Database: The Journal of Biological Databases and Curation最新文献

{"title":"A similarity metric, rubric, and unified hierarchy for biomedical publication types and study designs.","authors":"Neil R Smalheiser, Joe D Menke, Arthur W Holt","doi":"10.1093/database/baag022","DOIUrl":"10.1093/database/baag022","url":null,"abstract":"<p><p>Our goal is to unify the 72 biomedical publication types and study designs (collectively, PTs) into a single rubric and hierarchy. This is carried out in a data-driven manner by computing pairwise similarities of each PT against all others to form a similarity matrix. By performing hierarchical clustering we place each PT in a specific category and collect these into broader categories. Spearman correlations among PT pairs ranged from strongly negative to strongly positive (-0.732 to +0.997), with a mean of 0.176. Overall, we obtained 13 clusters of PTs and 5 more general categories: Observational Clinical Research, Qualitative and Genetic Methods, Clinical Evaluation and Validation, Interventional Trial Research, and Scholarly Synthesis and Discourse. These were then utilized to construct a unified hierarchy of PT terms. The rubric provides a flexible classification scheme for publication types and study designs that can accommodate new PTs as they are added over time. The similarity metric has the potential to improve the modelling, implementation, and evaluation of automated indexing systems. The PT rubric provides an overview that complements the existing NIH MeSH Hierarchy trees, and the unified hierarchy permits proper automated expansion for PT indexing and PubMed user queries involving PT terms.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13237591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148175512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MOKCa-3D database: functional and structural analysis of missense mutations in cancer.","authors":"Biniam Haile, Adnan Cinar, Sayeda F Banini, Katrin Zhivkova, Finn J Gallagher, Christopher J Richardson, Frances M G Pearl","doi":"10.1093/database/baag001","DOIUrl":"10.1093/database/baag001","url":null,"abstract":"<p><p>Determining the functional consequence of missense mutations acquired in the development of cancer is critical to the understanding of the evolution and the therapeutic vulnerabilities of an individual tumour. Several million missense mutations associated with cancer have been reported across different databases with little functional annotation accompanying each mutation. We have designed the MOKCa-3D database, (https://bioinformaticslab.sussex.ac.uk/MOKCa-3D/) to enable the contextualization and interpretation of cancer somatic missense mutations, including the structural impact of the mutation on the 3D structure, and whether the mutation results in a gain or loss of the protein's function. For each protein, a sequence feature viewer enables interactive visualization of the amino acid sequence, missense mutations, post-translational modification sites, protein domains, active sites, binding sites, protein-protein interaction sites, and mutational frequency. The mutation-level page concisely presents functional insights for each individual mutation, and an interactive MOL* viewer highlights mutated residue on an AlphaFold protein structural model. The SAAP structural impact analysis pipeline was used to identify the structural impact of the mutation. MOKCa-3D concisely presents functional insights and structural impacts of cancer somatic missense mutations enabling users to interpret their functional consequences. It is freely accessible and easy to navigate, making it usable by the widest range of researchers.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13112024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147765158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset of xenobiotics human renal clearance values.","authors":"Natalia Łapińska, Sebastian Polak","doi":"10.1093/database/baag018","DOIUrl":"10.1093/database/baag018","url":null,"abstract":"<p><p>Scientific articles have been searched for experimentally established critical pharmacokinetic parameter-renal clearance. The main source of the documents was PubMed database, considered one of the most important and comprehensive repositories of biomedical literature. After manual data collection and thorough quality check database presenting human renal clearance values of exogenous substances was developed. After collecting the data, preliminary processing and simple analysis were carried out. The final database contains over 1700 experimental observations from 761 scientific articles and covers over 500 unique substances studied. Database URL: doi: https://10.17632/3427x3wzzc.2.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13070669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147671198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive morphological database of hognose Porthidium pitvipers (Viperidae: Crotalinae).","authors":"Carlos Patron-Rivero, Carlos Yañez-Arenas, Sara Ruane, Xavier Chiappa-Carrara, Octavio R Rojas-Soto","doi":"10.1093/database/baaf085","DOIUrl":"10.1093/database/baaf085","url":null,"abstract":"<p><p>Generating and sharing primary biological data is essential to support reproducible research, stimulate new hypotheses, and advance our understanding of biodiversity. Here, we present a comprehensive database of morphological traits for snakes of the genus Porthidium (Viperidae: Crotalinae). This database includes linear measurements, pholidosis (scale counts), and head shape data from preserved specimens across five different herpetological collections. These data comprise 13 morphological traits, 8 scale counts, and 55 landmarks collected from 484 individuals across 9 species. The specimens represent both juvenile and adult stages. All data were collected using standardized protocols to ensure comparability across individuals and species. The dataset is a valuable resource for studies in systematics, morphological evolution, ecological adaptation, and ontogeny, as well as facilitating reproducibility and reuse in the fields of evolutionary biology, herpetology, and comparative morphology.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12813580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145997451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving annotation, access, and comparison of nutritional composition data for underutilized crops.","authors":"Agnes Aboagye, Paul D Shaw, Sebastian Raubach, Sean Mayes, Graham King, Guillermina M Mendiondo","doi":"10.1093/database/baag050","DOIUrl":"10.1093/database/baag050","url":null,"abstract":"<p><p>Open and interoperable data infrastructures are essential to advancing food and nutritional security research, yet few data management systems are specifically designed for crop nutritional datasets that support seamless querying, visualization, and comparison. Existing data sources are often syntactically and semantically heterogeneous, creating substantial barriers to interoperability and data reuse, and ultimately limiting the translation of research into sustainable agricultural innovation. To address this gap, we developed an ontology-based data access and integration (OBDI) framework that harmonizes publicly available, heterogeneous plant nutritional datasets within a unified semantic structure. By annotating datasets using established plant science ontologies, we enhanced their Findability, Accessibility, Interoperability, and Reusability by providing a scalable mechanism for virtual data integration via a knowledge graph enriched with domain semantics. Our workflow enables consistent comparison and computational reasoning across crop nutritional composition data, supporting both machine and human interpretation. The integration of compositional datasets for underutilized crops (UCs) alongside major crops allows for the identification of genetic resources that provide enhanced nutritional outcomes, fostering evidence-based diversification strategies. We outline how this approach may facilitate data integration and assist in the wider adoption and utilization of UCs. More generally, this open, ontology-driven approach highlights how investment in standardized, FAIR-aligned data infrastructure has the potential to accelerate interdisciplinary collaboration across plant sciences, nutrition, and policy. We demonstrate the concepts and specific methodologies for establishing semantic relationship between diverse datasets useful in operationalizing heterogeneous crop trait phenotyping knowledge bases. Furthermore, we discuss how the integration of ontology-based data integration (OBDI) with machine learning enables intelligent, accessible trait data management, thereby enhancing the adoption and advancement of AI-driven decision-making.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13504263/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IBDkb: an AI-enhanced integrative knowledge base for inflammatory bowel disease research and drug discovery.","authors":"Liwen Tao, Shiqing Shi, Ruixin Zhu, Zhuo Liu, Bin Yang, Lei Liu, Wanning Chen, Qian Long, Na Jiao, Guoqing Zhang, Pingping Xu, Dingfeng Wu","doi":"10.1093/database/baag038","DOIUrl":"10.1093/database/baag038","url":null,"abstract":"<p><p>IBDkb (Inflammatory Bowel Disease Knowledge Base; https://www.biosino.org/ibdkb) is a freely accessible, integrated web-based platform that systematically curates and harmonizes multi-source data related to inflammatory bowel disease (IBD). To address the fragmentation and therapeutic gaps in existing specialized resources, IBDkb establishes a unified framework featuring advanced full-text search, interactive visualizations, cross-module knowledge graphs, and AI-powered utilities for real-time literature retrieval, trend analysis, text/PDF interpretation, and domain-specific conversational assistance. The platform currently integrates 98 453 research articles, 3390 clinical trials, 200 investigational drugs, 200 606 bioactive compounds, 103 therapeutic targets, 77 experimental models, 12 pathogenesis summaries, and 15 treatment strategies. These integrated tools facilitate efficient exploration of complex associations among drugs, targets, trials, and mechanisms, thereby accelerating hypothesis generation and translational research in IBD. The platform is openly available without registration and supports data downloads. A case study on structure-aware drug comparison further demonstrates its utility in facilitating cross-disease drug repositioning hypotheses.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13352432/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148419562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Goals and strategies for the indexing of publication types and study designs.","authors":"Neil R Smalheiser, Joe D Menke, Arthur W Holt, Halil Kilicoglu, Jodi Schneider","doi":"10.1093/database/baag039","DOIUrl":"10.1093/database/baag039","url":null,"abstract":"<p><p>Major research and implementation efforts have been devoted to indexing articles according to the major topics discussed, but much less effort to indexing their publication types and study designs (collectively, PTs). In this Perspective, we discuss how indexing PTs differs from topical Medical Subject Heading (MeSH) indexing and requires a different approach. Rather than focus on the technical aspects of machine learning-based indexing models, we emphasize the goals and purposes for which biomedical articles are indexed, and the surprisingly thorny question of how indexing systems should be evaluated. Topical Medical Subject Heading (MeSH) terms are assigned to articles that cover the major topics discussed; when more than one term is applicable, only the most specific term is assigned. In contrast, PTs are assigned to articles that have a given structure or use a particular design. To meet the needs of end users, particularly groups involved in evidence syntheses, PT indexing needs to be comprehensive and employ probabilistic goodness-of-fit prediction scores. Whereas existing National Library of Medicine (NLM) hierarchies place publication types and study design-related terms on separate trees from each other, we have created a unified hierarchy that permits more appropriate retrieval via automatic expansion. Automated PT indexing systems should allow users to input article records or full-text PDFs and receive scores in real time. This will offer consistent indexing across bibliographic databases, as well as preprints and unpublished manuscripts. Automated PT indexing systems, properly designed and implemented, hold the promise of greatly improving the retrieval of biomedical articles, saving substantial effort when writing evidence syntheses and benefiting other users as well. Database URL:  https://www.nlm.nih.gov/medline/medline_home.html.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13366529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148435576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MetaCancerDB: a database of site-specific RNA-miRNA correlations in cancer metastasis.","authors":"Myeonghun Cho, Byungkyu Park, Kyungsook Han","doi":"10.1093/database/baag026","DOIUrl":"10.1093/database/baag026","url":null,"abstract":"<p><p>Cancer metastasis involves complex molecular mechanisms that cannot be fully explained by individual gene expression profiles. Previous studies have shown that correlations between RNA and miRNA expression can capture metastatic behaviour more effectively than expression of individual genes. However, no publicly available databases provide systematic analysis of RNA-miRNA correlations specific to cancer metastasis. We developed an efficient computational method to identify differential correlations between miRNAs and RNAs that are specific to individual tumour samples. Using data from The Cancer Genome Atlas (TCGA), we computed differential correlations for tumour samples across 9 cancer types and 21 metastatic sites, encompassing ~200 million RNA-miRNA pairs. Statistical analysis identified RNA-miRNA pairs with site-specific correlations using Mann-Whitney U-tests. MetaCancerDB contains RNA-miRNA correlation networks for 9 primary cancer types and 21 metastatic sites. Site-specific correlations showed distinct patterns, with lung metastasis displaying the most conserved correlations across cancer types. Survival analysis revealed that specific RNA-miRNA pairs are prognostic for patient outcomes in a metastatic site-dependent manner. MetaCancerDB provides a comprehensive resource for exploring RNA-miRNA correlations in cancer metastasis. The database enables researchers to identify molecular signatures specific to metastatic sites and can serve as a foundation for developing predictive biomarkers. MetaCancerDB is freely available for academic purposes.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147971135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-scale manual curation and harmonization of metadata from metagenomic and cancer genomic repositories: challenges and solutions.","authors":"Kaelyn Long, Kai Gravel-Pucillo, Levi Waldron, Sean Davis, Sehyun Oh","doi":"10.1093/database/baag027","DOIUrl":"10.1093/database/baag027","url":null,"abstract":"<p><p>Public omics repositories contain vast amounts of valuable data, but their metadata suffers from extreme heterogeneity, unstandardized terminologies, and quality issues that severely limit data reusability and cross-study integration. While prospective metadata standards exist, the majority of published omics data remain in non-standardized formats requiring retrospective harmonization. We performed comprehensive manual curation and harmonization of metadata, such as participant characteristics and study conditions, from 212 027 omics samples across 468 studies in two repositories: curatedMetagenomicData (93 studies, 22 588 samples) and cBioPortal (375 studies, 189 438 samples). Through systematic ontology mapping, we consolidated redundant, dispersed information into far fewer harmonized columns, reduced unique values, and increased the completeness of major attributes. This curation process revealed common metadata quality issues, including typos, inconsistent terminologies, misplaced values, conflicting annotations, and inappropriately merged information across attributes. We document the challenges, decisions, and solutions during this large-scale metadata harmonization. The harmonized metadata, accessible through the OmicsMLRepoR Bioconductor package, enables repository-wide queries and cross-study analyses previously challenging with heterogeneous metadata. Our experience provides practical guidance for similar curation efforts and demonstrates the value of investing in retrospective metadata improvement for existing public omics resources.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13196698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148005155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MedHopQA: a disease-centred multi-hop reasoning benchmark and evaluation framework for LLM-based biomedical question answering.","authors":"Rezarta Islamaj, Robert Leaman, Joey Chan, Nicholas Wan, Qiao Jin, Natalie Xie, John Wilbur, Shubo Tian, Lana Yeganova, Po-Ting Lai, Chih-Hsuan Wei, Yifan Yang, Yao Ge, Qingqing Zhu, Zhizheng Wang, Zhiyong Lu","doi":"10.1093/database/baag057","DOIUrl":"10.1093/database/baag057","url":null,"abstract":"<p><p>Evaluating large language models (LLMs) in the biomedical domain requires benchmarks that can distinguish genuine reasoning from pattern matching and that remain discriminative as model capabilities improve. Existing biomedical question answering (QA) benchmarks are limited in this respect: multiple-choice formats allow models to succeed by answer elimination rather than inference, and widely circulated exam-style datasets are subject to performance saturation and training data contamination. Multi-hop reasoning, i.e. the ability to integrate information across multiple sources to derive an answer, is central to clinically meaningful tasks such as diagnosis support, literature-based discovery, and hypothesis generation, yet remains underrepresented in current biomedical QA benchmarks. We present MedHopQA, a disease-centred multi-hop reasoning benchmark of 1000 expert-curated question-answer pairs introduced as a shared task at BioCreative IX. Each question requires synthesis of information across two distinct Wikipedia articles, and answers are provided in open-ended free-text format rather than as multiple-choice selections. Gold annotations are augmented with ontology-grounded synonym sets (MONDO, NCBI Gene, and NCBI Taxonomy) to support both lexical and concept-level evaluation. The dataset was constructed through a multi-stage human-AI pipeline combining structured human annotation, triage, iterative verification, and LLM-as-a-judge validation. To reduce leaderboard gaming and contamination risk, the 1000 scored questions are embedded within a publicly downloadable set of 10 000 questions, with answers withheld, on a CodaBench leaderboard. Evaluation of four frontier LLMs under a zero-shot setting (GPT-5.1, Gemini 2.5 Pro, Claude Sonnet 4.5, and GPT-4o) reveals performance variation across answer types, with overall accuracy ranging from 66.3% to 83.4%. Performance is strongest on chemical and anatomical questions and most variable on disease and gene/protein categories, where fine-grained semantic discrimination is required. MedHopQA provides both a benchmark and a reusable framework for constructing future biomedical QA datasets that prioritize compositional reasoning, saturation resistance, and contamination mitigation as design constraints.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148966725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}