Adv Health Sci Educ Theory Pract. 2025 Jun 25. doi: 10.1007/s10459-025-10426-7. Online ahead of print.

ABSTRACT

Clinical reasoning skills develop through increased knowledge acquisition, greater clinical experience, and continued practice over time. Yet, across undergraduate and graduate medical education, it is inconsistently taught. As progressive clinical reasoning curricula emerge, research is needed to help inform the content and activities appropriate for different learner levels. While much is understood about the clinical reasoning skills of novices and experts, less has been theorized about students in between those two extremes. Our study explores the clinical reasoning skills of medical students in their final year of medical school, informed by clinical reasoning models and information processing theories. We conducted think-aloud interviews with 18 4th-year medical students tasked with completing a novel web-based assessment. Students reviewed simulated patient charts, answered clinically relevant questions, and justified their thinking and responses. Using a qualitative data collection and analysis framework, we coded interviews for clinical reasoning elements and emergent themes. Our findings present an initial framework for understanding the clinical reasoning skills of 4th-year medical students. The framework includes four high-level skills that we defined as interpreting, framing, generating, and justifying. These skills reflect elements of nonanalytic and analytic thinking in that students used semantic qualifiers, partially activated illness scripts, and engaged in aspects of hypothetical-deductive reasoning. Our framework can help shape how best to structure clinical reasoning instruction in medical education across the novice-to-expert continuum, as well as aid in the development of clinical reasoning theories that incorporate a range of learner levels.

PMID:40560425 | DOI:10.1007/s10459-025-10426-7

Neural Comput Appl. 2025;37(18):11737-11753. doi: 10.1007/s00521-024-10674-5. Epub 2024 Dec 14.

ABSTRACT

The research in Materials Science and Engineering focuses on the design, synthesis, properties, and performance of materials. An important class of materials that is widely investigated are crystalline materials, including metals and semiconductors. Crystalline material typically contains a specific type of defect called "dislocation". This defect significantly affects various material properties, including bending strength, fracture toughness, and ductility. Researchers have devoted a significant effort in recent years to understanding dislocation behaviour through experimental characterization techniques and simulations, e.g., dislocation dynamics simulations. This paper presents how data from dislocation dynamics simulations can be modelled using semantic web technologies through annotating data with ontologies. We extend the dislocation ontology by adding missing concepts and aligning it with two other domain-related ontologies (i.e., the Elementary Multi-perspective Material Ontology and the Materials Design Ontology), allowing for efficiently representing the dislocation simulation data. Moreover, we present a real-world use case for representing the discrete dislocation dynamics data as a knowledge graph (DisLocKG) which can depict the relationship between them. We also developed a SPARQL endpoint that brings extensive flexibility for querying DisLocKG.

PMID:40538984 | PMC:PMC12174205 | DOI:10.1007/s00521-024-10674-5

Res Integr Peer Rev. 2025 Jun 18;10(1):10. doi: 10.1186/s41073-025-00166-y.

ABSTRACT

BACKGROUND: Although commonly used to evaluate health interventions, cluster randomized trials raise difficult ethical issues. Recognizing this, the Ottawa Statement on the Ethical Design and Conduct of Cluster Randomized Trials, published in 2012, provides 15 recommendations to address ethical issues across seven domains. But due to several developments in the design and implementation of cluster randomized trials, there are new issues requiring guidance. To inform the forthcoming update of the Ottawa Statement, we aimed to identify any gaps in the Ottawa Statement discussed within the literature.

METHODS: We searched Google Scholar, Scopus, and Web of Science using the 'cited by' function on 11 November 2022.We included all types of publications, including articles, book chapters, commentaries, editorials, ethics guidelines, theses and trial-related publications (i.e., primary reports, protocols, and secondary analyses), that cited and engaged with the Ottawa Statement, the Ottawa Statement précis, or one or more of its four background papers. Data were extracted by four reviewers working in rotating pairs. Reviewers captured relevant text verbatim and recorded whether it reflected a gap relating to one or more of the Ottawa Statement domains. Using a thematic analysis approach, semantic coding was used to summarize the content of the data into distinct gaps within the Ottawa Statement domains, which was subsequently expanded in an inductive manner through discussion.

RESULTS: The qualitative analysis of the text from 53 articles resulted in the identification of 24 distinct gaps in the Ottawa Statement: 4 gaps about justifying the cluster randomized design; 2 gaps about research ethics committee review; 3 gaps about identifying research participants; 4 gaps about obtaining informed consent; 3 gaps about gatekepeers; 6 gaps about assessing benefits and harms; 1 gap about protecting vulnerable participants; and 1 gap about equity-related issues in cluster randomized trials.

CONCLUSION: Identifying 24 gaps reveals a need to update the Ottawa Statement. Alongside additional gaps identified in ongoing empirical work and through engagement with our patient and public partners, the gaps identified through this citation analysis should be considered in the forthcoming Ottawa Statement update.

PMID:40528254 | DOI:10.1186/s41073-025-00166-y

AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:46-55. eCollection 2025.

ABSTRACT

We developed a simulated process to show a software implementation to facilitate an approach to integrate the Informed Consent Ontology, a reference ontology of informed consent information, to express implicit description and implement conceptual permission from informed consent life cycle. An early study introduced an experimental method to use Semantic Web Rule Language (SWRL) to describe and represent permissions to computational deduce more information from the Informed Consent Ontology (ICO), demonstrated by the use of the All of Us informed consent documents. We show how incomplete information in informed consent documents can be elucidated using a computational model of permissions toward health information technology that integrates ontologies. Future goals entail applying our computational approach for specific sub-domains of the informed consent life cycle, specifically for vaccine informed consent.

PMID:40502263 | PMC:PMC12150727

AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:300-311. eCollection 2025.

ABSTRACT

Rare diseases affect approximately 1 in 11 Americans, yet their diagnosis remains challenging due to limited clinical evidence, low awareness, and lack of definitive treatments. Our project aims to accelerate rare disease diagnosis by developing a comprehensive informatics framework leveraging data mining, semantic web technologies, deep learning, and graph-based embedding techniques. However, our on-premises computational infrastructure faces significant challenges in scalability, maintenance, and collaboration. This study focuses on developing and evaluating a cloud-based computing infrastructure to address these challenges. By migrating to a scalable, secure, and collaborative cloud environment, we aim to enhance data integration, support advanced predictive modeling for differential diagnoses, and facilitate widespread dissemination of research findings to stakeholders, the research community, and the public and also proposed a facilitated through a reliable, standardized workflow designed to ensure minimal disruption and maintain data integrity for existing research project.

PMID:40502250 | PMC:PMC12150693

Data Brief. 2025 May 14;60:111648. doi: 10.1016/j.dib.2025.111648. eCollection 2025 Jun.

ABSTRACT

Scene Text Recognition (STR) has advanced significantly in recent years, yet languages utilizing Arabic-based scripts, such as Kurdish, remain underrepresented in existing datasets. This paper introduces KSTRV1, the first large-scale dataset designed for Kurdish Scene Text Recognition (KSTR), addressing the lack of resources for non-Latin scripts. The dataset comprises 1,420 natural scene images and 19,872 cropped word samples, covering Kurdish (Sorani and Badini dialects), Arabic, and English. Additionally, 20,000 synthetic text instances have been generated to enhance the dataset's diversity, quantity, and quality by incorporating varied fonts, orientations, distortions, and background complexities. KSTRV1 captures the multilingual landscape of the Kurdistan Region while addressing real-world challenges like occlusion, lighting variations, and script complexity. The dataset includes detailed annotations with bounding boxes, language identification, and text orientation labels, ensuring comprehensive support for training and evaluating STR models. By providing both natural and synthetic data, KSTRV1 enables the development of robust text recognition models, particularly for Central Kurdish, a low-resource language. The KSTRV1 dataset is publicly available at https://doi.org/10.5281/zenodo.15038953 and is expected to significantly contribute to research in multilingual STR, document analysis, and optical character recognition (OCR), facilitating more inclusive and accurate text recognition systems.

PMID:40496736 | PMC:PMC12151206 | DOI:10.1016/j.dib.2025.111648

BMC Med Inform Decis Mak. 2025 Jun 10;25(Suppl 1):211. doi: 10.1186/s12911-025-03022-7.

ABSTRACT

BACKGROUND: Use of the FAIR principles (Findable, Accessible, Interoperable and Reusable) allows the rapidly growing number of biomedical datasets to be optimally (re)used. An important aspect of the FAIR principles is metadata. The FAIR Data Point specifications and reference implementation have been designed as an example on how to publish metadata according to the FAIR principles. Metadata can be added to a FAIR Data Point with the FDP's web interface or through its API. However, these methods are either limited in scalability or only usable by users with a background in programming. We aim to provide a new tool for populating FDPs with metadata that addresses these limitations with the FAIR Data Point Populator.

RESULTS: The FAIR Data Point Populator consists of a GitHub workflow together with Excel templates that have tooltips, validation and documentation. The Excel templates are targeted towards non-technical users, and can be used collaboratively in online spreadsheet software. A more technical user then uses the GitHub workflow to read multiple entries in the Excel sheets, and transform it into machine readable metadata. This metadata is then automatically uploaded to a connected FAIR Data Point. We applied the FAIR Data Point Populator on the metadata of two datasets, and a patient registry. We were then able to run a query on the FAIR Data Point Index, in order to retrieve one of the datasets.

CONCLUSION: The FAIR Data Point Populator addresses the limitations of the other metadata publication methods by allowing the bulk creation of metadata entries while remaining accessible for users without a background in programming. Additionally, it allows efficient collaboration. As a result of this, the barrier of entry for FAIRification is lower, which allows the creation of FAIR data by more people.

PMID:40495132 | DOI:10.1186/s12911-025-03022-7

Int J Cardiol. 2025 Jun 5:133483. doi: 10.1016/j.ijcard.2025.133483. Online ahead of print.

ABSTRACT

BACKGROUND: Takotsubo cardiomyopathy (TTC) is a stress-induced condition with limited evidence-based treatment options. Beta-blockers are commonly used, yet their efficacy remains uncertain. This meta-analysis evaluates the impact of beta-blocker therapy on mortality and recurrence in TTC patients.

METHODS: We systematically searched PubMed, EMBASE, Cochrane Library, Web of Science, Google Scholar, and Semantic Scholar, alongside trial registries and grey literature, for studies from inception to March 2025. Included studies examined adult TTC patients treated with beta-blockers versus controls, reporting all-cause mortality and recurrence. Odds ratios (ORs) with 95 % confidence intervals (CIs) were pooled using a random-effects model. Heterogeneity was assessed with I2 statistics, and publication bias was evaluated via funnel plots. Subgroup analyses stratified studies by design (retrospective, prospective, mixed) to assess methodological heterogeneity. A meta-regression explored ejection fraction (EF) as a moderator of mortality outcomes.

RESULTS: Nineteen studies (n = 11,167 patients, predominantly female, mean age 59-74 years) were included. Beta-blocker therapy significantly reduced all-cause mortality by 28 % (OR 0.72, 95 % CI: 0.62-0.84, p < 0.001; I2 = 30 %) with consistent effects across study designs (between-subgroup heterogeneity p = 0.86). Subgroup analyses showed a non-significant 21 % reduction in 1-year mortality (OR 0.79, 95 % CI: 0.54-1.16, p = 0.23; I2 = 52 %) and a significant 29 % reduction in 2-5-year mortality (OR 0.71, 95 % CI: 0.61-0.82, p < 0.001; I2 = 7 %). Recurrence decreased by 29 % overall (OR 0.71, 95 % CI: 0.52-0.97, p = 0.03; I2 = 57 %), with significant protective effects in mixed (OR 0.595) and retrospective (OR 0.485) studies but not prospective studies (OR 0.842), demonstrating significant between-subgroup heterogeneity (p = 0.01). Meta-regression showed no significant moderation of mortality by EF (p = 0.64), suggesting consistent benefits across cardiac function levels.

CONCLUSIONS: Beta-blockers significantly reduce long-term mortality and recurrence in TTC. While mortality benefits are consistent across study designs, recurrence outcomes show methodological sensitivity, with stronger evidence from mixed and retrospective studies. Benefits are more pronounced with sustained therapy, with no variation by EF. These findings support beta-blocker use in long-term TTC management, though randomized trials are needed to confirm causality and optimize protocols.

PMID:40482835 | DOI:10.1016/j.ijcard.2025.133483

Front Neurosci. 2025 May 21;19:1591410. doi: 10.3389/fnins.2025.1591410. eCollection 2025.

ABSTRACT

BACKGROUNDS: This study innovatively enhances personalized emotional responses and user experience quality in traditional Chinese folding armchair (Jiaoyi chair) design through an interdisciplinary methodology.

GOAL: To systematically extract user emotional characteristics, we developed a hybrid research framework integrating web-behavior data mining.

METHODS: 1) the KJ method combined with semantic crawlers extracts emotional descriptors from multi-source social data; 2) expert evaluation and fuzzy comprehensive assessment reduce feature dimensionality; 3) random forest and K-prototype clustering identify three core emotional preference factors: "Flexible Refinement," "Uncompromising Quality," and "ergonomic stability."

DISCUSSION: A CNN-GRU-Attention hybrid deep learning model was constructed, incorporating dynamic convolutional kernels and gated residual connections to address feature degradation in long-term semantic sequences. Experimental validation demonstrated the superior performance of our model in three chair design preference prediction tasks (RMSE = 0.038953, 0.066123, 0.0069777), outperforming benchmarks (CNN, SVM, LSTM). Based on the top-ranked preference encoding, we designed a new Jiaoyi chair prototype, achieving significantly reduced prediction errors in final user testing (RMSE = 0.0034127, 0.0026915, 0.0035955).

CONCLUSION: This research establishes a quantifiable intelligent design paradigm for modernizing cultural heritage through computational design.

PMID:40470295 | PMC:PMC12133947 | DOI:10.3389/fnins.2025.1591410

Arch Suicide Res. 2025 Jun 2:1-20. doi: 10.1080/13811118.2025.2509653. Online ahead of print.

ABSTRACT

OBJECTIVE: Suicide rates continue to rise, particularly among young adults, with college student-athletes representing a specific subgroup of concern. The aim of this systematic review was to clarify the individual and environmental risk factors for suicide specific to U.S. college student-athletes.

METHOD: Databases searched included the State University of New York (SUNY) libraries, Google Scholar, Web of Science, PsychINFO, Semantic Scholar, and PubMed. No date restrictions were applied, resulting in 112 articles and reports included in this review. Studies examining U.S. student-athletes participating in intercollegiate athletics within the context of suicide, including ideation, actions, or attempts, met the inclusion criteria for this thematic review. The PRISMA framework guided the literature selection and content review.

RESULTS: Risk factors included the convergence of academic and athletic pressure, toxic team culture, barriers to accessing services, complexities of the athlete identity, and experiences of injury.

CONCLUSION: Given these unique risk factors, approaches to suicide prevention, intervention, and postvention for U.S. college student-athletes should include mandated suicide training for college athletic department personnel, routine mental health screening for athletes, improved access to mental health services, and the implementation of collaborative multidisciplinary care.

PMID:40454444 | DOI:10.1080/13811118.2025.2509653

Discov Oncol. 2025 Jun 1;16(1):978. doi: 10.1007/s12672-025-02716-8.

ABSTRACT

BACKGROUND: Cancer poses a significant health threat, causing millions of deaths annually. Although chemotherapy-based comprehensive therapies are common, their low accuracy and severe side effects limit effectiveness. Metal-organic frameworks (MOFs), with their superior biocompatibility and stability, show great promise for drug delivery and cancer treatment. This study aims to explore the potential and developmental trajectories of MOFs in cancer research through a bibliometric analysis.

METHODS: The Web of Science Core Collection was searched for documents from its inception in 2009 to December 31, 2023. We analyzed and visualized document types, countries, institutions, authors, journals, references, and keywords using the Bibliometrix package, dplyr, sankeywheel, term extraction, and ggplot2. Additionally, the Latent Dirichlet Allocation (LDA) algorithm was employed for detailed semantic analysis, uncovering latent thematic distributions.

RESULTS: A total of 7106 authors from 1591 institutions across 45 countries contributed 1955 papers on MOFs in cancer research, published in 327 journals. China leads in research output and international collaboration, with the Chinese Academy of Sciences as the top institution. Lin Wenbin from the University of Chicago is the most influential author, and ACS Applied Materials & Interfaces is the most active journal. MOFs are predominantly studied for breast cancer, followed by lung and liver cancers. Drug delivery remains a focal point for future research.

CONCLUSIONS: This study provides a comprehensive overview of the research landscape on MOFs in cancer treatment, offering insights into key trends and future directions, particularly in drug delivery and disease-specific applications.

PMID:40450655 | DOI:10.1007/s12672-025-02716-8

J Cheminform. 2025 May 31;17(1):91. doi: 10.1186/s13321-025-01025-0.

ABSTRACT

Persistent air quality pollution poses a serious threat to human health, and is one of the action points that policy makers should monitor according to the Directive 2008/50/EC. While deploying a massive network of hyperlocal sensors could provide extensive monitoring, this approach cannot generate geospatial continuous data and present several challenges in terms of logistics. Thus, developing accurate and trustable expert systems based on chemistry transport models is a key strategy for environmental protection. However, chemistry transport models present an important lack of standardization, and the formats are not interoperable between different systems, which limits the use for different stakeholders. In this context, semantic technologies provide methods and standards for scientific data and make information readable for expert systems. Therefore, this paper proposes a novel methodology for an ontology driven transformation for CHIMERE simulations, a chemistry transport model, allowing to generate knowledge graphs representing air quality information. It enables the transformation of netCDF files into RDF triples for short term air quality forecasting. Concretely, we utilize the Semantic Web Integration Tool (SWIT) framework for mapping individuals using an ontology as a template. Then, a new ontology for CHIMERE has been defined in this work, reusing concepts for other standards in the state of the art. Our approach demonstrates that RDF files can be created from netCDF in a linear computational time, allowing the scalability for expert systems. In addition, the ontology complains with the OQuaRE quality metrics and can be extended in future extensions to be applied to other chemistry transport models. SCIENTIFIC CONTRIBUTIONS: Development of the first ontology for a chemistry transport model. FAIRification of physical models thanks to the generation of knowledge graphs from netCDF files. The ontology proposed is published in PURL ( https://purl.org/chimere-ontology ) and the knowledge graph generated for a 72-h simulation can be accessed in the following repository: https://doi.org/10.5281/zenodo.13981544 .

PMID:40450355 | DOI:10.1186/s13321-025-01025-0

Brain Sci. 2025 May 18;15(5):515. doi: 10.3390/brainsci15050515.

ABSTRACT

Background/Objectives: Autobiographical memory (AM) is typically viewed in terms of comprising episodic (EAM) and semantic (SAM) components. Despite the emergence of numerous meta-analyses, the literature on these constructs remains fragmented. We aimed to summarize neural activations and to discuss the relations between constructs based on theory and experimentation, while evaluating the consistency between literature sources and discussing the critical issues and challenges of current research. Methods: We conducted a scoping meta-review on AM, EAM, and SAM based on meta-analytic studies in five scientific databases (PubMed, Web of Science, Scopus, PsychInfo, and PsychArticles). No temporal or language limits were applied. Results: We included twelve meta-analyses on AM, EAM and SAM in healthy populations. The meta-analyses of AM and EAM actually investigated the same construct, leading to misinterpretation. The two available meta-analyses on SAM used two different operationalizations of the construct. Neural data about EAM were analyzed via mean rank classification, finding the most relevant areas in the posterior cingulate cortex, hippocampus, precuneus, temporo-parietal junction, angular gyrus, and medial prefrontal cortex. SAM was linked to the posterior and anterior cingulate cortexes, middle and inferior frontal gyri, thalamus, middle and superior temporal gyri, inferior frontal and fusiform gyri, and parahippocampal cortex. Conclusions: Variability in reported activation patterns persists, reflecting differences in methodology and assumptions. We propose the homogenization the notations of EAM and AM based on experimental practice. In this notation, AM does not have a separate experimental task nor activation pattern and may not indicate a separate construct but an array of its components.

PMID:40426686 | DOI:10.3390/brainsci15050515

Entropy (Basel). 2025 Apr 27;27(5):473. doi: 10.3390/e27050473.

ABSTRACT

In this study, we propose a computational approach that applies text mining and deep learning to conduct controversy detection on social media platforms. Unlike previous research, our method integrates multidimensional and heterogeneous information from social media into a heterogeneous signed attributed network, encompassing various users' attributes, semantic information, and structural heterogeneity. We introduce a deep dual-layer self-supervised algorithm for community detection and analyze controversy within this network. A novel controversy metric is devised by considering three dimensions of controversy: community distinctions, betweenness centrality, and user representations. A comparison between our method and other classical controversy measures such as Random Walk, Biased Random Walk (BRW), BCC, EC, GMCK, MBLB, and community-based methods reveals that our model consistently produces more stable and accurate controversy scores. Additionally, we calculated the level of controversy and computed p-values for the detected communities on our crawled dataset Weibo, including #Microblog (3792), #Comment (45,741), #Retweet (36,126), and #User (61,327). Overall, our model had a comprehensive and nuanced understanding of controversy on social media platforms. To facilitate its use, we have developed a user-friendly web server.

PMID:40422428 | DOI:10.3390/e27050473

Ann Gen Psychiatry. 2025 May 23;24(1):32. doi: 10.1186/s12991-025-00568-8.

ABSTRACT

Adolescent suicide represents a critical global health issue. While research has identified numerous risk factors, the specific impact of parental dysfunction on adolescent suicide remains understudied, especially in Chinese contexts. This study explores how parental dysfunction manifests in suicide notes and affects adolescent mental health. We collected data from Chinese social media platforms using web crawlers, yielding 30 valid suicide notes for analysis. Using the AI-aided content analysis platform DiVoMiner®, we conducted high-frequency word and semantic network analyses. Our findings reveal that parents are a central concern for suicidal youth. We identified three primary patterns of parental dysfunction: excessive emphasis on instrumental goals, neglect of basic emotional needs, and inadequate protection from life traumas. These dysfunctions contribute to severe psychological distress, identity loss, and negative coping behaviors among youth. The research highlights two significant phenomena in contemporary Chinese family dynamics: the "short-sightedness" of prioritizing short-term instrumental goals over long-term social-emotional development, and the remarkably high prevalence of "lack of autonomy" in parenting approaches. Our study extends the literature by exploring mechanisms through which parental dysfunctions contribute to suicidal behaviors in young people. These findings emphasize the need for collaborative efforts among parents, educators, policymakers, and mental health professionals to foster nurturing environments characterized by emotional support, autonomy encouragement, and balanced academic expectations-all crucial for adolescent well-being.

PMID:40410879 | DOI:10.1186/s12991-025-00568-8

Eur Burn J. 2025 May 15;6(2):25. doi: 10.3390/ebj6020025.

ABSTRACT

Burn injuries lead to significant physical and psychological consequences, including chronic pain, post-traumatic stress, depression, and social isolation. Mindfulness-based interventions (MBIs) have been proposed as a holistic approach to address these challenges in burn rehabilitation. This systematic review evaluates the efficacy of dispositional mindfulness and MBIs, including mindfulness meditation, yoga, and self-compassion training, in managing pain, emotional distress, and psychosocial adaptation in burn survivors. A comprehensive literature search was conducted through MEDLINE and Web of Science, covering studies up to February 2025, with additional papers retrieved from Google Scholar and Semantic Scholar. Studies were included if they reported quantitative data on the effects of MBIs in burn patients and/or their families, excluding opinion pieces, editorials, reviews, and qualitative studies. After screening 91 studies retrieved from the databases and adding a compelling paper retrieved from the other sources explored, 12 studies were included in the final pool, categorized into cross-sectional studies (n = 6), and intervention studies (n = 6). The extracted data included publication year, research design, sample characteristics, intervention details, main findings, and data for quality assessment. The synthesis of the results suggests that mindfulness is associated with reduced psychological symptoms, improved emotional regulation, and enhanced self-compassion, leading to better coping strategies and social reintegration. However, the long-term efficacy of MBIs remains inconclusive, and further research is needed to differentiate mindfulness-specific effects from those of general physical exercise. Evidence also suggests that mindfulness interventions may reduce anxiety and secondary trauma in children with burns and their caregivers. This review highlights the potential of MBIs as adjuncts to conventional burn rehabilitation programs, but further high-quality trials are needed to establish their sustained efficacy and to understand the specific benefits of mindfulness.

PMID:40407681 | DOI:10.3390/ebj6020025

Database (Oxford). 2025 May 20;2025:baaf008. doi: 10.1093/database/baaf008.

ABSTRACT

We previously described Graph2VR, a prototype that enables researchers to use virtual reality (VR) to explore and navigate through Linked Data graphs using SPARQL queries (see https://doi.org/10.1093/database/baae008). Here we evaluate the use of Graph2VR in three realistic life science use cases. The first use case visualizes metadata from large-scale multi-center cohort studies across Europe and Canada via the EUCAN Connect catalogue. The second use case involves a set of genomic data from synthetic rare disease patients, which was processed through the Variant Interpretation Pipeline and then converted into Resource Description Format for visualization. The third use case involves enriching a graph with additional information, in this case, the Dutch Anatomical Therapeutic Chemical code Ontology with the DrugID from Drugbank. These examples collectively showcase Graph2VR's potential for data exploration and enrichment, as well as some of its limitations. We conclude that the endless three-dimensional space provided by VR indeed shows much potential for the navigation of very large knowledge graphs, and we provide recommendations for data preparation and VR tooling moving forward. Database URL: https://doi.org/10.1093/database/baaf008.

PMID:40402773 | DOI:10.1093/database/baaf008

J Cheminform. 2025 May 21;17(1):79. doi: 10.1186/s13321-025-01017-0.

ABSTRACT

Named entities, such as chemicals/drugs, genes/proteins, and diseases, and their associations are not only important components of biomedical literature, but also the foundation of creating biomedical knowledgebases and knowledge graphs. This work addresses the challenges of expressing co-occurrence associations between named entities extracted from a biomedical literature corpus in a machine-readable format. We developed a Resource Description Framework (RDF) data model and integrated it into the PubChemRDF resource, which is freely accessible and publicly available. The developed co-occurrence data model was populated into a triplestore with named entities and their associations derived from text mining of millions of biomedical references found in PubMed. The utility of the data model was demonstrated through multiple use cases. Together with meta-data modeling of the references including the information about the author, journal, grant, and funding agency, this data model allows researchers to address pertinent biomedical questions through SPARQL queries and helps to exploit biomedical knowledge in various user perspectives and use cases.

PMID:40399973 | DOI:10.1186/s13321-025-01017-0

Database (Oxford). 2025 May 20;2025:baaf008. doi: 10.1093/database/baaf008.

ABSTRACT

We previously described Graph2VR, a prototype that enables researchers to use virtual reality (VR) to explore and navigate through Linked Data graphs using SPARQL queries (see https://doi.org/10.1093/database/baae008). Here we evaluate the use of Graph2VR in three realistic life science use cases. The first use case visualizes metadata from large-scale multi-center cohort studies across Europe and Canada via the EUCAN Connect catalogue. The second use case involves a set of genomic data from synthetic rare disease patients, which was processed through the Variant Interpretation Pipeline and then converted into Resource Description Format for visualization. The third use case involves enriching a graph with additional information, in this case, the Dutch Anatomical Therapeutic Chemical code Ontology with the DrugID from Drugbank. These examples collectively showcase Graph2VR's potential for data exploration and enrichment, as well as some of its limitations. We conclude that the endless three-dimensional space provided by VR indeed shows much potential for the navigation of very large knowledge graphs, and we provide recommendations for data preparation and VR tooling moving forward. Database URL: https://doi.org/10.1093/database/baaf008.

PMID:40392751 | DOI:10.1093/database/baaf008

Gigascience. 2025 Jan 6;14:giaf045. doi: 10.1093/gigascience/giaf045.

ABSTRACT

BACKGROUND: In recent decades, several life science resources have structured data using the same framework and made these accessible using the same query language to facilitate interoperability. Knowledge graphs have seen increased adoption in bioinformatics due to their advantages for representing data in a generic graph format. For example, yummydata.org catalogs more than 60 knowledge graphs accessible through SPARQL, a technical query language. Although SPARQL allows powerful, expressive queries, even across physically distributed knowledge graphs, formulating such queries is a challenge for most users. Therefore, to guide users in retrieving the relevant data, many of these resources provide representative examples. These examples can also be an important source of information for machine learning (for example, machine-learning algorithms for translating natural language questions to SPARQL), if a sufficiently large number of examples are provided and published in a common, machine-readable, and standardized format across different resources.

FINDINGS: We introduce a large collection of human-written natural language questions and their corresponding SPARQL queries over federated bioinformatics knowledge graphs (KGs) collected for several years across different research groups at the SIB Swiss Institute of Bioinformatics. The collection comprises more than 1,000 example questions and queries, including almost 100 federated queries. We propose a methodology to uniformly represent the examples with minimal metadata, based on existing standards. Furthermore, we introduce an extensive set of open-source applications, including query graph visualizations and smart query editors, easily reusable by KG maintainers who adopt the proposed methodology.

CONCLUSIONS: We encourage the community to adopt and extend the proposed methodology, towards richer KG metadata and improved Semantic Web services. URL: https://github.com/sib-swiss/sparql-examples.

PMID:40378136 | DOI:10.1093/gigascience/giaf045