Biomedical Ontologies Dotaz Zobrazit nápovědu
The wide-spread use of Common Data Models and information models in biomedical informatics encourages assumptions that those models could provide the entirety of what is needed for knowledge representation purposes. Based on the lack of computable semantics in frequently used Common Data Models, there appears to be a gap between knowledge representation requirements and these models. In this use-case oriented approach, we explore how a system-theoretic, architecture-centric, ontology-based methodology can help to better understand this gap. We show how using the Generic Component Model helps to analyze the data management system in a way that allows accounting for data management procedures inside the system and knowledge representation of the real world at the same time.
- Klíčová slova
- Biomedical Ontologies, Information Models, Knowledge Representation, Systems Theory, eHealth,
- MeSH
- bio-ontologie * MeSH
- data management MeSH
- sémantika * MeSH
- Publikační typ
- časopisecké články MeSH
The paper describes procedures and a tool we have developed to simplify and speed up creating of Czech biomedical ontologies. Our method is based on searching for concepts in a corpus of medical texts and binding those concepts to an established international ontology. The new ontology will have two major advantages: it will be compatible with the international ontology and it will possibly cover all concepts used in the Czech healthcare. The tool supports an author of ontology by mechanizing some routine tasks that occurs in the process of an ontology creation. It tries to learn how to identify concepts in texts and how to bind them to the ontology. The tool then displays the suggestions to a user, who can correct them and add some new ones. Based on this feedback the tool adjusts rules for concept finding and binding. To accomplish such behaviour we have employed some natural language processing methods and information extraction tools.
- MeSH
- lékařská informatika organizace a řízení MeSH
- rozvoj plánování * MeSH
- systémy pro podporu klinického rozhodování MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Česká republika MeSH
The concept of Data Management Plan (DMP) has emerged as a fundamental tool to help researchers through the systematical management of data. The Research Data Alliance DMP Common Standard (DCS) working group developed a set of universal concepts characterising a DMP so it can be represented as a machine-actionable artefact, i.e., machine-actionable Data Management Plan (maDMP). The technology-agnostic approach of the current maDMP specification: (i) does not explicitly link to related data models or ontologies, (ii) has no standardised way to describe controlled vocabularies, and (iii) is extensible but has no clear mechanism to distinguish between the core specification and its extensions.This paper reports on a community effort to create the DMP Common Standard Ontology (DCSO) as a serialisation of the DCS core concepts, with a particular focus on a detailed description of the components of the ontology. Our initial result shows that the proposed DCSO can become a suitable candidate for a reference serialisation of the DMP Common Standard.
- Klíčová slova
- Data management plan, Machine-actionable data management plan, Ontology, Semantic web technologies,
- MeSH
- bio-ontologie * MeSH
- data management * MeSH
- řízený slovník MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
As the amount of genome information increases rapidly, there is a correspondingly greater need for methods that provide accurate and automated annotation of gene function. For example, many high-throughput technologies--e.g., next-generation sequencing--are being used today to generate lists of genes associated with specific conditions. However, their functional interpretation remains a challenge and many tools exist trying to characterize the function of gene-lists. Such systems rely typically in enrichment analysis and aim to give a quick insight into the underlying biology by presenting it in a form of a summary-report. While the load of annotation may be alleviated by such computational approaches, the main challenge in modern annotation remains to develop a systems form of analysis in which a pipeline can effectively analyze gene-lists quickly and identify aggregated annotations through computerized resources. In this article we survey some of the many such tools and methods that have been developed to automatically interpret the biological functions underlying gene-lists. We overview current functional annotation aspects from the perspective of their epistemology (i.e., the underlying theories used to organize information about gene function into a body of verified and documented knowledge) and find that most of the currently used functional annotation methods fall broadly into one of two categories: they are based either on 'known' formally-structured ontology annotations created by 'experts' (e.g., the GO terms used to describe the function of Entrez Gene entries), or--perhaps more adventurously--on annotations inferred from literature (e.g., many text-mining methods use computer-aided reasoning to acquire knowledge represented in natural languages). Overall however, deriving detailed and accurate insight from such gene lists remains a challenging task, and improved methods are called for. In particular, future methods need to (1) provide more holistic insight into the underlying molecular systems; (2) provide better follow-up experimental testing and treatment options, and (3) better manage gene lists derived from organisms that are not well-studied. We discuss some promising approaches that may help achieve these advances, especially the use of extended dictionaries of biomedical concepts and molecular mechanisms, as well as greater use of annotation benchmarks.
- Klíčová slova
- Benchmarks, Functional annotation, GO term enrichment, Keyword enhancement, Systems biology, Text mining,
- MeSH
- data mining metody trendy MeSH
- databáze genetické * trendy MeSH
- genová ontologie * trendy MeSH
- lidé MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
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.
This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.
- Klíčová slova
- biomedical semantics, electronic health records, formal ontologies, natural language processing, precision medicine, terminologies,
- Publikační typ
- časopisecké články MeSH
Human biomedical datasets that are critical for research and clinical studies to benefit human health also often contain sensitive or potentially identifying information of individual participants. Thus, care must be taken when they are processed and made available to comply with ethical and regulatory frameworks and informed consent data conditions. To enable and streamline data access for these biomedical datasets, the Global Alliance for Genomics and Health (GA4GH) Data Use and Researcher Identities (DURI) work stream developed and approved the Data Use Ontology (DUO) standard. DUO is a hierarchical vocabulary of human and machine-readable data use terms that consistently and unambiguously represents a dataset's allowable data uses. DUO has been implemented by major international stakeholders such as the Broad and Sanger Institutes and is currently used in annotation of over 200,000 datasets worldwide. Using DUO in data management and access facilitates researchers' discovery and access of relevant datasets. DUO annotations increase the FAIRness of datasets and support data linkages using common data use profiles when integrating the data for secondary analyses. DUO is implemented in the Web Ontology Language (OWL) and, to increase community awareness and engagement, hosted in an open, centralized GitHub repository. DUO, together with the GA4GH Passport standard, offers a new, efficient, and streamlined data authorization and access framework that has enabled increased sharing of biomedical datasets worldwide.
- Klíčová slova
- FAIR, GA4GH, automated data access, consent, controlled access, data access, data restrictions, ontology, secondary data use, standard,
- Publikační typ
- časopisecké články MeSH
In predators an ontogenetic trophic shift includes change from small to large prey of several different taxa. In myrmecophagous predators that are also mimics of ants, the ontogenetic trophic shift should be accompanied by a parallel mimetic change. Our aim was to test whether ant-eating jumping spider, Mexcala elegans, is myrmecomorphic throughout their ontogenetic development, and whether there is an ontogenetic shift in realised trophic niche and their mimetic models. We performed field observations on the association of Mexcala with ant species and investigated the natural prey of the ontogenetic classes by means of molecular methods. Then we measured the mimetic similarity of ontogenetic morphs to putative mimetic models. We found Mexcala is an inaccurate mimic of ants both in the juvenile and adult stages. During ontogenesis it shifts mimetic models. The mimetic similarity was rather superficial, so an average bird predator should distinguish spiders from ants based on colouration. The realised trophic niche was narrow, composed mainly of ants of different species. There was no significant difference in the prey composition between ontogenetic stages. Females were more stenophagous than juveniles. We conclude that Mexcala is an ant-eating specialist that reduces its prey spectrum and shifts ant models during ontogenesis.
- MeSH
- bio-ontologie MeSH
- biologická adaptace fyziologie MeSH
- biologická evoluce MeSH
- ekosystém MeSH
- Formicidae MeSH
- mimikry fyziologie MeSH
- pavouci metabolismus fyziologie MeSH
- predátorské chování fyziologie MeSH
- selekce (genetika) genetika MeSH
- zvířata MeSH
- Check Tag
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
To improve patient outcomes after trauma, the need to decrypt the post-traumatic immune response has been identified. One prerequisite to drive advancement in understanding that domain is the implementation of surgical biobanks. This paper focuses on the outcomes of patients with one of two diagnoses: post-traumatic arthritis and osteomyelitis. In creating surgical biobanks, currently, many obstacles must be overcome. Roadblocks exist around scoping of data that is to be collected, and the semantic integration of these data. In this paper, the generic component model and the Semantic Web technology stack are used to solve issues related to data integration. The results are twofold: (a) a scoping analysis of data and the ontologies required to harmonize and integrate it, and (b) resolution of common data integration issues in integrating data relevant to trauma surgery.
- Klíčová slova
- biomedical ontologies, knowledge representation, osteomyelitis, post-traumatic arthritis, semantic data integration, surgical biobank, system theory,
- Publikační typ
- časopisecké články MeSH
Macromolecular complexes are essential functional units in nearly all cellular processes, and their atomic-level understanding is critical for elucidating and modulating molecular mechanisms. The Protein Data Bank (PDB) serves as the global repository for experimentally determined structures of macromolecules. Structural data in the PDB offer valuable insights into the dynamics, conformation, and functional states of biological assemblies. However, the current annotation practices lack standardised naming conventions for assemblies in the PDB, complicating the identification of instances representing the same assembly. In this study, we introduce a method leveraging resources external to PDB, such as the Complex Portal, UniProt and Gene Ontology, to describe assemblies and contextualise them within their biological settings accurately. Employing the proposed approach, we assigned standard names to over 90% of unique assemblies in the PDB and provided persistent identifiers for each assembly. This standardisation of assembly data enhances the PDB, facilitating a deeper understanding of macromolecular complexes. Furthermore, the data standardisation improves the PDB's FAIR attributes, fostering more effective basic and translational research and scientific education.
