Chemical-induced disease relation extraction via convolutional neural network

Gu, Jinghang; Sun, Fuqing; Qian, Longhua; Zhou, Guodong

doi:10.1093/database/bax024

Cited by 83 publications

(104 citation statements)

References 32 publications

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“…Various machine learning-based methods including supervised machine learning methods (30, 31), pattern clustering (32) and topic modeling (33) were used before deep learning models became dominant among the recent advances. Besides conventional DNN models (34, 35), dependency (15, 36) and character level (16) information have been used to enhance the models with improvement over their baselines. Recently, the attention mechanism on top of DNN models has shown promise in various NLP tasks, such as machine translation (23), question answering (37), document classification (38) as well as relation extraction.…”

Section: Related Workmentioning

confidence: 99%

Extracting chemical–protein relations using attention-based neural networks

et al. 2018

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Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical–protein interactions. We investigate multiple deep neural network (DNN) models, including convolutional neural networks, recurrent neural networks (RNNs) and attention-based (ATT-) RNNs (ATT-RNNs) to extract chemical–protein relations. Our experimental results indicate that ATT-RNN models outperform the same models without using attention and the ATT-gated recurrent unit (ATT-GRU) achieves the best performing micro average F1 score of 0.527 on the test set among the tested DNNs. In addition, the result of word-level attention weights also shows that attention mechanism is effective on selecting the most important trigger words when trained with semantic relation labels without the need of semantic parsing and feature engineering. The source code of this work is available at https://github.com/ohnlp/att-chemprot.

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Section: Related Workmentioning

confidence: 99%

Extracting chemical–protein relations using attention-based neural networks

et al. 2018

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“…Gu et al [6], [16], some heuristic rules are applied to construct the intra-and inter-sentence levels instances. The details of the heuristic rules are listed as follows.…”

Section: Relation Instance Constructionmentioning

confidence: 99%

“…Following Gu et al [16], we also use the Medical Subject Headings (MeSH) controlled vocabulary [23] to determine the hypernym relationship between entities in a document. Then we remove the hyper-relation instances that involve more general entities than other entities already existing in the candidate instance.…”

Section: Hypernym Filteringmentioning

confidence: 99%

Chemical-induced disease relation extraction with dependency information and prior knowledge

Zhou

Ning

Yang

et al. 2018

Journal of Biomedical Informatics

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Chemical-disease relation (CDR) extraction is significantly important to various areas of biomedical research and health care. Nowadays, many large-scale biomedical knowledge bases (KBs) containing triples about entity pairs and their relations have been built. KBs are important resources for biomedical relation extraction. However, previous research pays little attention to prior knowledge. In addition, the dependency tree contains important syntactic and semantic information, which helps to improve relation extraction. So how to effectively use it is also worth studying. In this paper, we propose a novel convolutional attention network (CAN) for CDR extraction. Firstly, we extract the shortest dependency path (SDP) between chemical and disease pairs in a sentence, which includes a sequence of words, dependency directions, and dependency relation tags. Then the convolution operations are performed on the SDP to produce deep semantic dependency features. After that, an attention mechanism is employed to learn the importance/weight of each semantic dependency vector related to knowledge representations learned from KBs. Finally, in order to combine dependency information and prior knowledge, the concatenation of weighted semantic dependency representations and knowledge representations is fed to the softmax layer for classification. Experiments on the BioCreative V CDR dataset show that our method achieves comparable performance with the state-of-the-art systems, and both dependency information and prior knowledge play important roles in CDR extraction task.

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“…Previous researches on biomedical relation extraction mostly focus on protein-protein interactions (1)(2)(3)(4), drug-drug interactions (6)(7)(8)(9), and chemical-disease relations (10)(11)(12)(13)(14). They can be roughly divided into three categories: rule-based methods, feature-based methods and neural network-based methods.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, deep learning techniques have achieved great success in relation extraction tasks (4,9,12,13,(16)(17)(18)(19)(20). Without feature engineering efforts, deep neural networks could effectively extract semantic information for relation extraction.…”

Section: Related Workmentioning

confidence: 99%

Leveraging prior knowledge for protein–protein interaction extraction with memory network

et al. 2018

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Automatically extracting protein–protein interactions (PPIs) from biomedical literature provides additional support for precision medicine efforts. This paper proposes a novel memory network-based model (MNM) for PPI extraction, which leverages prior knowledge about protein–protein pairs with memory networks. The proposed MNM captures important context clues related to knowledge representations learned from knowledge bases. Both entity embeddings and relation embeddings of prior knowledge are effective in improving the PPI extraction model, leading to a new state-of-the-art performance on the BioCreative VI PPI dataset. The paper also shows that multiple computational layers over an external memory are superior to long short-term memory networks with the local memories.Database URL: http://www.biocreative.org/tasks/biocreative-vi/track-4/

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Chemical-induced disease relation extraction via convolutional neural network

Cited by 83 publications

References 32 publications

Extracting chemical–protein relations using attention-based neural networks

Extracting chemical–protein relations using attention-based neural networks

Chemical-induced disease relation extraction with dependency information and prior knowledge

Leveraging prior knowledge for protein–protein interaction extraction with memory network

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