Susan M. Bello

Motivation: Biocuration is the integration of biological information databases for the enhancement of research. Curation of these databases is challenged by the exponential growth of scientific data and literature. Integration of machine learning and artificial intelligence methods into biocuration workflows may help address this challenge. We report on the discussions, ideas, and recommendations gathered from a workshop “AI and biodata resources: implications for sustainability and best practices in biocuration” at the 18th Annual International Biocuration Conference 2025. Results: Participants agreed that while AI offers transformative potential for efficiency and expanded curatorial capacity, its integration faces substantial hurdles. Key challenges revolve around data and model quality. Reproducibility issues and a lack of open, domain-specific training datasets further compound these problems. Broader concerns include inconsistent data standards, underdeveloped ontologies, unstructured data, legacy systems, and underfunded teams. Despite these issues, several successful AI applications were identified, including tools for literature summarization and workflow assistance. Participants emphasized the need for a refined model of human-AI collaboration requiring clear data provenance and transparency, new skills, and avoiding over-reliance on AI-generated data. The workshop ultimately recommended concerted efforts in infrastructure development, standardization, training, and quality assurance to guide the community toward effective human-AI collaboration that maintains scientific rigor.

The Human Disease Ontology Knowledgebase (DO-KB; https://disease-ontology.org/), a Global Core Biodata Resource, serves as a reference framework for multiscale biomedical data integration and analysis, within a unifying etiology-based disease classification. In this 2026 update of the Human Disease Ontology (DO) and DO-KB resource, we present significant advances in programmatic data retrieval and disease representation since our previous NARdb publication. Here we report on the development of a DO Nosology education program, the Spanish translation of the DO website and ontology content, and expansion of disease knowledge representation.

The mouse is a premier model system for investigating gene function and modeling human disease. For almost 40 years, Mouse Genome Informatics has worked to capture and integrate the data generated from mouse studies. A critical component of this integration is the development and use of the Mammalian Phenotype (MP) Ontology to capture the morphological and physiological effects of alterations to gene function in the mouse. As the wealth of phenotype data captured using the MP has expanded, its utility in the diagnosis of human disease has increased. Tools have been developed to use mouse and human phenotypes in variant identification. To enhance the applicability of the MP in disease diagnosis and increase the ability of researchers to find models for specific research questions, we have undertaken a disease-focused expansion of the MP. In addition, we have worked to improve the alignment of the MP to the Human Phenotype Ontology to make automated translation between mouse and human phenotypes easier and more reliable.

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

In 2003, the Human Disease Ontology (DO, https://disease-ontology.org/) was established at Northwestern University. In the intervening 20 years, the DO has expanded to become a highly-utilized disease knowledge resource. Serving as the nomenclature and classification standard for human diseases, the DO provides a stable, etiology-based structure integrating mechanistic drivers of human disease. Over the past two decades the DO has grown from a collection of clinical vocabularies, into an expertly curated semantic resource of over 11300 common and rare diseases linking disease concepts through more than 37000 vocabulary cross mappings (v2023-08-08). Here, we introduce the recently launched DO Knowledgebase (DO-KB), which expands the DO’s representation of the diseaseome and enhances the findability, accessibility, interoperability and reusability (FAIR) of disease data through a new SPARQL service and new Faceted Search Interface. The DO-KB is an integrated data system, built upon the DO’s semantic disease knowledge backbone, with resources that expose and connect the DO’s semantic knowledge with disease-related data across Open Linked Data resources. This update includes descriptions of efforts to assess the DO’s global impact and improvements to data quality and content, with emphasis on changes in the last two years.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

The International Society for Biocuration (ISB) aims to promote the field of biocuration and provide a community forum for information exchange and networking. Over the past 14 years, the ISB has hosted annual international conferences, entirely dedicated to the field of biocuration, that rotate between regions across the world. These meetings bring together biocurators from various roles, including database curators, bioinformaticians, ontology developers and students. Due to the ongoing global pandemic, the 14th Annual ISB Biocuration Conference (ISB2021) was held virtually in the form of four sessions and one workshop over the course of the year. Each of the four virtual sessions included panel discussions covering (i) The Future of Biocuration, (ii) Career paths and projections in Biocuration, (iii) Addressing Implicit or Unconscious Bias: Equity, Diversity and Inclusion and (iv) Strategic planning. Here we report on highlights from the virtual conference and share some of the ideas and future goals of the ISB. Database URL:https://www.biocuration.org/14th-annual-biocuration-conference-virtual/

The Human Disease Ontology (DO) (www.disease-ontology.org) database, has significantly expanded the disease content and enhanced our userbase and website since the DO’s 2018 Nucleic Acids Research DATABASE issue paper. Conservatively, based on available resource statistics, terms from the DO have been annotated to over 1.5 million biomedical data elements and citations, a 10× increase in the past 5 years. The DO, funded as a NHGRI Genomic Resource, plays a key role in disease knowledge organization, representation, and standardization, serving as a reference framework for multiscale biomedical data integration and analysis across thousands of clinical, biomedical and computational research projects and genomic resources around the world. This update reports on the addition of 1,793 new disease terms, a 14% increase of textual definitions and the integration of 22 137 new SubClassOf axioms defining disease to disease connections representing the DO’s complex disease classification. The DO’s updated website provides multifaceted etiology searching, enhanced documentation and educational resources.