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Article ; Online: A metric learning-based method for biomedical entity linking.

Le, Ngoc D / Nguyen, Nhung T H

Frontiers in research metrics and analytics

2023 Volume 8, Page(s) 1247094

Abstract: Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept ... ...

Abstract	Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept or
Language	English
Publishing date	2023-12-19
Publishing country	Switzerland
Document type	Journal Article
ISSN	2504-0537
ISSN (online)	2504-0537
DOI	10.3389/frma.2023.1247094
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: GENA: A knowledge graph for nutrition and mental health.

Dang, Linh D / Phan, Uyen T P / Nguyen, Nhung T H

Journal of biomedical informatics

2023 Volume 145, Page(s) 104460

Abstract: While a large number of knowledge graphs have previously been developed by automatically extracting and structuring knowledge from literature, there is currently no such knowledge graph that encodes relationships between food, biochemicals and mental ... ...

Abstract	While a large number of knowledge graphs have previously been developed by automatically extracting and structuring knowledge from literature, there is currently no such knowledge graph that encodes relationships between food, biochemicals and mental illnesses, even though a large amount of knowledge about these relationships is available in the form of unstructured text in biomedical literature articles. To address this limitation, this article describes the development of GENA - (Graph of mEntal-health and Nutrition Association), a knowledge graph that represents relations between nutrition and mental health, extracted from biomedical abstracts. GENA is constructed from PubMed abstracts that contain keywords relating to chemicals, food, and health. A hybrid named entity recognition (NER) model is firstly applied to these abstracts to identify various entities of interest. Subsequently, a deep syntax-based relation extraction model is used to detect binary relations between the identified entities. Finally, the resulting relations are used to populate the GENA knowledge graph, whose relationships can be accessed in an intuitive and interpretable manner using the Neo4J Database Management System. To evaluate the reliability of GENA, two annotators manually assessed a subset of the extracted relations. The evaluation results show that our methods obtain high precision for the NER task and acceptable precision and relative recall for the relation extraction task. GENA consists of 43,367 relationships that encode information about nutrition and health, of which 94.04% are new relations that are not present in existing ontologies of food and diseases. GENA is constructed based on scientific principles, and has the potential to be used within further applications to contribute towards scientific research within the domain. It is a pioneering knowledge graph in nutrition and mental health, containing a diverse range of relationship types. All of our source code and results are publicly available at https://github.com/ddlinh/gena-db.
MeSH term(s)	Mental Health ; Pattern Recognition, Automated ; Reproducibility of Results ; Software ; PubMed
Language	English
Publishing date	2023-08-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2023.104460
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Book ; Online: Span-based Named Entity Recognition by Generating and Compressing Information

Nguyen, Nhung T. H. / Miwa, Makoto / Ananiadou, Sophia

2023

Abstract: The information bottleneck (IB) principle has been proven effective in various NLP applications. The existing work, however, only used either generative or information compression models to improve the performance of the target task. In this paper, we ... ...

Abstract	The information bottleneck (IB) principle has been proven effective in various NLP applications. The existing work, however, only used either generative or information compression models to improve the performance of the target task. In this paper, we propose to combine the two types of IB models into one system to enhance Named Entity Recognition (NER). For one type of IB model, we incorporate two unsupervised generative components, span reconstruction and synonym generation, into a span-based NER system. The span reconstruction ensures that the contextualised span representation keeps the span information, while the synonym generation makes synonyms have similar representations even in different contexts. For the other type of IB model, we add a supervised IB layer that performs information compression into the system to preserve useful features for NER in the resulting span representations. Experiments on five different corpora indicate that jointly training both generative and information compression models can enhance the performance of the baseline span-based NER system. Our source code is publicly available at https://github.com/nguyennth/joint-ib-models. Comment: The paper has 13 pages but the main content is in 9 pages. There are two figures and 9 tables. The paper is accepted as a long paper at EACL 2023
Keywords	Computer Science - Computation and Language ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-02-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Contextualized medication event extraction with levitated markers.

Vasilakes, Jake / Georgiadis, Panagiotis / Nguyen, Nhung T H / Miwa, Makoto / Ananiadou, Sophia

Journal of biomedical informatics

2023 Volume 141, Page(s) 104347

Abstract: Automatic extraction of patient medication histories from free-text clinical notes can increase the amount of relevant information to clinicians for developing treatment plans. In addition to detecting medication events, clinical text mining systems must ...

Abstract	Automatic extraction of patient medication histories from free-text clinical notes can increase the amount of relevant information to clinicians for developing treatment plans. In addition to detecting medication events, clinical text mining systems must also be able to predict event context, such as negation, uncertainty, and time of occurrence, in order to construct accurate patient timelines. Towards this goal, we introduce Levitated Context Markers (LCMs), a novel transformer-based model for contextualized event extraction. LCMs are an adaptation of levitated markers -originally developed for relation extraction- that allow pretrained transformer models to utilize global input representations while also focusing on event-related subspans using a sparse attention mechanism. In addition to outperforming a strong baseline model on the Contextualized Medication Event Dataset, we show that LCMs' sparse attention can provide interpretable predictions by detecting relevant context cues in an unsupervised manner.
MeSH term(s)	Humans ; Data Mining ; Records ; Natural Language Processing
Language	English
Publishing date	2023-04-06
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2023.104347
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Article: COPIOUS: A gold standard corpus of named entities towards extracting species occurrence from biodiversity literature.

Nguyen, Nhung T H / Gabud, Roselyn S / Ananiadou, Sophia

Biodiversity data journal

2019 , Issue 7, Page(s) e29626

Abstract: ... ...

Abstract	Background
Language	English
Publishing date	2019-01-22
Publishing country	Bulgaria
Document type	Journal Article
ZDB-ID	2736709-5
ISSN	1314-2828
ISSN	1314-2828
DOI	10.3897/BDJ.7.e29626
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A term-based and citation network-based search system for COVID-19.

Zerva, Chrysoula / Taylor, Samuel / Soto, Axel J / Nguyen, Nhung T H / Ananiadou, Sophia

JAMIA open

2021 Volume 4, Issue 4, Page(s) ooab104

Abstract: The COVID-19 pandemic resulted in an unprecedented production of scientific literature spanning several fields. To facilitate navigation of the scientific literature related to various aspects of the pandemic, we developed an exploratory search system. ... ...

Abstract	The COVID-19 pandemic resulted in an unprecedented production of scientific literature spanning several fields. To facilitate navigation of the scientific literature related to various aspects of the pandemic, we developed an exploratory search system. The system is based on automatically identified technical terms, document citations, and their visualization, accelerating identification of relevant documents. It offers a multi-view interactive search and navigation interface, bringing together unsupervised approaches of term extraction and citation analysis. We conducted a user evaluation with domain experts, including epidemiologists, biochemists, medicinal chemists, and medicine students. In general, most users were satisfied with the relevance and speed of the search results. More interestingly, participants mostly agreed on the capacity of the system to enable exploration and discovery of the search space using the graph visualization and filters. The system is updated on a weekly basis and it is publicly available at http://www.nactem.ac.uk/cord/.
Language	English
Publishing date	2021-12-14
Publishing country	United States
Document type	Journal Article
ISSN	2574-2531
ISSN (online)	2574-2531
DOI	10.1093/jamiaopen/ooab104
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Article ; Online: An ensemble of neural models for nested adverse drug events and medication extraction with subwords.

Ju, Meizhi / Nguyen, Nhung T H / Miwa, Makoto / Ananiadou, Sophia

Journal of the American Medical Informatics Association : JAMIA

2019 Volume 27, Issue 1, Page(s) 22–30

Abstract: Objective: This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2.: Materials and methods: We designed a ... ...

Abstract	Objective: This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2. Materials and methods: We designed a neural model to tackle both nested (entities embedded in other entities) and polysemous entities (entities annotated with multiple semantic types) based on MIMIC III discharge summaries. To better represent rare and unknown words in entities, we further tokenized the MIMIC III data set by splitting the words into finer-grained subwords. We finally combined all the models to boost the performance. Additionally, we implemented a featured-based conditional random field model and created an ensemble to combine its predictions with those of the neural model. Results: Our method achieved 92.78% lenient micro F1-score, with 95.99% lenient precision, and 89.79% lenient recall, respectively. Experimental results showed that combining the predictions of either multiple models, or of a single model with different settings can improve performance. Discussion: Analysis of the development set showed that our neural models can detect more informative text regions than feature-based conditional random field models. Furthermore, most entity types significantly benefit from subword representation, which also allows us to extract sparse entities, especially nested entities. Conclusion: The overall results have demonstrated that the ensemble method can accurately recognize entities, including nested and polysemous entities. Additionally, our method can recognize sparse entities by reconsidering the clinical narratives at a finer-grained subword level, rather than at the word level.
MeSH term(s)	Drug-Related Side Effects and Adverse Reactions ; Electronic Health Records ; Humans ; Information Storage and Retrieval/methods ; Narration ; Natural Language Processing ; Neural Networks, Computer
Language	English
Publishing date	2019-06-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocz075
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Zs.A 4128: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article: Maximising resilience to sea-level rise in urban coastal ecosystems through systematic conservation planning

Nguyen, Nhung T.H. / Friess, Daniel A. / Todd, Peter A. / Mazor, Tessa / Lovelock, Catherine E. / Lowe, Ryan / Gilmour, James / Ming Chou, Loke / Bhatia, Natasha / Jaafar, Zeehan / Tun, Karenne / Yaakub, Siti Maryam / Huang, Danwei

Landscape and urban planning. 2022 May, v. 221

2022

Abstract: Coastal cities and their natural environments are vulnerable to the impacts of climate change, especially sea-level rise (SLR). Hard coastal defences play a key role in protecting at-risk urban coastal populations from flooding and erosion, but coastal ... ...

Abstract	Coastal cities and their natural environments are vulnerable to the impacts of climate change, especially sea-level rise (SLR). Hard coastal defences play a key role in protecting at-risk urban coastal populations from flooding and erosion, but coastal ecosystems also play important roles in the overall sustainability and resilience of cities and urban centres by contributing to coastal protection. Conserving coastal ecosystems and maximising their resilience will ensure that urban coastal communities can continue to benefit from ecosystem services and improve their adaptive capacity to cope with adverse impacts in the future. Using the hyper-urbanised coast of Singapore as a case study, we modelled the resilience of coastal wetlands to SLR and integrated resilience in conservation planning. We found that the responses of coastal habitats to rising sea level vary across the modelling periods. While there is a slight net gain in the extent of mangrove forests and tidal flats by the end of the century due to potential habitat conversion, the existing habitats will experience a loss in coverage. Highly modified coastlines associated with urbanisation impede the ability of existing wetlands to migrate landward, which is a key mechanism for coastal habitats to cope with rising sea levels. Systematic conservation planning can identify sites that are potentially resilient to SLR and incorporate factors that influence an ecosystem’s capability to respond to change. Crucially, the relatively slow rates of SLR and persistence of coastal wetlands during the earlier half of this century present an opportunity to introduce management interventions aimed at enhancing ecosystem resilience.
Keywords	Singapore ; case studies ; climate change ; coasts ; ecological resilience ; habitats ; landscapes ; sea level ; urbanization
Language	English
Dates of publication	2022-05
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	742504-1
ISSN	1872-6062 ; 0169-2046
ISSN (online)	1872-6062
ISSN	0169-2046
DOI	10.1016/j.landurbplan.2022.104374
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Multilayered regulations of RIG-I in the anti-viral signaling pathway.

Kim, Nari / Now, Hesung / Nguyen, Nhung T H / Yoo, Joo-Yeon

Journal of microbiology (Seoul, Korea)

2016 Volume 54, Issue 9, Page(s) 583–587

Abstract: RIG-I is a cytosolic receptor recognizing virus-specific RNA structures and initiates an antiviral signaling that induces the production of interferons and proinflammatory cytokines. Because inappropriate RIG-I signaling affects either viral clearance or ...

Abstract	RIG-I is a cytosolic receptor recognizing virus-specific RNA structures and initiates an antiviral signaling that induces the production of interferons and proinflammatory cytokines. Because inappropriate RIG-I signaling affects either viral clearance or immune toxicity, multiple regulations of RIG-I have been investigated since its discovery as the viral RNA detector. In this review, we describe the recent progress in research on the regulation of RIG-I activity or abundance. Specifically, we focus on the mechanism that modulates RIG-I-dependent antiviral response through post-translational modifications of or protein-protein interactions with RIG-I.
MeSH term(s)	Animals ; DEAD Box Protein 58/genetics ; DEAD Box Protein 58/immunology ; Gene Expression Regulation ; Host-Pathogen Interactions ; Humans ; Protein Binding ; Receptors, Immunologic ; Signal Transduction ; Virus Diseases/genetics ; Virus Diseases/immunology ; Virus Diseases/virology ; Viruses/genetics ; Viruses/immunology
Chemical Substances	Receptors, Immunologic ; RIGI protein, human (EC 3.6.1.-) ; DEAD Box Protein 58 (EC 3.6.4.13)
Language	English
Publishing date	2016-08-31
Publishing country	Korea (South)
Document type	Journal Article ; Review
ZDB-ID	2012399-1
ISSN	1976-3794 ; 1225-8873
ISSN (online)	1976-3794
ISSN	1225-8873
DOI	10.1007/s12275-016-6322-2
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ab Jg. 2022: Lesesaal (EG)

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Article: Large conservation opportunities exist in >90% of tropic-subtropic coastal habitats adjacent to cities

Mazor, Tessa / Friess, Daniel A. / Todd, Peter A. / Huang, Danwei / Nguyen, Nhung T.H. / Saunders, Megan I. / Runting, Rebecca K. / Lowe, Ryan J. / Cartwright, Paula / Gilmour, James P. / Lovelock, Catherine E.

Elsevier Inc. One earth. 2021 July 23, v. 4, no. 7

2021

Abstract: Coastal habitats have faced decades of loss caused by urbanization. Global recognition of the ecosystem services that coastal habitats provide has led to an emphasis on cities to adopt nature-based solutions (NBS). However, a broad assessment of urban ... ...

Abstract	Coastal habitats have faced decades of loss caused by urbanization. Global recognition of the ecosystem services that coastal habitats provide has led to an emphasis on cities to adopt nature-based solutions (NBS). However, a broad assessment of urban areas and their potential to conserve remaining coastal habitat has not been undertaken. Here we apply spatial analytics to investigate 5,096 coastal urban areas in tropical and subtropical regions within the distribution of mangroves, tidal flats, seagrass meadows, and coral reefs, and find <50% of urban areas have natural coastal habitats within their extent. Large conservation opportunities for urban areas exist within an adjacent 50 km buffer zone where a significant proportion (93%) of urban-influenced coastal habitat lies and where 26% is currently protected. Potential high-conservation areas across the globe provide a unique opportunity to increase the resilience of urbanizing coasts and NBS for long-term socioeconomic and conservation goals.
Keywords	corals ; ecosystems ; urbanization
Language	English
Dates of publication	2021-0723
Size	p. 1004-1015.
Publishing place	Elsevier Inc.
Document type	Article
ISSN	2590-3322
DOI	10.1016/j.oneear.2021.06.010
Database	NAL-Catalogue (AGRICOLA)

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