating representation learning and multihead attention to improve biomedical cross-sentence n-ary relation extraction

et al. 2020

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“…BGRU training time is shorter than BLSTM. The F 1 value of BGRU 51 is 87.09% and 75.33% for IMDB and Polarity dataset. This has an increase in the percentage of 0.57% and 0.28% for both the dataset than BLSTM.…”

Section: Experiment-2: Bidirectional Model-blstm and Bgrumentioning

confidence: 96%

Hybrid convolutional bidirectional recurrent neural network based sentiment analysis on movie reviews

Sivakumar

Rajalakshmi

2020

Computational Intelligence

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Sentiment analysis is the process of extracting the opinions of customers from online reviews. In general, customers express their reviews in natural language. It becomes a complex task when applying sentiment analysis on those reviews. In earlier stages, word-level features with various feature weighting methods such as Bag of Words, TF-IDF, and Word2Vec were applied for sentiment analysis and deep learning networks are not explored much. We considered phrase level and sentence level features instead of applying word-level features for sentiment analysis and also enhanced by applying various deep learning techniques. In this article, we have proposed a hybrid convolutional bidirectional recurrent neural network model (CBRNN) by combining two-layer convolutional neural network (CNN) with a bidirectional gated recurrent unit (BGRU). In the proposed CBRNN model, the CNN layer extracts the rich set of phrase-level features and BGRU captures the chronological features through long term dependency in a multi-layered sentence. The proposed approach was evaluated on two benchmark datasets and compared with various baselines. The experimental results show that the proposed hybrid model provides better results than any other models with an F 1 score of 87.62% and

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“…In recent years, there have also been studies that use a novel approach, i.e., graph convolutional networks (GCN) (Kipf and Welling, 2016 ) for relation extraction using dependency graphs (Zhang et al, 2018b ; Zhao et al, 2019 ). Graph convolutional networks use the same concept of CNN, but with the advantage of using graphs as inputs and outputs.…”

Section: Inferring Relationsmentioning

confidence: 99%

“…By using dependency paths to represent text as graphs, GCNs can be applied to relation extraction tasks. In Zhao et al ( 2019 ), the authors use a hybrid model that combines GCNs preceded by bidirectional gated recurrent units (bi-GRU) layer to achieve significant F-measures. Furthermore, for identifying drug-drug interactions, a syntax convolutional neural network has been evaluated for the DDIExtraction 2013 corpus (Herrero-Zazo et al, 2013 ) and found to outperform other methods (Zhao et al, 2016 ).…”

Section: Inferring Relationsmentioning

confidence: 99%

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

Perera

Dehmer

Emmert‐Streib

2020

Front. Cell Dev. Biol.

102

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The number of scientific publications in the literature is steadily growing, containing our knowledge in the biomedical, health, and clinical sciences. Since there is currently no automatic archiving of the obtained results, much of this information remains buried in textual details not readily available for further usage or analysis. For this reason, natural language processing (NLP) and text mining methods are used for information extraction from such publications. In this paper, we review practices for Named Entity Recognition (NER) and Relation Detection (RD), allowing, e.g., to identify interactions between proteins and drugs or genes and diseases. This information can be integrated into networks to summarize large-scale details on a particular biomedical or clinical problem, which is then amenable for easy data management and further analysis. Furthermore, we survey novel deep learning methods that have recently been introduced for such tasks.

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Extracting drug–drug interactions with hybrid bidirectional gated recurrent unit and graph convolutional network

Cited by 27 publications

References 25 publications

Incorporating representation learning and multihead attention to improve biomedical cross-sentence n-ary relation extraction

Incorporating representation learning and multihead attention to improve biomedical cross-sentence n-ary relation extraction

Hybrid convolutional bidirectional recurrent neural network based sentiment analysis on movie reviews

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

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