Using deep neural network to recognize mutation entities in biomedical literature

Tong, Fan; Luo, Zheheng; Zhao, Dongsheng

doi:10.1109/bibm.2018.8621134

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…BioNER, modeled as a sequence labeling problem [9], can then be solved end-to-end by deep learning methods, which avoids manual feature engineering and improves the performance to a certain extent. [10][11][12][13] used long short-term memory (LSTM) networks, and [14][15][16] used conditional random field (CRF) to recognize biomedical entities. However, a major problem is the lack of large-scale high-quality annotated training data.…”

Section: Introductionmentioning

confidence: 99%

BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework

Zheng

Luo

et al. 2022

Preprint

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Background: Automatic and accurate recognition of various biomedical named entities from literature is an important task of biomedical text mining, which is the foundation of extracting biomedical knowledge from unstructured texts into structured formats. Using the sequence labeling framework and deep neural networks to implement biomedical named entity recognition (BioNER) is a common method at present. However, the above method often underutilizes syntactic features such as dependencies and topology of sentences. Therefore, it is an urgent problem to be solved to integrate semantic and syntactic features into the BioNER model. Results: In this paper, we propose a novel biomedical named entity recognition model, named BioByGANS (BioBERT/SpaCy-Graph Attention Network-Softmax), which uses a graph to model the dependencies and topology of a sentence and formulate the BioNER task as a node classification problem. This formulation can introduce more topological features of language and no longer be only concerned about the distance between words in the sequence. First, we use periods to segment sentences and spaces and symbols to segment words. Second, contextual features are encoded by BioBERT, and syntactic features such as POS, dependencies and topology are preprocessed by SpaCy respectively. A graph attention network is then used to generate a fusing representation considering both the contextual features and syntactic features. Last, a softmax function is used to calculate the probabilities and get the results. We conduct experiments on 8 benchmark datasets, and our proposed model outperforms existing BioNER state-of-the-art methods on the BC2GM, JNLPBA, BC4CHEMD, BC5CDR-chem, BC5CDR-disease, NCBI-disease, Species-800, and LINNAEUS datasets, and achieves F1-scores of 85.15%, 78.16%, 92.97%, 94.74%, 87.74%, 91.57%, 75.01%, 90.99%, respectively. Conclusion: The experimental results on 8 biomedical benchmark datasets demonstrate the effectiveness of our model, and indicate that formulating the BioNER task into a node classification problem and combining syntactic features into the graph attention networks can significantly improve model performance.

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Section: Introductionmentioning

confidence: 99%

BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework

Zheng

Luo

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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The Entity Recognition of Thai Poem Compose by Sunthorn Phu by Using the Bidirectional Long Short Term Memory Technique

Khongtum

Promrit

Waijanya

2019

Lecture Notes in Computer Science

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Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison

Song

Liu

et al. 2021

Briefings in Bioinformatics

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The biomedical literature is growing rapidly, and the extraction of meaningful information from the large amount of literature is increasingly important. Biomedical named entity (BioNE) identification is one of the critical and fundamental tasks in biomedical text mining. Accurate identification of entities in the literature facilitates the performance of other tasks. Given that an end-to-end neural network can automatically extract features, several deep learning-based methods have been proposed for BioNE recognition (BioNER), yielding state-of-the-art performance. In this review, we comprehensively summarize deep learning-based methods for BioNER and datasets used in training and testing. The deep learning methods are classified into four categories: single neural network-based, multitask learning-based, transfer learning-based and hybrid model-based methods. They can be applied to BioNER in multiple domains, and the results are determined by the dataset size and type. Lastly, we discuss the future development and opportunities of BioNER methods.

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Using deep neural network to recognize mutation entities in biomedical literature

Cited by 6 publications

References 11 publications

BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework

BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework

The Entity Recognition of Thai Poem Compose by Sunthorn Phu by Using the Bidirectional Long Short Term Memory Technique

Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison

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