Ontologies and Data Management: A Brief Survey
Ontology highlight
ABSTRACT:
SUBMITTER: Schneider T
PROVIDER: S-EPMC7497697 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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Project description:Communication about the deactivation of implantable cardioverter-defibrillators (ICDs) in patients near the end of life is rare.To determine whether hospices are admitting patients with ICDs, whether such patients are receiving shocks, and how hospices manage ICDs.Cross-sectional survey.Randomly selected hospice facilities.900 hospices, 414 of which responded fully.Frequency of admission of patients with ICDs, frequency with which patients received shocks, existence of ICD deactivation policies, and frequency of deactivation.97% of hospices admitted patients with ICDs, and 58% reported that in the past year, a patient had been shocked. Only 10% of hospices had a policy that addressed deactivation. On average, 42% (95% CI, 37% to 48%) of patients with ICDs had the shocking function deactivated.The study relied on the knowledge of hospice administrators.Hospices are admitting patients with ICDs, and patients are being shocked at the end of life. Ensuring that hospices have policies in place to address deactivation may improve the care for patients with these devices. The authors provide a sample deactivation policy.National Institute of Aging and National Institute of Nursing Research.
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Project description:Systems biology requires not only genome-scale data but also methods to integrate these data into interpretable models. Previously, we developed approaches that organize omics data into a structured hierarchy of cellular components and pathways, called a "data-driven ontology." Such hierarchies recapitulate known cellular subsystems and discover new ones. To broadly facilitate this type of modeling, we report the development of a software library called the Data-Driven Ontology Toolkit (DDOT), consisting of a Python package (https://github.com/idekerlab/ddot) to assemble and analyze ontologies and a web application (http://hiview.ucsd.edu) to visualize them. Using DDOT, we programmatically assemble a compendium of ontologies for 652 diseases by integrating gene-disease mappings with a gene similarity network derived from omics data. For example, the ontology for Fanconi anemia describes known and novel disease mechanisms in its hierarchy of 194 genes and 74 subsystems. DDOT provides an easy interface to share ontologies online at the Network Data Exchange.
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Project description:Engineered nanomaterials (ENMs) are being developed to meet specific application needs in diverse domains across the engineering and biomedical sciences (e.g. drug delivery). However, accompanying the exciting proliferation of novel nanomaterials is a challenging race to understand and predict their possibly detrimental effects on human health and the environment. The eNanoMapper project (www.enanomapper.net) is creating a pan-European computational infrastructure for toxicological data management for ENMs, based on semantic web standards and ontologies. Here, we describe the development of the eNanoMapper ontology based on adopting and extending existing ontologies of relevance for the nanosafety domain. The resulting eNanoMapper ontology is available at http://purl.enanomapper.net/onto/enanomapper.owl. We aim to make the re-use of external ontology content seamless and thus we have developed a library to automate the extraction of subsets of ontology content and the assembly of the subsets into an integrated whole. The library is available (open source) at http://github.com/enanomapper/slimmer/. Finally, we give a comprehensive survey of the domain content and identify gap areas. ENM safety is at the boundary between engineering and the life sciences, and at the boundary between molecular granularity and bulk granularity. This creates challenges for the definition of key entities in the domain, which we also discuss.
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Project description:COMICS is an interactive and open-access web platform for integration and visualization of molecular expression data in anatomograms of zebrafish, carp, and mouse model systems. Anatomical ontologies are used to map omics data across experiments and between an experiment and a particular visualization in a data-dependent manner. COMICS is built on top of several existing resources. Zebrafish and mouse anatomical ontologies with their controlled vocabulary (CV) and defined hierarchy are used with the ontoCAT R package to aggregate data for comparison and visualization. Libraries from the QGIS geographical information system are used with the R packages "maps" and "maptools" to visualize and interact with molecular expression data in anatomical drawings of the model systems. COMICS allows users to upload their own data from omics experiments, using any gene or protein nomenclature they wish, as long as CV terms are used to define anatomical regions or developmental stages. Common nomenclatures such as the ZFIN gene names and UniProt accessions are provided additional support. COMICS can be used to generate publication-quality visualizations of gene and protein expression across experiments. Unlike previous tools that have used anatomical ontologies to interpret imaging data in several animal models, including zebrafish, COMICS is designed to take spatially resolved data generated by dissection or fractionation and display this data in visually clear anatomical representations rather than large data tables. COMICS is optimized for ease-of-use, with a minimalistic web interface and automatic selection of the appropriate visual representation depending on the input data.
| S-EPMC5772887 | biostudies-literature
Project description:BackgroundThe wealth of phenotypic descriptions documented in the published articles, monographs, and dissertations of phylogenetic systematics is traditionally reported in a free-text format, and it is therefore largely inaccessible for linkage to biological databases for genetics, development, and phenotypes, and difficult to manage for large-scale integrative work. The Phenoscape project aims to represent these complex and detailed descriptions with rich and formal semantics that are amenable to computation and integration with phenotype data from other fields of biology. This entails reconceptualizing the traditional free-text characters into the computable Entity-Quality (EQ) formalism using ontologies.Methodology/principal findingsWe used ontologies and the EQ formalism to curate a collection of 47 phylogenetic studies on ostariophysan fishes (including catfishes, characins, minnows, knifefishes) and their relatives with the goal of integrating these complex phenotype descriptions with information from an existing model organism database (zebrafish, http://zfin.org). We developed a curation workflow for the collection of character, taxonomic and specimen data from these publications. A total of 4,617 phenotypic characters (10,512 states) for 3,449 taxa, primarily species, were curated into EQ formalism (for a total of 12,861 EQ statements) using anatomical and taxonomic terms from teleost-specific ontologies (Teleost Anatomy Ontology and Teleost Taxonomy Ontology) in combination with terms from a quality ontology (Phenotype and Trait Ontology). Standards and guidelines for consistently and accurately representing phenotypes were developed in response to the challenges that were evident from two annotation experiments and from feedback from curators.Conclusions/significanceThe challenges we encountered and many of the curation standards and methods for improving consistency that we developed are generally applicable to any effort to represent phenotypes using ontologies. This is because an ontological representation of the detailed variations in phenotype, whether between mutant or wildtype, among individual humans, or across the diversity of species, requires a process by which a precise combination of terms from domain ontologies are selected and organized according to logical relations. The efficiencies that we have developed in this process will be useful for any attempt to annotate complex phenotypic descriptions using ontologies. We also discuss some ramifications of EQ representation for the domain of systematics.
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Project description:MOTIVATION:Over the past years, significant resources have been invested into formalizing biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns and encode domain background knowledge. The domain knowledge of biomedical ontologies may have also the potential to provide background knowledge for machine learning and predictive modelling. RESULTS:We use ontology-based machine learning methods to evaluate the contribution of formal axioms and ontology meta-data to the prediction of protein-protein interactions and gene-disease associations. We find that the background knowledge provided by the Gene Ontology and other ontologies significantly improves the performance of ontology-based prediction models through provision of domain-specific background knowledge. Furthermore, we find that the labels, synonyms and definitions in ontologies can also provide background knowledge that may be exploited for prediction. The axioms and meta-data of different ontologies contribute to improving data analysis in a context-specific manner. Our results have implications on the further development of formal knowledge bases and ontologies in the life sciences, in particular as machine learning methods are more frequently being applied. Our findings motivate the need for further development, and the systematic, application-driven evaluation and improvement, of formal axioms in ontologies. AVAILABILITY AND IMPLEMENTATION:https://github.com/bio-ontology-research-group/tsoe. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
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Project description:ObjectivesThere exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms "semantic integration" and "knowledge representation". This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs).MethodsWe employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles.ResultsWe demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT.ConclusionsWe conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.
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Project description:Background The recent pandemic by COVID-19 is a global threat to human health. The disease is caused by SARS-CoV-2 and the infection rate is increased more quickly than MERS and SARS as their rapid adaptation to varied climatic conditions through rapid mutations. It becomes more severe due to the lack of proper therapeutic drugs, insufficient diagnostic tool, scarcity of appropriate drug, life supporting medical facility and mostly lack of awareness. Therefore, preventive measure is one of the important strategies to control. In this context, herbal medicinal plants received a noticeable attention to treat COVID-19 in Indian subcontinent. Here, 44 Indian traditional plants have been discussed with their novel phytochemicals that prevent the novel corona virus. The basic of SARS-CoV-2, their common way of transmission including their effect on immune and nervous system have been discussed. We have analysed their mechanism of action against COVID-19 following in-silico analysis. Their probable mechanism and therapeutic approaches behind the activity of phytochemicals to stimulate immune response as well as inhibition of viral multiplication discussed rationally. Thus, mixtures of active secondary metabolites/phytochemicals are the only choice to prevent the disease in countries where vaccination will take long time due to overcrowded population density. Graphical abstract Image 1
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Project description:Background:Most users of unsupported Internet interventions visit that site only once, therefore there is a need to create interventions that can be offered as a single brief interaction with the user. Objective:The main goal of this study was to compare the effect of a one-session unsupported Internet intervention on participants' clinical symptoms (depressive and anxiety symptoms) and related variables (mood, confidence and motivation). Method:A total of 765 adults residing in the United States took part in a randomized controlled trial. Participants were randomly assigned to one of five brief plain text interventions lasting 5-10 min. The interventions designed to address depressive symptoms were: thoughts (increasing helpful thoughts), activities (increasing activity level), sleep hygiene, assertiveness (increasing assertiveness awareness), Own Methods (utilizing methods that were previously successful). They were followed-up one week after consenting. Results:A main effect of time was observed for both depression (F(1, 563) = 234.70, p < 0.001) and anxiety (F(1, 551) = 170.27, p < 0.001). In all cases, regardless of assigned condition and Major Depressive Episode status, mean scores on both positive outcomes (mood, confidence and motivation) and negative outcome scores (depression and anxiety) improved over time. Conclusions:Brief unsupported Internet interventions can improve depressive symptoms at one-week follow-up. Further outcome data and research implications will be discussed.
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Project description:The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.
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