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Zhao W, Zhang J, Yang J, Jiang X, He T. Document-Level Chemical-Induced Disease Relation Extraction via Hierarchical Representation Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2782-2793. [PMID: 34077368 DOI: 10.1109/tcbb.2021.3086090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Over the past decades, Chemical-induced Disease (CID) relations have attracted extensive attention in biomedical community, reflecting wide applications in biomedical research and healthcare field. However, prior efforts fail to make full use of the interaction between local and global contexts in biomedical document, and the derived performance needs to be improved accordingly. In this paper, we propose a novel framework for document-level CID relation extraction. More specifically, a stacked Hypergraph Aggregation Neural Network (HANN) layers are introduced to model the complicated interaction between local and global contexts, based on which better contextualized representations are obtained for CID relation extraction. In addition, the CID Relation Heterogeneous Graph is constructed to capture the information with different granularities and improve further the performance of CID relation classification. Experiments on a real-world dataset demonstrate the effectiveness of the proposed framework.
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Li Z, Wang M, Peng D, Liu J, Xie Y, Dai Z, Zou X. Identification of Chemical-Disease Associations Through Integration of Molecular Fingerprint, Gene Ontology and Pathway Information. Interdiscip Sci 2022; 14:683-696. [PMID: 35391615 DOI: 10.1007/s12539-022-00511-5] [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: 10/28/2021] [Revised: 03/16/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
The identification of chemical-disease association types is helpful not only to discovery lead compounds and study drug repositioning, but also to treat disease and decipher pathomechanism. It is very urgent to develop computational method for identifying potential chemical-disease association types, since wet methods are usually expensive, laborious and time-consuming. In this study, molecular fingerprint, gene ontology and pathway are utilized to characterize chemicals and diseases. A novel predictor is proposed to recognize potential chemical-disease associations at the first layer, and further distinguish whether their relationships belong to biomarker or therapeutic relations at the second layer. The prediction performance of current method is assessed using the benchmark dataset based on ten-fold cross-validation. The practical prediction accuracies of the first layer and the second layer are 78.47% and 72.07%, respectively. The recognition ability for lead compounds, new drug indications, potential and true chemical-disease association pairs has also been investigated and confirmed by constructing a variety of datasets and performing a series of experiments. It is anticipated that the current method can be considered as a powerful high-throughput virtual screening tool for drug researches and developments.
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Affiliation(s)
- Zhanchao Li
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People's Republic of China.
- NMPA Key Laboratory for Technology Research and Evaluation of Pharmacovigilance, Guangzhou, 510006, People's Republic of China.
- Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica of State Administration of Traditional Chinese Medicine, Guangzhou, 510006, People's Republic of China.
| | - Mengru Wang
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People's Republic of China
| | - Dongdong Peng
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People's Republic of China
| | - Jie Liu
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People's Republic of China
| | - Yun Xie
- HuiZhou University, Huizhou, 516007, People's Republic of China
| | - Zong Dai
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Xiaoyong Zou
- School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China.
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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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Wu H, Ji J, Tian H, Chen Y, Ge W, Zhang H, Yu F, Zou J, Nakamura M, Liao J. Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding-Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model. JMIR Med Inform 2021; 9:e26407. [PMID: 34855616 PMCID: PMC8686410 DOI: 10.2196/26407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/22/2021] [Accepted: 10/05/2021] [Indexed: 12/17/2022] Open
Abstract
Background With the increasing variety of drugs, the incidence of adverse drug events (ADEs) is increasing year by year. Massive numbers of ADEs are recorded in electronic medical records and adverse drug reaction (ADR) reports, which are important sources of potential ADR information. Meanwhile, it is essential to make latent ADR information automatically available for better postmarketing drug safety reevaluation and pharmacovigilance. Objective This study describes how to identify ADR-related information from Chinese ADE reports. Methods Our study established an efficient automated tool, named BBC-Radical. BBC-Radical is a model that consists of 3 components: Bidirectional Encoder Representations from Transformers (BERT), bidirectional long short-term memory (bi-LSTM), and conditional random field (CRF). The model identifies ADR-related information from Chinese ADR reports. Token features and radical features of Chinese characters were used to represent the common meaning of a group of words. BERT and Bi-LSTM-CRF were novel models that combined these features to conduct named entity recognition (NER) tasks in the free-text section of 24,890 ADR reports from the Jiangsu Province Adverse Drug Reaction Monitoring Center from 2010 to 2016. Moreover, the man-machine comparison experiment on the ADE records from Drum Tower Hospital was designed to compare the NER performance between the BBC-Radical model and a manual method. Results The NER model achieved relatively high performance, with a precision of 96.4%, recall of 96.0%, and F1 score of 96.2%. This indicates that the performance of the BBC-Radical model (precision 87.2%, recall 85.7%, and F1 score 86.4%) is much better than that of the manual method (precision 86.1%, recall 73.8%, and F1 score 79.5%) in the recognition task of each kind of entity. Conclusions The proposed model was competitive in extracting ADR-related information from ADE reports, and the results suggest that the application of our method to extract ADR-related information is of great significance in improving the quality of ADR reports and postmarketing drug safety evaluation.
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Affiliation(s)
- Hong Wu
- School of Science, China Pharmaceutical University, Nanjing, China
| | - Jiatong Ji
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Haimei Tian
- School of Computer Engineering, Jinling Institute of Technology, Nanjing, China
| | - Yao Chen
- School of Science, China Pharmaceutical University, Nanjing, China
| | - Weihong Ge
- Department of Pharmacy, Nanjing Drum Tower Hospital, Nanjing, China
| | - Haixia Zhang
- Department of Pharmacy, Nanjing Drum Tower Hospital, Nanjing, China
| | - Feng Yu
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jianjun Zou
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Mitsuhiro Nakamura
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, Gifu, Japan
| | - Jun Liao
- School of Science, China Pharmaceutical University, Nanjing, China
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Kanjirangat V, Rinaldi F. Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet Information. J Biomed Inform 2021; 122:103893. [PMID: 34481058 DOI: 10.1016/j.jbi.2021.103893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 08/17/2021] [Accepted: 08/22/2021] [Indexed: 10/20/2022]
Abstract
Entity relation extraction plays an important role in the biomedical, healthcare, and clinical research areas. Recently, pre-trained models based on transformer architectures and their variants have shown remarkable performances in various natural language processing tasks. Most of these variants were based on slight modifications in the architectural components, representation schemes and augmenting data using distant supervision methods. In distantly supervised methods, one of the main challenges is pruning out noisy samples. A similar situation can arise when the training samples are not directly available but need to be constructed from the given dataset. The BioCreative V Chemical Disease Relation (CDR) task provides a dataset that does not explicitly offer mention-level gold annotations and hence replicates the above scenario. Selecting the representative sentences from the given abstract or document text that could convey a potential entity relationship becomes essential. Most of the existing methods in literature propose to either consider the entire text or all the sentences which contain the entity mentions. This could be a computationally expensive and time consuming approach. This paper presents a novel approach to handle such scenarios, specifically in biomedical relation extraction. We propose utilizing the Shortest Dependency Path (SDP) features for constructing data samples by pruning out noisy information and selecting the most representative samples for model learning. We also utilize triplet information in model learning using the biomedical variant of BERT, viz., BioBERT. The problem is represented as a sentence pair classification task using the sentence and the entity-relation pair as input. We analyze the approach on both intra-sentential and inter-sentential relations in the CDR dataset. The proposed approach that utilizes the SDP and triplet features presents promising results, specifically on the inter-sentential relation extraction task. We make the code used for this work publicly available on Github.1.
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Affiliation(s)
- Vani Kanjirangat
- Istituto Dalle Molle di Studi sull'Intelligenza Artificiale USI/SUPSI, Lugano, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Fabio Rinaldi
- Istituto Dalle Molle di Studi sull'Intelligenza Artificiale USI/SUPSI, Lugano, Switzerland; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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Delmas M, Filangi O, Paulhe N, Vinson F, Duperier C, Garrier W, Saunier PE, Pitarch Y, Jourdan F, Giacomoni F, Frainay C. FORUM: Building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases. Bioinformatics 2021; 37:3896-3904. [PMID: 34478489 PMCID: PMC8570811 DOI: 10.1093/bioinformatics/btab627] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 08/16/2021] [Accepted: 09/01/2021] [Indexed: 11/22/2022] Open
Abstract
Motivation Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories. Results The use of a Semantic Web framework on biological data allows us to apply ontological-based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries. Availability and implementation A web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM KG, are available at https://forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https://github.com/eMetaboHUB/Forum-DiseasesChem. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- M Delmas
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31300, France
| | - O Filangi
- IGEPP, INRAE, Institut Agro, Université de Rennes, Domaine de la Motte, Le Rheu, 35653, France
| | - N Paulhe
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, F-63000, France
| | - F Vinson
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31300, France
| | - C Duperier
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, F-63000, France
| | - W Garrier
- ISIMA, Campus des Cézeaux, Aubière, 63177, France
| | - P-E Saunier
- ISIMA, Campus des Cézeaux, Aubière, 63177, France
| | - Y Pitarch
- IRIT, Université de Toulouse, Cours Rose Dieng-Kuntz, Toulouse, 31400, France
| | - F Jourdan
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31300, France
| | - F Giacomoni
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, F-63000, France
| | - C Frainay
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31300, France
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Zeng D, Zhao C, Quan Z. CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction. Front Genet 2021; 12:624307. [PMID: 33643385 PMCID: PMC7902761 DOI: 10.3389/fgene.2021.624307] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 01/18/2021] [Indexed: 11/26/2022] Open
Abstract
Automatic extraction of chemical-induced disease (CID) relation from unstructured text is of essential importance for disease treatment and drug development. In this task, some relational facts can only be inferred from the document rather than single sentence. Recently, researchers investigate graph-based approaches to extract relations across sentences. It iteratively combines the information from neighbor nodes to model the interactions in entity mentions that exist in different sentences. Despite their success, one severe limitation of the graph-based approaches is the over-smoothing problem, which decreases the model distinguishing ability. In this paper, we propose CID-GCN, an effective Graph Convolutional Networks (GCNs) with gating mechanism, for CID relation extraction. Specifically, we construct a heterogeneous graph which contains mention, sentence and entity nodes. Then, the graph convolution operation is employed to aggregate interactive information on the constructed graph. Particularly, we combine gating mechanism with the graph convolution operation to address the over-smoothing problem. The experimental results demonstrate that our approach significantly outperforms the baselines.
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Affiliation(s)
- Daojian Zeng
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China
| | - Chao Zhao
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China
| | - Zhe Quan
- College of Information Science and Engineering, Hunan University, Changsha, China
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Mitra S, Saha S, Hasanuzzaman M. A Multi-View Deep Neural Network Model for Chemical-Disease Relation Extraction From Imbalanced Datasets. IEEE J Biomed Health Inform 2020; 24:3315-3325. [DOI: 10.1109/jbhi.2020.2983365] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Wang J, Chen X, Zhang Y, Zhang Y, Wen J, Lin H, Yang Z, Wang X. Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation. JMIR Med Inform 2020; 8:e17638. [PMID: 32459636 PMCID: PMC7458061 DOI: 10.2196/17638] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 04/14/2020] [Accepted: 04/25/2020] [Indexed: 11/22/2022] Open
Abstract
Background Automatically extracting relations between chemicals and diseases plays an important role in biomedical text mining. Chemical-disease relation (CDR) extraction aims at extracting complex semantic relationships between entities in documents, which contain intrasentence and intersentence relations. Most previous methods did not consider dependency syntactic information across the sentences, which are very valuable for the relations extraction task, in particular, for extracting the intersentence relations accurately. Objective In this paper, we propose a novel end-to-end neural network based on the graph convolutional network (GCN) and multihead attention, which makes use of the dependency syntactic information across the sentences to improve CDR extraction task. Methods To improve the performance of intersentence relation extraction, we constructed a document-level dependency graph to capture the dependency syntactic information across sentences. GCN is applied to capture the feature representation of the document-level dependency graph. The multihead attention mechanism is employed to learn the relatively important context features from different semantic subspaces. To enhance the input representation, the deep context representation is used in our model instead of traditional word embedding. Results We evaluate our method on CDR corpus. The experimental results show that our method achieves an F-measure of 63.5%, which is superior to other state-of-the-art methods. In the intrasentence level, our method achieves a precision, recall, and F-measure of 59.1%, 81.5%, and 68.5%, respectively. In the intersentence level, our method achieves a precision, recall, and F-measure of 47.8%, 52.2%, and 49.9%, respectively. Conclusions The GCN model can effectively exploit the across sentence dependency information to improve the performance of intersentence CDR extraction. Both the deep context representation and multihead attention are helpful in the CDR extraction task.
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Affiliation(s)
- Jian Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Xiaoyu Chen
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Yu Zhang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Yijia Zhang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Jiabin Wen
- Department of VIP, The Second Hospital of Dalian Medical University, Dalian, China
| | - Hongfei Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Zhihao Yang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Xin Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
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Kilicoglu H, Rosemblat G, Fiszman M, Shin D. Broad-coverage biomedical relation extraction with SemRep. BMC Bioinformatics 2020; 21:188. [PMID: 32410573 PMCID: PMC7222583 DOI: 10.1186/s12859-020-3517-7] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 04/29/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In the era of information overload, natural language processing (NLP) techniques are increasingly needed to support advanced biomedical information management and discovery applications. In this paper, we present an in-depth description of SemRep, an NLP system that extracts semantic relations from PubMed abstracts using linguistic principles and UMLS domain knowledge. We also evaluate SemRep on two datasets. In one evaluation, we use a manually annotated test collection and perform a comprehensive error analysis. In another evaluation, we assess SemRep's performance on the CDR dataset, a standard benchmark corpus annotated with causal chemical-disease relationships. RESULTS A strict evaluation of SemRep on our manually annotated dataset yields 0.55 precision, 0.34 recall, and 0.42 F 1 score. A relaxed evaluation, which more accurately characterizes SemRep performance, yields 0.69 precision, 0.42 recall, and 0.52 F 1 score. An error analysis reveals named entity recognition/normalization as the largest source of errors (26.9%), followed by argument identification (14%) and trigger detection errors (12.5%). The evaluation on the CDR corpus yields 0.90 precision, 0.24 recall, and 0.38 F 1 score. The recall and the F 1 score increase to 0.35 and 0.50, respectively, when the evaluation on this corpus is limited to sentence-bound relationships, which represents a fairer evaluation, as SemRep operates at the sentence level. CONCLUSIONS SemRep is a broad-coverage, interpretable, strong baseline system for extracting semantic relations from biomedical text. It also underpins SemMedDB, a literature-scale knowledge graph based on semantic relations. Through SemMedDB, SemRep has had significant impact in the scientific community, supporting a variety of clinical and translational applications, including clinical decision making, medical diagnosis, drug repurposing, literature-based discovery and hypothesis generation, and contributing to improved health outcomes. In ongoing development, we are redesigning SemRep to increase its modularity and flexibility, and addressing weaknesses identified in the error analysis.
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Affiliation(s)
- Halil Kilicoglu
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, Bethesda, 20894 MD USA
- University of Illinois at Urbana-Champaign, School of Information Sciences, 501 E Daniel Street, Champaign, 61820 IL USA
| | - Graciela Rosemblat
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, Bethesda, 20894 MD USA
| | | | - Dongwook Shin
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, Bethesda, 20894 MD USA
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Zhou H, Yang Y, Ning S, Liu Z, Lang C, Lin Y, Huang D. Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1879-1889. [PMID: 29994540 DOI: 10.1109/tcbb.2018.2838661] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the development of biomedical research. KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction. However, previous researches pay less attention to the prior knowledge existing in KBs. This paper proposes a neural network-based attention model (NAM) for CDR extraction, which makes full use of context information in documents and prior knowledge in KBs. For a pair of entities in a document, an attention mechanism is employed to select important context words with respect to the relation representations learned from KBs. Experiments on the BioCreative V CDR dataset show that combining context and knowledge representations through the attention mechanism, could significantly improve the CDR extraction performance while achieve comparable results with state-of-the-art systems.
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Gu J, Sun F, Qian L, Zhou G. Chemical-induced disease relation extraction via attention-based distant supervision. BMC Bioinformatics 2019; 20:403. [PMID: 31331263 PMCID: PMC6647285 DOI: 10.1186/s12859-019-2884-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 05/08/2019] [Indexed: 11/24/2022] Open
Abstract
Background Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extract relations between biomedical entities from scientific literature, its success, however, heavily depends on large-scale biomedical corpora manually annotated with intensive labor and tremendous investment. Results We present an attention-based distant supervision paradigm for the BioCreative-V CDR extraction task. Training examples at both intra- and inter-sentence levels are generated automatically from the Comparative Toxicogenomics Database (CTD) without any human intervention. An attention-based neural network and a stacked auto-encoder network are applied respectively to induce learning models and extract relations at both levels. After merging the results of both levels, the document-level CDRs can be finally extracted. It achieves the precision/recall/F1-score of 60.3%/73.8%/66.4%, outperforming the state-of-the-art supervised learning systems without using any annotated corpus. Conclusion Our experiments demonstrate that distant supervision is promising for extracting chemical disease relations from biomedical literature, and capturing both local and global attention features simultaneously is effective in attention-based distantly supervised learning.
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Affiliation(s)
- Jinghang Gu
- Natural Language Processing Lab, School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China.,Big Data Group, Baidu Inc., Beijing, China
| | - Fuqing Sun
- Department of Gynecology Minimally Invasive Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
| | - Longhua Qian
- Natural Language Processing Lab, School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China.
| | - Guodong Zhou
- Natural Language Processing Lab, School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China
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13
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Named entity recognition from Chinese adverse drug event reports with lexical feature based BiLSTM-CRF and tri-training. J Biomed Inform 2019; 96:103252. [PMID: 31323311 DOI: 10.1016/j.jbi.2019.103252] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 07/12/2019] [Accepted: 07/14/2019] [Indexed: 01/04/2023]
Abstract
BACKGROUND The Adverse Drug Event Reports (ADERs) from the spontaneous reporting system are important data sources for studying Adverse Drug Reactions (ADRs) as well as post-marketing pharmacovigilance. Apart from the conventional ADR information contained in the structured section of ADERs, more detailed information such as pre- and post- ADR symptoms, multi-drug usages and ADR-relief treatments are described in the free-text section, which can be mined through Natural Language Processing (NLP) tools. OBJECTIVE The goal of this study was to extract ADR-related entities from free-text section of Chinese ADERs, which can act as supplements for the information contained in structured section, so as to further assist in ADR evaluation. METHODS Three models of Conditional Random Field (CRF), Bidirectional Long Short-Term Memory-CRF (BiLSTM-CRF) and Lexical Feature based BiLSTM-CRF (LF-BiLSTM-CRF) were constructed to conduct Named Entity Recognition (NER) tasks in free-text section of Chinese ADERs. A semi-supervised learning method of tri-training was applied on the basis of the three established models to give un-annotated raw data with reliable tags. RESULTS Among the three basic models, the LF-BiLSTM-CRF achieved the highest average F1 score of 94.35%. After the process of tri-training, almost half of the un-annotated cases were tagged with labels, and the performances of all the three models improved after iterative training. CONCLUSIONS The LF-BiLSTM-CRF model that we constructed could achieve a comparatively high F1 score, and the fusion of CRF, while BiLSTM-CRF and LF-BiLSTM-CRF in tri-training might further strengthen the reliability of predicted tags. The results suggested the usefulness of our methods in developing the specialized NER tools for identifying ADR-related information from Chinese ADERs.
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14
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Zhou H, Liu Z, Ning S, Lang C, Lin Y, Du L. Knowledge-aware attention network for protein-protein interaction extraction. J Biomed Inform 2019; 96:103234. [PMID: 31202937 DOI: 10.1016/j.jbi.2019.103234] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 06/06/2019] [Accepted: 06/13/2019] [Indexed: 11/19/2022]
Abstract
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. However, many of the current PPI extraction methods need extensive feature engineering and cannot make full use of the prior knowledge in knowledge bases (KBs). KBs contain huge amounts of structured information about entities and relationships, therefore play a pivotal role in PPI extraction. This paper proposes a knowledge-aware attention network (KAN) to fuse prior knowledge about protein-protein pairs and context information for PPI extraction. The proposed model first adopts a diagonal-disabled multi-head attention mechanism to encode context sequence along with knowledge representations learned from KBs. Then a novel multi-dimensional attention mechanism is used to select the features that can best describe the encoded context. Experiment results on the BioCreative VI PPI dataset show that the proposed approach could acquire knowledge-aware dependencies between different words in a sequence and lead to a new state-of-the-art performance.
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Affiliation(s)
- Huiwei Zhou
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China.
| | - Zhuang Liu
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China.
| | - Shixian Ning
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China.
| | - Chengkun Lang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China.
| | - Yingyu Lin
- School of Foreign Languages, Dalian University of Technology, Dalian 116024, Liaoning, China.
| | - Lei Du
- School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, Liaoning, China.
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15
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Zhou H, Lang C, Liu Z, Ning S, Lin Y, Du L. Knowledge-guided convolutional networks for chemical-disease relation extraction. BMC Bioinformatics 2019; 20:260. [PMID: 31113357 PMCID: PMC6528333 DOI: 10.1186/s12859-019-2873-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 05/02/2019] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Automatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development. Meanwhile, biomedical experts have built many highly-structured knowledge bases (KBs), which contain prior knowledge about chemicals and diseases. Prior knowledge provides strong support for CDR extraction. How to make full use of it is worth studying. RESULTS This paper proposes a novel model called "Knowledge-guided Convolutional Networks (KCN)" to leverage prior knowledge for CDR extraction. The proposed model first learns knowledge representations including entity embeddings and relation embeddings from KBs. Then, entity embeddings are used to control the propagation of context features towards a chemical-disease pair with gated convolutions. After that, relation embeddings are employed to further capture the weighted context features by a shared attention pooling. Finally, the weighted context features containing additional knowledge information are used for CDR extraction. Experiments on the BioCreative V CDR dataset show that the proposed KCN achieves 71.28% F1-score, which outperforms most of the state-of-the-art systems. CONCLUSIONS This paper proposes a novel CDR extraction model KCN to make full use of prior knowledge. Experimental results demonstrate that KCN could effectively integrate prior knowledge and contexts for the performance improvement.
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Affiliation(s)
- Huiwei Zhou
- School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China.
| | - Chengkun Lang
- School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China
| | - Zhuang Liu
- School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China
| | - Shixian Ning
- School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China
| | - Yingyu Lin
- School of Foreign Languages, Dalian University of Technology, Arts Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China
| | - Lei Du
- School of Mathematical Sciences, Dalian University of Technology, Chuangxinyuan Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China
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16
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Kim J, Kim JJ, Lee H. DigChem: Identification of disease-gene-chemical relationships from Medline abstracts. PLoS Comput Biol 2019; 15:e1007022. [PMID: 31091224 PMCID: PMC6519793 DOI: 10.1371/journal.pcbi.1007022] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 04/10/2019] [Indexed: 11/18/2022] Open
Abstract
Chemicals interact with genes in the process of disease development and treatment. Although much biomedical research has been performed to understand relationships among genes, chemicals, and diseases, which have been reported in biomedical articles in Medline, there are few studies that extract disease-gene-chemical relationships from biomedical literature at a PubMed scale. In this study, we propose a deep learning model based on bidirectional long short-term memory to identify the evidence sentences of relationships among genes, chemicals, and diseases from Medline abstracts. Then, we develop the search engine DigChem to enable disease-gene-chemical relationship searches for 35,124 genes, 56,382 chemicals, and 5,675 diseases. We show that the identified relationships are reliable by comparing them with manual curation and existing databases. DigChem is available at http://gcancer.org/digchem.
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Affiliation(s)
- Jeongkyun Kim
- Gwangju Institute of Science and Technology, School of Electrical Engineering and Computer Science, Gwangju, Korea
| | - Jung-jae Kim
- Institute for Infocomm Research, A-STAR, 138632, Singapore
| | - Hyunju Lee
- Gwangju Institute of Science and Technology, School of Electrical Engineering and Computer Science, Gwangju, Korea
- * E-mail:
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17
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Liu S, Shao Y, Qian L, Zhou G. Hierarchical sequence labeling for extracting BEL statements from biomedical literature. BMC Med Inform Decis Mak 2019; 19:63. [PMID: 30961584 PMCID: PMC6454591 DOI: 10.1186/s12911-019-0758-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Background Extracting relations between bio-entities from biomedical literature is often a challenging task and also an essential step towards biomedical knowledge expansion. The BioCreative community has organized a shared task to evaluate the robustness of the causal relationship extraction algorithms in Biological Expression Language (BEL) from biomedical literature. Method We first map the sentence-level BEL statements in the BC-V training corpus to the corresponding text segments, thus generating hierarchically tagged training instances. A hierarchical sequence labeling model was afterwards induced from these training instances and applied to the test sentences in order to construct the BEL statements. Results The experimental results on extracting BEL statements from BioCreative V Track 4 test corpus show that our method achieves promising performance with an overall F-measure of 31.6%. Furthermore, it has the potential to be enhanced by adopting more advanced machine learning approaches. Conclusion We propose a framework for hierarchical relation extraction using hierarchical sequence labeling on the instance-level training corpus derived from the original sentence-level corpus via word alignment. Its main advantage is that we can make full use of the original training corpus to induce the sequence labelers and then apply them to the test corpus.
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Affiliation(s)
- Suwen Liu
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Yifan Shao
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Longhua Qian
- School of Computer Science and Technology, Soochow University, Suzhou, China.
| | - Guodong Zhou
- School of Computer Science and Technology, Soochow University, Suzhou, China
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18
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Liu S, Cheng W, Qian L, Zhou G. Combining relation extraction with function detection for BEL statement extraction. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019; 2019:5277249. [PMID: 30624649 PMCID: PMC6323300 DOI: 10.1093/database/bay133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 11/26/2018] [Indexed: 11/29/2022]
Abstract
The BioCreative-V community proposed a challenging task of automatic extraction of causal relation network in Biological Expression Language (BEL) from the biomedical literature. Previous studies on this task largely used models induced from other related tasks and then transformed intermediate structures to BEL statements, which left the given training corpus unexplored. To make full use of the BEL training corpus, in this work, we propose a deep learning-based approach to extract BEL statements. Specifically, we decompose the problem into two subtasks: entity relation extraction and entity function detection. First, two attention-based bidirectional long short-term memory networks models are used to extract entity relation and entity function, respectively. Then entity relation and their functions are combined into a BEL statement. In order to boost the overall performance, a strategy of threshold filtering is applied to improve the precision of identified entity functions. We evaluate our approach on the BioCreative-V Track 4 corpus with or without gold entities. The experimental results show that our method achieves the state-of-the-art performance with an overall F1-measure of 46.9% in stage 2 and 21.3% in stage 1, respectively.
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Affiliation(s)
- Suwen Liu
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Wei Cheng
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Longhua Qian
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Guodong Zhou
- School of Computer Science and Technology, Soochow University, Suzhou, China
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19
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Chen T, Wu M, Li H. A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019; 2019:5645655. [PMID: 31800044 PMCID: PMC6892305 DOI: 10.1093/database/baz116] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2019] [Revised: 07/16/2019] [Accepted: 09/02/2019] [Indexed: 01/07/2023]
Abstract
The automatic extraction of meaningful relations from biomedical literature or clinical records is crucial in various biomedical applications. Most of the current deep learning approaches for medical relation extraction require large-scale training data to prevent overfitting of the training model. We propose using a pre-trained model and a fine-tuning technique to improve these approaches without additional time-consuming human labeling. Firstly, we show the architecture of Bidirectional Encoder Representations from Transformers (BERT), an approach for pre-training a model on large-scale unstructured text. We then combine BERT with a one-dimensional convolutional neural network (1d-CNN) to fine-tune the pre-trained model for relation extraction. Extensive experiments on three datasets, namely the BioCreative V chemical disease relation corpus, traditional Chinese medicine literature corpus and i2b2 2012 temporal relation challenge corpus, show that the proposed approach achieves state-of-the-art results (giving a relative improvement of 22.2, 7.77, and 38.5% in F1 score, respectively, compared with a traditional 1d-CNN classifier). The source code is available at https://github.com/chentao1999/MedicalRelationExtraction.
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Affiliation(s)
- Tao Chen
- Department of Computer Science and Engineering, Faculty of Intelligent Manufacturing, Wuyi University, No.22, Dongcheng village, Pengjiang district, Jiangmen City, Guangdong Province, 529020, China
| | - Mingfen Wu
- Department of Computer Science and Engineering, Faculty of Intelligent Manufacturing, Wuyi University, No.22, Dongcheng village, Pengjiang district, Jiangmen City, Guangdong Province, 529020, China
| | - Hexi Li
- Department of Computer Science and Engineering, Faculty of Intelligent Manufacturing, Wuyi University, No.22, Dongcheng village, Pengjiang district, Jiangmen City, Guangdong Province, 529020, China
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20
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Zhou H, Liu Z, Ning S, Yang Y, Lang C, Lin Y, Ma K. Leveraging prior knowledge for protein-protein interaction extraction with memory network. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:5053999. [PMID: 30010731 PMCID: PMC6047414 DOI: 10.1093/database/bay071] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 06/14/2018] [Indexed: 11/14/2022]
Abstract
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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Affiliation(s)
- Huiwei Zhou
- School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No. 2 Linggong Road, Ganjingzi District, Dalian, Liaoning, China
| | - Zhuang Liu
- School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No. 2 Linggong Road, Ganjingzi District, Dalian, Liaoning, China
| | - Shixian Ning
- School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No. 2 Linggong Road, Ganjingzi District, Dalian, Liaoning, China
| | - Yunlong Yang
- School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No. 2 Linggong Road, Ganjingzi District, Dalian, Liaoning, China
| | - Chengkun Lang
- School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No. 2 Linggong Road, Ganjingzi District, Dalian, Liaoning, China
| | - Yingyu Lin
- School of Foreign Languages, Dalian University of Technology, Arts Building, No. 2 Linggong Road, Ganjingzi District, Dalian, Liaoning, China
| | - Kun Ma
- School of Life Science and Medicine, Dalian University of Technology, F03 Building, No. 2 Dagong Road, Liaodongwan District, Panjin, Liaoning, China
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21
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Onye SC, Akkeleş A, Dimililer N. relSCAN - A system for extracting chemical-induced disease relation from biomedical literature. J Biomed Inform 2018; 87:79-87. [PMID: 30296491 DOI: 10.1016/j.jbi.2018.09.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 09/17/2018] [Accepted: 09/30/2018] [Indexed: 11/20/2022]
Abstract
This paper proposes an effective and robust approach for Chemical-Induced Disease (CID) relation extraction from PubMed articles. The study was performed on the Chemical Disease Relation (CDR) task of BioCreative V track-3 corpus. The proposed system, named relSCAN, is an efficient CID relation extraction system with two phases to classify relation instances from the Co-occurrence and Non-Co-occurrence mention levels. We describe the case of chemical and disease mentions that occur in the same sentence as 'Co-occurrence', or as 'Non-Co-occurrence' otherwise. In the first phase, the relation instances are constructed on both mention levels. In the second phase, we employ a hybrid feature set to classify the relation instances at both of these mention levels using the combination of two Machine Learning (ML) classifiers (Support Vector Machine (SVM) and J48 Decision tree). This system is entirely corpus dependent and does not rely on information from external resources in order to boost its performance. We achieved good results, which are comparable with the other state-of-the-art CID relation extraction systems on the BioCreative V corpus. Furthermore, our system achieves the best performance on the Non-Co-occurrence mention level.
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Affiliation(s)
- Stanley Chika Onye
- Department of Applied Mathematics and Computer Science, Faculty of Arts & Sciences, Eastern Mediterranean University, Famagusta, North Cyprus via Mersin 10, Turkey.
| | - Arif Akkeleş
- Department of Mathematics, Faculty of Arts & Sciences, Eastern Mediterranean University, Famagusta, North Cyprus via Mersin 10, Turkey
| | - Nazife Dimililer
- Department of Information Technology, School of Computing and Technology, Eastern Mediterranean University, Famagusta, North Cyprus via Mersin 10, Turkey
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22
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Zheng W, Lin H, Liu X, Xu B. A document level neural model integrated domain knowledge for chemical-induced disease relations. BMC Bioinformatics 2018; 19:328. [PMID: 30223767 PMCID: PMC6142695 DOI: 10.1186/s12859-018-2316-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2018] [Accepted: 08/14/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The effective combination of texts and knowledge may improve performances of natural language processing tasks. For the recognition of chemical-induced disease (CID) relations which may span sentence boundaries in an article, although existing CID systems explored the utilization for knowledge bases, the effects of different knowledge on the identification of a special CID haven't been distinguished by these systems. Moreover, systems based on neural network only constructed sentence or mention level models. RESULTS In this work, we proposed an effective document level neural model integrated domain knowledge to extract CID relations from biomedical articles. Basic semantic information of an article with respect to a special CID candidate pair was learned from the document level sub-network module. Furthermore, knowledge attention depending on the representation of the article was proposed to distinguish the influences of different knowledge on the special CID pair and then the final representation of knowledge was formed by aggregating weighed knowledge. Finally, the integrated representations of texts and knowledge were passed to a softmax classifier to perform the CID recognition. Experimental results on the chemical-disease relation corpus proposed by BioCreative V show that our proposed system integrated knowledge achieves a good overall performance compared with other state-of-the-art systems. CONCLUSIONS Experimental analyses demonstrate that the introduced attention mechanism on domain knowledge plays a significant role in distinguishing influences of different knowledge on the judgment for a special CID relation.
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Affiliation(s)
- Wei Zheng
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China.,College of Software, Dalian JiaoTong University, Dalian, China
| | - Hongfei Lin
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China.
| | - Xiaoxia Liu
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Bo Xu
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China.
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23
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Chemical-induced disease relation extraction with dependency information and prior knowledge. J Biomed Inform 2018; 84:171-178. [DOI: 10.1016/j.jbi.2018.07.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 07/09/2018] [Accepted: 07/11/2018] [Indexed: 11/18/2022]
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24
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Bhasuran B, Natarajan J. Automatic extraction of gene-disease associations from literature using joint ensemble learning. PLoS One 2018; 13:e0200699. [PMID: 30048465 PMCID: PMC6061985 DOI: 10.1371/journal.pone.0200699] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 07/02/2018] [Indexed: 12/26/2022] Open
Abstract
A wealth of knowledge concerning relations between genes and its associated diseases is present in biomedical literature. Mining these biological associations from literature can provide immense support to research ranging from drug-targetable pathways to biomarker discovery. However, time and cost of manual curation heavily slows it down. In this current scenario one of the crucial technologies is biomedical text mining, and relation extraction shows the promising result to explore the research of genes associated with diseases. By developing automatic extraction of gene-disease associations from the literature using joint ensemble learning we addressed this problem from a text mining perspective. In the proposed work, we employ a supervised machine learning approach in which a rich feature set covering conceptual, syntax and semantic properties jointly learned with word embedding are trained using ensemble support vector machine for extracting gene-disease relations from four gold standard corpora. Upon evaluating the machine learning approach shows promised results of 85.34%, 83.93%,87.39% and 85.57% of F-measure on EUADR, GAD, CoMAGC and PolySearch corpora respectively. We strongly believe that the presented novel approach combining rich syntax and semantic feature set with domain-specific word embedding through ensemble support vector machines evaluated on four gold standard corpora can act as a new baseline for future works in gene-disease relation extraction from literature.
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Affiliation(s)
- Balu Bhasuran
- DRDO-BU Center for Life Sciences, Bharathiar University Campus, Coimbatore, Tamilnadu, India
| | - Jeyakumar Natarajan
- DRDO-BU Center for Life Sciences, Bharathiar University Campus, Coimbatore, Tamilnadu, India
- Data mining and Text mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamilnadu, India
- * E-mail:
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25
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Zheng W, Lin H, Li Z, Liu X, Li Z, Xu B, Zhang Y, Yang Z, Wang J. An effective neural model extracting document level chemical-induced disease relations from biomedical literature. J Biomed Inform 2018; 83:1-9. [DOI: 10.1016/j.jbi.2018.05.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 03/14/2018] [Accepted: 05/04/2018] [Indexed: 01/06/2023]
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26
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Warikoo N, Chang YC, Hsu WL. LPTK: a linguistic pattern-aware dependency tree kernel approach for the BioCreative VI CHEMPROT task. Database (Oxford) 2018; 2018:5139652. [PMID: 30346607 PMCID: PMC6196310 DOI: 10.1093/database/bay108] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 08/30/2018] [Accepted: 09/24/2018] [Indexed: 11/14/2022]
Abstract
Identifying the interactions between chemical compounds and genes from biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this paper, we describe Linguistic Pattern-Aware Dependency Tree Kernel, a linguistic interaction pattern learning method developed for CHEMPROT task-BioCreative VI, to capture chemical-protein interaction (CPI) patterns within biomedical literatures. We also introduce a framework to integrate these linguistic patterns with smooth partial tree kernel to extract the CPIs. This new method of feature representation models aspects of linguistic probability in geometric representation, which not only optimizes the sufficiency of feature dimension for classification, but also defines features as interpretable contexts rather than long vectors of numbers. In order to test the robustness and efficiency of our system in identifying different kinds of biological interactions, we evaluated our framework on three separate data sets, i.e. CHEMPROT corpus, Chemical-Disease Relation corpus and Protein-Protein Interaction corpus. Corresponding experiment results demonstrate that our method is effective and outperforms several compared systems for each data set.
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Affiliation(s)
- Neha Warikoo
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Yung-Chun Chang
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
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27
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Liu S, Shen F, Komandur Elayavilli R, Wang Y, Rastegar-Mojarad M, Chaudhary V, Liu H. Extracting chemical-protein relations using attention-based neural networks. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:5122756. [PMID: 30295724 PMCID: PMC6174551 DOI: 10.1093/database/bay102] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 09/10/2018] [Indexed: 11/14/2022]
Abstract
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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Affiliation(s)
- Sijia Liu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.,Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA
| | - Feichen Shen
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Yanshan Wang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Majid Rastegar-Mojarad
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.,University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Vipin Chaudhary
- Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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Gu J, Sun F, Qian L, Zhou G. Chemical-induced disease relation extraction via convolutional neural network. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2017; 2017:3098440. [PMID: 28415073 PMCID: PMC5467558 DOI: 10.1093/database/bax024] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 03/01/2017] [Indexed: 01/08/2023]
Abstract
This article describes our work on the BioCreative-V chemical–disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively. In our work, relation extraction between entity concepts in documents was simplified to relation extraction between entity mentions. We first constructed pairs of chemical and disease mentions as relation instances for training and testing stages, then we trained and applied the ME model and the convolutional neural network model for inter- and intra-sentence level, respectively. Finally, we merged the classification results from mention level to document level to acquire the final relations between chemical and disease concepts. The evaluation on the BioCreative-V CDR corpus shows the effectiveness of our proposed approach. Database URL:http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/
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Affiliation(s)
- Jinghang Gu
- School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China
| | - Fuqing Sun
- Department of Gynecology Minimally Invasive Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, 17 Qihelou Street, Beijing, China
| | - Longhua Qian
- School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China
| | - Guodong Zhou
- School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China
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