Deep learning for extracting protein-protein interactions from
            biomedical literature

Peng, Yifan; Lu, Zhiyong

doi:10.18653/v1/w17-2304

Cited by 81 publications

(60 citation statements)

References 35 publications

(53 reference statements)

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“…We are also interested in extending the method to chemical–protein relations that manifest beyond the sentence boundaries. Finally, we would like to test and generalize this approach to other biomedical relations such as protein–protein interactions (5). …”

Section: Resultsmentioning

confidence: 99%

“…We followed the work of Peng and Lu (5) to build our CNN model. Instead of using multichannels, we applied one channel but used two input layers (Figure 3).…”

Section: Methodsmentioning

confidence: 99%

“…When compared with traditional machine-learning methods, they may be able to overcome the feature sparsity and engineering problems. For example, various convolutional neural networks (CNNs) were found to be well-suited for protein–protein interaction and chemical-induced disease extraction (5–7). Furthermore, recurrent neural networks (RNNs) with multiple attention layers have been used to extract drug–drug interactions from text (8, 9).…”

Section: Introductionmentioning

confidence: 99%

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Extracting chemical–protein relations with ensembles of SVM and deep learning models

et al. 2018

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Mining relations between chemicals and proteins from the biomedical literature is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical–protein relations in running text (PubMed abstracts). This work describes our CHEMPROT track entry, which is an ensemble of three systems, including a support vector machine, a convolutional neural network, and a recurrent neural network. Their output is combined using majority voting or stacking for final predictions. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an F-score of 0.6410 during the challenge, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature and achieving the highest performance in the task during the 2017 challenge.Database URL: http://www.biocreative.org/tasks/biocreative-vi/track-5/

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Section: Resultsmentioning

confidence: 99%

“…We followed the work of Peng and Lu (5) to build our CNN model. Instead of using multichannels, we applied one channel but used two input layers (Figure 3).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Extracting chemical–protein relations with ensembles of SVM and deep learning models

et al. 2018

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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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“…The RE task is to identify relations between 7 entities mentioned in natural language texts and its importance in biomedical domain 8 stems in large part due to the fact that manual curation lags behind the growth in 9 biomedical research literature. Developing high-performing systems to automatically 10 extract relations from text is critical, and filling an important need. 11 There has been considerable effort invested in the extraction of different relations in 12 BioNLP.…”

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confidence: 99%

Using distant supervision to augment manually annotated data for relation extraction

et al. 2019

Preprint

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Significant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature. Building large-size datasets for deep learning is expensive since it involves considerable human effort and usually requires domain expertise in specialized fields. In this work, we consider augmenting manually annotated data with large amounts of data using distant supervision. However, data obtained by distant supervision is often noisy, we first apply some heuristics to remove some of the incorrect annotations. Then using methods inspired from transfer learning, we show that the resulting models outperform models trained on the original manually annotated sets. 14 annotated as positive or negative depending on whether that sentence expresses a 15 relation of interest among the marked entities. Many traditional (non-deep learning) 16 machine learning methods have been applied on these problems (see e.g. [4] [5] [6] [7]) 17 with most of them being feature-based or kernel-based methods. However, 18 April 26, 2019 1/14 features/kernels have to be manually designed and their performance are not up to par 19 with deep learning models when there is sufficient data. 20 Recently, deep learning methods show great advancement in various NLP tasks. 21 Convolutional neural network and recurrent neural network are two well-studied types 22 of deep learning architecture in NLP field. Promising results have been achieved by 23 CNN model [8] [9] and current state-of-art CNN systems on relation extraction usually 24 utilize refined architecture to incorporate more lexical and syntactic information. In [2], 25 they applied piecewise max pooling process after convolutional layer to extract the 26 structural features between the entities. The proposed method (piecewise CNN) 27 exhibits superior performance compared with pure CNN. Peng et al. [10] proposed 28 multiple channels in CNN to incorporate the syntactic dependency information and 29 better capture longer distance dependencies. Also, RNN model shows its advantage on 30 relation extraction, the model in [11] achieves state-of-the-art results on protein-protein 31 interaction (PPI) task only using the word embedding as the input of LSTM model. 32 However, each new task requires its own annotated data for training the deep 33 learning model. The annotation process of data needs considerable human effort to put 34 a label on each data instance and often requires domain expertise, especially in 35 specialized fields like Biomedicine. This issue is particularly onerous with deep learning 36 since the models require setting of a large number of parameters and hence typically 37 require large datasets. Currently, only small datasets are available for a number of tasks 38 and this situation can hinder us from achieving the full potential of deep learning 39 models. In...

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Deep learning for extracting protein-protein interactions from biomedical literature

Cited by 81 publications

References 35 publications

Extracting chemical–protein relations with ensembles of SVM and deep learning models

Extracting chemical–protein relations with ensembles of SVM and deep learning models

Extracting chemical–protein relations using attention-based neural networks

Using distant supervision to augment manually annotated data for relation extraction

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