Journal of Biomedical Semantics生物医学语义学杂志
Journal of Biomedical Semantics(英文缩写 J BIOMED SEMANT),ISSN 2041-1480,eISSN 2041-1480,中文译名:生物医学语义学杂志 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 1.675 | Q4 |
| 2022 | 1.900 | Q3 |
| 2023 | 1.600 | Q3 |
| 2024 | 2.000 | Q3 |
| 2025 | 2.600 | Q2 |
Journal of Biomedical Semantics 最新收录文献
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1. Utilizing BERTopic modeling for concept discovery in the domain of gerotranscendence and solitude.
PMID:日期:2026-09-01Ontology development is a complex, iterative process that traditionally requires extensive collaboration between ontology developers and subject matter experts. While effective, this manual approach is time-consuming, labor-intensive, and prone to cognitive bias. To streamline early-stage ontology development and uncover concepts that might be overlooked through manual review alone, we applied automated topic modeling with BERTopic to extract topics, keywords, topic labels, and summaries from The Handbook of Solitude: Psychological Perspectives on Social Isolation, Social Withdrawal, and Being Alone and Gerotranscendence: A Developmental Theory of Positive Aging. The extracted topic labels after review were used as candidate concepts for the Promoting Healthy Aging through Semantic Enrichment of Solitude Research (PHASES) Ontology. We implemented and compared two BERTopic pipelines: (1) the default pipeline and (2) a custom preprocessing pipeline incorporating part-of-speech filtering and n-gram tuning. The pipeline is customized to flexibly extract any specified number of topics and keywords based on user-defined parameters. To compare and merge topic modeling outputs across solitude and gerotranscendence, we used semantic embeddings of topic labels, keywords, and summaries from the custom pipeline. Cosine similarity identified semantically matched topic pairs above a set threshold, enabling categorization and integration into a merged conceptual framework that bridges both domains. From the solitude corpus, BERTopic generated 244 initial topics, which, after SMEs review, were refined to 32 high-quality topics with the custom pipeline and 46 with the default pipeline. For the gerotranscendence corpus, the pipeline produced 172 initial topics, refined to 33 with the custom pipeline and 32 with the default pipeline. Across both corpora, BERTopic contributed 90 ontology terms, 52 from the solitude corpus and 38 from the gerotranscendence corpus. Visual evaluations, including keyword score bar charts, hierarchical clustering dendrograms, and BART-generated summaries, revealed that the custom pipeline produced more fine-grained, domain-specific topics, while the default pipeline offered broader thematic coverage and clearer labels. Certain theory-laden concepts, however, required interpretive input from SMEs. BERTopic provided an efficient, semi-automated approach for identifying candidate ontology terms from domain literature, supporting both breadth and specificity in concept capture. The topics produced by BERTopic modeling uncover underlying latent concepts, serving as a link between raw text data and structured ontology classes. By facilitating the formation of the hierarchical relationships, it exposes a layered semantic structure that is essential for ontology development. Further, integrating semantic similarity analysis across thematic domains revealed conceptual intersections and overlaps, enhancing the semantic foundation of the PHASES Ontology and offering a replicable method for cross-domain ontology development.
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2. Benchmarking MeSH-augmented embeddings for biomedical document similarity.
PMID:日期:2026-08-14The extensive volume of biomedical scientific literature requires efficient methods for retrieving relevant documents based on semantic technologies and biomedical concepts. While embedding-based methods have shown improvements over traditional keyword-based methods, the integration of domain-specific terminologies like Medical Subject Headings (MeSH) into these models remain underexplored. This study compares three hybrid methods that integrate MeSH-based annotations with document embeddings (called "pre-annotation", "post-annotation" and"post-reduction"). We benchmark these hybrid methods against the following traditional methods: TF-IDF, standard neural embeddings (Word2Vec, fastText, Doc2Vec) and publicly available transformer-based models (BioBERT, SciBERT, SPECTER, SapBERT), using cosine similarity and Word Mover's Distance (WMD) as evaluation metrics. The benchmark experiments are based on the RELISH corpus, a manually curated dataset of PubMed articles where experts have labeled pairs of documents with regards to their relevance to each other, providing a 2-class (relevant vs. non-relevant) as well as a 3-class (relevant, partially relevant, non-relevant) judgment. Transformer-based models, particularly fine-tuned BioBERT and SciBERT align best with the expert judgements after fine-tuning. Among non-transformer methods, Doc2Vec and MeSH-based hybrid methods also perform well, demonstrating the benefits from combining structured biomedical vocabularies with embedding methods. Our experiments deliver extensive results showing that the baseline performance of 76-78% precision at position 5 can be achieved through almost all approaches, improvements of 2-4% with MeSH concepts can be achieved, but performances up to 90% is left to the fine-tuned large-scale public models. The performance gains from the integration of concepts may be underwhelming, however the benefits lie in the successful integration and benchmarking of structured vocabularies with embedding methods, the applicability of these techniques to aligning literature with other data sources via a controlled vocabulary and the potential for stronger performance on tasks and corpora where concept-based resources are better suited. All experiments have been conserved as a Dockerized pipeline, making the full benchmarking workflow reproducible and supporting future research in biomedical document retrieval.
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3. Introducing the TOP framework: a novel phenotyping solution for collaborative phenotype algorithm development and application.
PMID:日期:2026-07-27Phenotyping, the comprehensive assessment of observable characteristics, is essential for advancing medical understanding and personalised healthcare. However, traditional phenotyping methods are often manual, time-intensive, and limited in scope. To address these challenges, this work introduces the TOP Framework, a software suite that leverages a structured approach. It provides tools for the formal definition of phenotypes, their organisation into ontological classes, the creation of phenotype models for disease-specific knowledge representation, and the generation of phenotype queries for automated data retrieval and analysis. A dynamic phenotype algorithm integrates these modules to efficiently identify individuals meeting complex phenotypic criteria. The Model for End-Stage Liver Disease (MELD) score serves as a running example to illustrate the framework's capabilities. Furthermore, this paper presents a preliminary evaluation of the TOP Framework's user experience by means of the User Experience Questionnaire (UEQ), assessing its usability and suitability for researchers and clinicians. The TOP Framework includes a robust implementation of a reasoner model for deriving complex phenotypes and an automated testing module to ensure reliability. The user experience evaluation yielded generally positive results on a scale from -3 to 3 (n = 11), with mean scores and 95% confidence intervals as follows: attractiveness, 1.50 (CI = (1.07, 1.93)); pragmatic quality, 1.38 (CI = (1.00, 1.77)); and hedonic quality, 1.40 (CI = (0.76, 2.03)). The TOP Framework offers a novel and automated approach to phenotyping, with the potential to enhance the efficiency, scalability, and reproducibility of phenotyping studies. This advancement contributes to a deeper understanding of disease and the progression of precision medicine.
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4. Terminology binding with SNOMED CT to bridge differences in information structures: a feasibility study.
PMID:日期:2026-07-24Safe and accurate sharing of healthcare information is essential for good health and well-being. Information must be exchanged among multiple parties, making the transformation between different information structures unavoidable. It has been suggested that such a transformation could be automated if information were sufficiently structured. This study aims to explore whether terminology binding to SNOMED CT can act as a bridge between different information structures. This study employed a mixed-methods approach using two information structures from the breast cancer pathology domain. Pairs of semantically equivalent user interface terms from these structures were identified by a clinician and verified by two informaticians. The terms were considered semantically equivalent when a clinician would have used them to convey the same information. The pairs of SNOMED CT concepts bound to each of the interface terms were then compared and categorised as transformable, partially transformable, or non-transformable. Non-transformable concept pairs were analysed qualitatively, using an inductive content analysis approach, to identify underlying causes. 73 term pairs were identified. In 70% of these, the user interface terms were terminology bound to the same SNOMED CT concept. Among term pairs with terms bound to different concepts, 9% were fully transformable, 27% partially transformable, and 64% non-transformable. All is-a-related concept pairs were either fully or partially transformable, whereas concept pairs from different hierarchies were never transformable. Major obstacles within the same hierarchy included the use of primitive concepts and nearly identical sibling concepts. For concept pairs across different hierarchies, divergent terminology-binding strategies-such as handling of integers and "other" values-hindered transformability. Additional issues were observed with local user interface terms and SNOMED CT translations. Although concordance between the two structures was relatively high (70%), transformability among semantically equivalent term pairs bound to different SNOMED CT concepts was low (9%). This can be improved on the terminology-binding side through guidelines and shared terminology bindings, and on the ontology side by increasing the proportion of sufficiently defined concepts and ensuring the quality of descriptions and translations. These findings are significant for practitioners seeking to enhance interoperability, as well as for SNOMED International and researchers focusing on terminology quality and transformation approaches.
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5. Towards reliable Spanish clinical text de-identification through comparative evaluation of language model approaches.
PMID:日期:2026-07-15Unstructured clinical narratives in electronic health records contain essential information for healthcare delivery and research. However, the presence of personally identifiable information poses significant privacy risks, which limit the secondary use of data. Therefore, reliable automated de-identification is a prerequisite for the reuse of clinical texts. This study aims to evaluate different strategies for Spanish clinical text de-identification by assessing the performance of language models on synthetic and real-world datasets. This study presents a comparative evaluation of de-identification approaches using two datasets in Spanish: MEDDOCAN, a publicly available synthetic corpus, and ObstEHR, real-world clinical narratives from an obstetrics department. Three de-identification strategies were assessed: (i) inference with a pre-trained task-specific model used as-is, (ii) prompt-based inference using locally deployed large language models (LLMs), and (iii) fine-tuned task-specific models. Model performance was evaluated using precision, recall, and F1 score on test sets. In addition, a text preservation metric was introduced to assess prompt-based LLMs, and the impact of training set size was analyzed using progressively larger subsets of annotated ObstEHR data. Models without fine-tuning and prompt-based LLMs showed limited performance, with macro-averaged F1 scores ranging from 0.14 to 0.5 on ObstEHR and from 0.19 to 0.5 on MEDDOCAN. Fine-tuned models achieved higher performance, reaching macro-averaged F1 scores of up to 0.956. Learning curve analyses showed consistently high precision and gradual improvements in recall as the amount of training data increased, with high performance achieved with moderate amounts of annotated data. Fine-tuning-based approaches outperform models without task-specific adaptation and prompt-based strategies. Despite their flexibility in generative settings, LLM-based prompt strategies show limited reliability and information preservation in clinical de-identification. Prompt-based LLMs make slight textual modifications that cause token misalignment, leading to a subsequent decrease in evaluation metrics. Therefore, domain-specific fine-tuning remains the most effective strategy for real-world clinical text de-identification.
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6. ACCELERATE-BASSO: early experiences and emerging best practices for ontology development in behavioral and social science research.
PMID:日期:2026-07-14Behavioral and social science research (BSSR) is essential for understanding human behavior and informing interventions, policy, and health outcomes; however, such research faces persistent challenges related to fragmented knowledge, imprecise and inconsistent terminology, and limited interoperability across studies. Ontologies provide a promising solution by enabling standardized, machine-readable representations of concepts and relationships to support data integration, knowledge synthesis, and reproducibility. We present an initial overview of the ACCELERATE-BASSO Research Network, an NIH-supported consortium established to advance BSSR through ontology-driven approaches. Specifically, we describe the role of the Dissemination and Coordination Center (DCC) and the Best Practices Working Group (BPWG) in supporting a network of projects focusing on accelerating BSSR through ontology development and use. Based on the consortium's early experiences, we summarize emerging ontology-driven practices, current interoperability efforts, and common methodological considerations identified across the participating projects. Rather than proposing a formal consensus framework, this work provides an initial consortium perspective on current ontology development activities and shared lessons learned. We also discuss the potential role of ontologies in supporting AI-related applications in BSSR. This paper provides an initial consortium perspective on ontology-driven approaches for advancing BSSR. By synthesizing early experiences, emerging practices, and shared challenges across multiple projects, it offers insights to inform future ontology development, interoperability, and community consensus while laying the foundation for more systematic evaluation and broader adoption within the BSSR community.
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7. OnTEPT: a multimodal OWL ontology for post-traumatic stress disorder.
PMID:日期:2026-07-07Post-traumatic stress disorder (PTSD) is a heterogeneous neuropsychiatric condition that develops after exposure to traumatic events. Its diagnosis is currently governed by two major international classification systems, Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases) ICD, which differ in structure, granularity, and diagnostic thresholds. Moreover, PTSD assessment is also multimodal, spanning structured interviews, self-report psychometric instruments, and increasing neurophysiological recordings such as electroencephalography (EEG). This study presents OnTEPT, an OWL-based multimodal ontology designed to formalize DSM-5 and ICD-11 diagnostic criteria, integrate psychometric instruments and EEG-derived features, and support automated reasoning over individual patient instances. OnTEPT was developed following Ontology Development 101 (OD101) in Protégé 5. A hybrid strategy was used, combining top-down and bottom-up approaches and reusing concepts from health and mental health ontologies. The current version includes 112 classes, 18 object properties, and 113 data properties. DSM-5 (A-H) and ICD-11 (1-6) criteria are formalized as OWL equivalence axioms. These use constraints on cardinality, duration, clinical significance, and exclusions. Dissociative and complex PTSD are also represented. Four instruments are modeled at the item-level with cutoff thresholds, and two of them are linked to diagnostic criteria through annotations. The ontology was populated with psychometric data from 304 patients, EEG data from 41 participants, and 30 structured synthetic instances generated with Owlready2. 36 quantitative EEG features are encoded as typed data properties. Validation with HermiT, Pellet, and FaCT + + confirmed logical consistency and class satisfiability. Four intentionally inconsistent instances were correctly rejected due to classification issues. Query-based validation retrieved 113 patients above screening thresholds. It also found three with full DSM-5 profiles, twelve under ICD-11, two with dissociative PTSD, and one with complex PTSD. OnTEPT provides a computable semantic framework that harmonizes PTSD diagnosis and its subtypes between DSM-5 and ICD-11. It integrates psychometric and EEG information within a single ontology. Its main contribution is an inferential layer that enables the automated classification of patient instances using both diagnostic systems. The architecture is extensible and ready to include biomarker-based rules, longitudinal data, and semantic workflows to support clinical decision-making.
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8. Immune biomarkers, profiles, and responses: a vaccine ontology perspective.
PMID:日期:2026-06-27Vaccines have the ability to induce a range of immune responses under different conditions, for example stimulating the production of neutralizing antibodies to block pathogen entry or activating cytotoxic T cells to eliminate infected cells. Many such immune responses have not been thoroughly examined and classified. The Vaccine Ontology (VO) is a community-based ontology in the domain of vaccinology. We here describe how VO is used to represent the variety of immune responses associated with vaccines, together with associated biomarkers and profiles. The VO differentiates 'vaccination' and 'vaccine immunization.' The former is a process of administering a vaccine in vivo; the latter is the outcome of vaccine induction of immune response. This distinction is critical for understanding both the procedure of vaccination and the resulting immune effects. VO also models and represents various vaccine-induced responses at multiple biological levels, including population, organism, organ/tissue, cell, and gene/protein levels. Such an approach captures the complexity of vaccine-induced immunity, from population-wide trend (for example: herd immunity) to molecular mechanisms. VO defines immune biomarkers as material entities such as neutralizing antibodies that signify a humoral immune response, and IFN gamma that is indicative of cell-mediated responses. Such biomarkers provide measurable indicators of the immune system's functional state post vaccination, enabling robust evaluation of vaccine efficacy. VO classifies 'immune response profile' and 'correlated profile (or correlate) of immune protection' as 'process profiles,' a class in the Basic Formal Ontology (BFO 2.0). Immune response profiles, such as 'Th1 (or Th2)-biased profile,' can be induced by various vaccines and vaccine adjuvants. Different types of 'correlated profile of immune protection' are also identified, such as mechanistic and non-mechanistic correlates of immune protection. The important immune-related terms for immune biomarkers, profiles, and responses are modeled ontologically in VO together with their interrelations. The results support enhanced classification and analysis of vaccine-induced immune responses and related biomarkers and immune profiles, leading to further understanding of the vaccine immune mechanisms and enhanced vaccine research and development.
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9. A pragmatist approach to bridging tables and ontologies through LinkML and punning.
PMID:日期:2026-06-23Ontologies support data integration and knowledge discovery across multiple life science disciplines through the application of formal semantics that define hierarchical and cross-hierarchical relationships which enable automated reasoning capabilities. At the same time, tabular data is by far the most commonly produced format of data in most disciplines. This is especially true in biodiversity dataset production. Creating rich semantic data from tables can be complex because of a mismatch between flat tabular data and hierarchical ontology models. Here, we present a practical approach for closing this gap using Linked Data Modeling Language (LinkML) in combination with OWL punning. LinkML allows tabular schemas to be formally defined, validated, and automatically documented. OWL punning allows schema elements to function simultaneously as both classes and data properties. This approach has many practical advantages that we showcase using mammal trait data assembled from the Ranges Digitization Network. Our work demonstrates how using LinkML + punning simplifies alignment of trait data in tabular format with ontologies such as the FuTRES Ontology of Vertebrate Traits (FOVT). Additionally, we show multiple advantages of our practical approach, including: maintaining synchronization between schema, ontology, and documentation, reducing modeling overhead, and preserving compatibility with conventional relational data workflows. On the downside, full logical reasoning is limited relative to more verbose RDF translations. Often this is a perfectly acceptable tradeoff for many use cases focused on data discovery and integration. The LinkML + punning framework shown here provides a scalable and pragmatic strategy for building semantically rich, ontology-aligned data resources that lower the barrier to semantic interoperability in biodiversity and trait informatics.
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10. FAIR in practice: minimum metadata schema for bioinformatics analytics by machines.
PMID:日期:2026-06-19One pillar of FAIR principles adoption is Reusability by machines to enable for example more efficient data analytics in fields such as Bioinformatics. However, it is not clear to what extent current metadata exposed by datasets and tools in common repositories enable this. In practice, metadata often lacks in machine actionability due to incomplete standardised metadata and lack of ontological descriptions. In this work, we identified minimal metadata that is needed to improve the machine actionability of bioinformatics tools and proposed a schema to address current limitations. The schema consists of metadata properties for the identification, selection, validation, and execution of tools. We also aligned this metadata to the metadata of datasets, in order to improve their integration for analytics by machines. The identified minimal metadata improves the machine actionability of tools and data, and can be incorporated into platforms for tool and data sharing, and in FAIR infrastructures.