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Cai L, Li J, Lv H, Liu W, Niu H, Wang Z. Integrating domain knowledge for biomedical text analysis into deep learning: A survey. J Biomed Inform 2023; 143:104418. [PMID: 37290540 DOI: 10.1016/j.jbi.2023.104418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 04/24/2023] [Accepted: 05/31/2023] [Indexed: 06/10/2023]
Abstract
The past decade has witnessed an explosion of textual information in the biomedical field. Biomedical texts provide a basis for healthcare delivery, knowledge discovery, and decision-making. Over the same period, deep learning has achieved remarkable performance in biomedical natural language processing, however, its development has been limited by well-annotated datasets and interpretability. To solve this, researchers have considered combining domain knowledge (such as biomedical knowledge graph) with biomedical data, which has become a promising means of introducing more information into biomedical datasets and following evidence-based medicine. This paper comprehensively reviews more than 150 recent literature studies on incorporating domain knowledge into deep learning models to facilitate typical biomedical text analysis tasks, including information extraction, text classification, and text generation. We eventually discuss various challenges and future directions.
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Affiliation(s)
- Linkun Cai
- School of Biological Science and Medical Engineering, Beihang University, 100191 Beijing, China
| | - Jia Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050 Beijing, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050 Beijing, China
| | - Wenjuan Liu
- Aerospace Center Hospital, 100049 Beijing, China
| | - Haijun Niu
- School of Biological Science and Medical Engineering, Beihang University, 100191 Beijing, China
| | - Zhenchang Wang
- School of Biological Science and Medical Engineering, Beihang University, 100191 Beijing, China; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050 Beijing, China.
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Luo L, Wei CH, Lai PT, Chen Q, Islamaj R, Lu Z. Assigning species information to corresponding genes by a sequence labeling framework. Database (Oxford) 2022; 2022:6760187. [PMID: 36227127 PMCID: PMC9558450 DOI: 10.1093/database/baac090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/26/2022] [Accepted: 10/11/2022] [Indexed: 01/24/2023]
Abstract
The automatic assignment of species information to the corresponding genes in a research article is a critically important step in the gene normalization task, whereby a gene mention is normalized and linked to a database record or an identifier by a text-mining algorithm. Existing methods typically rely on heuristic rules based on gene and species co-occurrence in the article, but their accuracy is suboptimal. We therefore developed a high-performance method, using a novel deep learning-based framework, to identify whether there is a relation between a gene and a species. Instead of the traditional binary classification framework in which all possible pairs of genes and species in the same article are evaluated, we treat the problem as a sequence labeling task such that only a fraction of the pairs needs to be considered. Our benchmarking results show that our approach obtains significantly higher performance compared to that of the rule-based baseline method for the species assignment task (from 65.8-81.3% in accuracy). The source code and data for species assignment are freely available. Database URL https://github.com/ncbi/SpeciesAssignment.
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Affiliation(s)
| | | | - Po-Ting Lai
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Qingyu Chen
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Rezarta Islamaj
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Zhiyong Lu
- *Corresponding author: Tel: +301 594 7089; Fax: +301 480 2288;
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Ramdani H, Brun A, Bonjour E, Monticolo D. DEEP, a methodology for entity extraction using organizational patterns: Application to job offers. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Luo L, Lai PT, Wei CH, Lu Z. A sequence labeling framework for extracting drug-protein relations from biomedical literature. Database (Oxford) 2022; 2022:baac058. [PMID: 35856889 PMCID: PMC9297941 DOI: 10.1093/database/baac058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/24/2022] [Accepted: 07/14/2022] [Indexed: 06/15/2023]
Abstract
UNLABELLED Automatic extracting interactions between chemical compound/drug and gene/protein are significantly beneficial to drug discovery, drug repurposing, drug design and biomedical knowledge graph construction. To promote the development of the relation extraction between drug and protein, the BioCreative VII challenge organized the DrugProt track. This paper describes the approach we developed for this task. In addition to the conventional text classification framework that has been widely used in relation extraction tasks, we propose a sequence labeling framework to drug-protein relation extraction. We first comprehensively compared the cutting-edge biomedical pre-trained language models for both frameworks. Then, we explored several ensemble methods to further improve the final performance. In the evaluation of the challenge, our best submission (i.e. the ensemble of models in two frameworks via major voting) achieved the F1-score of 0.795 on the official test set. Further, we realized the sequence labeling framework is more efficient and achieves better performance than the text classification framework. Finally, our ensemble of the sequence labeling models with majority voting achieves the best F1-score of 0.800 on the test set. DATABASE URL https://github.com/lingluodlut/BioCreativeVII_DrugProt.
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Affiliation(s)
- Ling Luo
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Po-Ting Lai
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Zhiyong Lu
- *Corresponding author: Tel: 301 594 7089; Fax: 301 480 2288;
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Chen J, Hu B, Peng W, Chen Q, Tang B. Biomedical relation extraction via knowledge-enhanced reading comprehension. BMC Bioinformatics 2022; 23:20. [PMID: 34991458 PMCID: PMC8734165 DOI: 10.1186/s12859-021-04534-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 12/13/2021] [Indexed: 12/01/2022] Open
Abstract
Background In biomedical research, chemical and disease relation extraction from unstructured biomedical literature is an essential task. Effective context understanding and knowledge integration are two main research problems in this task. Most work of relation extraction focuses on classification for entity mention pairs. Inspired by the effectiveness of machine reading comprehension (RC) in the respect of context understanding, solving biomedical relation extraction with the RC framework at both intra-sentential and inter-sentential levels is a new topic worthy to be explored. Except for the unstructured biomedical text, many structured knowledge bases (KBs) provide valuable guidance for biomedical relation extraction. Utilizing knowledge in the RC framework is also worthy to be investigated. We propose a knowledge-enhanced reading comprehension (KRC) framework to leverage reading comprehension and prior knowledge for biomedical relation extraction. First, we generate questions for each relation, which reformulates the relation extraction task to a question answering task. Second, based on the RC framework, we integrate knowledge representation through an efficient knowledge-enhanced attention interaction mechanism to guide the biomedical relation extraction. Results The proposed model was evaluated on the BioCreative V CDR dataset and CHR dataset. Experiments show that our model achieved a competitive document-level F1 of 71.18% and 93.3%, respectively, compared with other methods. Conclusion Result analysis reveals that open-domain reading comprehension data and knowledge representation can help improve biomedical relation extraction in our proposed KRC framework. Our work can encourage more research on bridging reading comprehension and biomedical relation extraction and promote the biomedical relation extraction.
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Affiliation(s)
- Jing Chen
- Intelligent Computing Research Center, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Baotian Hu
- Intelligent Computing Research Center, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
| | - Weihua Peng
- Baidu International Technology (Shenzhen) Co., Ltd, Shenzhen, China
| | - Qingcai Chen
- Intelligent Computing Research Center, Harbin Institute of Technology (Shenzhen), Shenzhen, China. .,Peng Cheng Laboratory, Shenzhen, China.
| | - Buzhou Tang
- Intelligent Computing Research Center, Harbin Institute of Technology (Shenzhen), Shenzhen, China.,Peng Cheng Laboratory, Shenzhen, China
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An attentive joint model with transformer-based weighted graph convolutional network for extracting adverse drug event relation. J Biomed Inform 2021; 125:103968. [PMID: 34871807 DOI: 10.1016/j.jbi.2021.103968] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/25/2021] [Accepted: 11/27/2021] [Indexed: 11/21/2022]
Abstract
Adverse drug event (ADE) relation extraction is a crucial task for drug safety surveillance which aims to discover potential relations between ADE mentions from unstructured medical texts. To date, the graph convolutional networks (GCN) have been the state-of-the-art solutions for improving the ability of relation extraction task. However, there are many challenging issues that should be addressed. Among these, the syntactic information is not fully exploited by GCN-based methods, especially the diversified dependency edges. Still, these methods fail to effectively extract complex relations that include nested, discontinuous and overlapping mentions. Besides, the task is primarily regarded as a classification problem where each candidate relation is treated independently which neglects the interaction between other relations. To deal with these issues, in this paper, we propose an attentive joint model with transformer-based weighted GCN for extracting ADE Relations, called ADERel. Firstly, the ADERel system formulates the ADE relation extraction task as an N-level sequence labelling so as to model the complex relations in different levels and capture greater interaction between relations. Then, it exploits our neural joint model to process the N-level sequences jointly. The joint model leverages the contextual and structural information by adopting a shared representation that combines a bidirectional encoder representation from transformers (BERT) and our proposed weighted GCN (WGCN). The latter assigns a score to each dependency edge within a sentence so as to capture rich syntactic features and determine the most influential edges for extracting ADE relations. Finally, the system employs a multi-head attention to exchange boundary knowledge across levels. We evaluate ADERel on two benchmark datasets from TAC 2017 and n2c2 2018 shared tasks. The experimental results show that ADERel is superior in performance compared with several state-of-the-art methods. The results also demonstrate that incorporating a transformer model with WGCN makes the proposed system more effective for extracting various types of ADE relations. The evaluations further highlight that ADERel takes advantage of joint learning, showing its effectiveness in recognizing complex relations.
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Li Z, Chen H, Qi R, Lin H, Chen H. DocR-BERT: Document-level R-BERT for Chemical-induced Disease Relation Extraction via Gaussian Probability Distribution. IEEE J Biomed Health Inform 2021; 26:1341-1352. [PMID: 34591774 DOI: 10.1109/jbhi.2021.3116769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Chemical-induced disease (CID) relation extraction from biomedical articles plays an important role in disease treatment and drug development. Existing methods are insufficient for capturing complete document level semantic information due to ignoring semantic information of entities in different sentences. In this work, we proposed an effective document-level relation extraction model to automatically extract intra-/inter-sentential CID relations from articles. Firstly, our model employed BERT to generate contextual semantic representations of the title, abstract and shortest dependency paths (SDPs). Secondly, to enhance the semantic representation of the whole document, cross attention with self-attention (named cross2self-attention) between abstract, title and SDPs was proposed to learn the mutual semantic information. Thirdly, to distinguish the importance of the target entity in different sentences, the Gaussian probability distribution was utilized to compute the weights of the co-occurrence sentence and its adjacent entity sentences. More complete semantic information of the target entity is collected from all entities occurring in the document via our presented document-level R-BERT (DocR-BERT). Finally, the related representations were concatenated and fed into the softmax function to extract CIDs. We evaluated the model on the CDR corpus provided by BioCreative V. The proposed model without external resources is superior in performance as compared with other state-of-the-art models (our model achieves 53.5%, 70%, and 63.7% of the F1-score on inter-/intra-sentential and overall CDR dataset). The experimental results indicate that cross2self-attention, the Gaussian probability distribution and DocR-BERT can effectively improve the CID extraction performance. Furthermore, the mutual semantic information learned by the cross self-attention from abstract towards title can significantly influence the extraction performance of document-level biomedical relation extraction tasks.
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