11 found
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  1. Infectious Disease Ontology.Lindsay Grey Cowell & Barry Smith - 2009 - In Infectious Disease Informatics. New York: Springer New York. pp. 373-395.
    Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and (...)
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    Coordinating virus research: The Virus Infectious Disease Ontology.John Beverley, Shane Babcock, Gustavo Carvalho, Lindsay G. Cowell, Sebastian Duesing, Yongqun He, Regina Hurley, Eric Merrell, Richard H. Scheuermann & Barry Smith - 2024 - PLoS ONE 1.
    The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies––structured, controlled, vocabularies––are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (...)
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  3. The Infectious Disease Ontology in the Age of COVID-19.Shane Babcock, Lindsay G. Cowell, John Beverley & Barry Smith - 2021 - Journal of Biomedical Semantics 12 (13).
    The Infectious Disease Ontology (IDO) is a suite of interoperable ontology modules that aims to provide coverage of all aspects of the infectious disease domain, including biomedical research, clinical care, and public health. IDO Core is designed to be a disease and pathogen neutral ontology, covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is then extended by a collection of ontology modules focusing on specific diseases and pathogens. In this paper we (...)
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  4. Towards an Ontological Representation of Resistance: The Case of MRSA.Albert Goldfain, Barry Smith & Lindsay G. Cowell - 2011 - Journal of Biomedical Informatics 44 (1):35-41.
    This paper addresses a family of issues surrounding the biological phenomenon of resistance and its representation in realist ontologies. The treatments of resistance terms in various existing ontologies are examined and found to be either overly narrow, internally inconsistent, or otherwise problematic. We propose a more coherent characterization of resistance in terms of what we shall call blocking dispositions, which are collections of mutually coordinated dispositions which are of such a sort that they cannot undergo simultaneous realization within a single (...)
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  5. Dispositions and the Infectious Disease Ontology.Albert Goldfain, Barry Smith & Lindsay Cowell - 2010 - In Formal Ontology in Information Systems: Proceedings of the Sixth International Conference (FOIS). IOS Press. pp. 400-413.
    This paper addresses the use of dispositions in the Infectious Disease Ontology (IDO). IDO is an ontology constructed according to the principles of the Open Biomedical Ontology (OBO) Foundry and uses the Basic Formal Ontology (BFO) as an upper ontology. After providing a brief introduction to disposition types in BFO and IDO, we discuss three general techniques for representing combinations of dispositions under the headings blocking dispositions, complementary dispositions, and collective dispositions. Motivating examples for each combination of dispositions is given (...)
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  6. An improved ontological representation of dendritic cells as a paradigm for all cell types.Anna Maria Masci, Cecilia N. Arighi, Alexander D. Diehl, Anne E. Liebermann, Chris Mungall, Richard H. Scheuermann, Barry Smith & Lindsay Cowell - 2009 - BMC Bioinformatics 10 (1):70.
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  7. Constructing a lattice of Infectious Disease Ontologies from a Staphylococcus aureus isolate repository.Albert Goldfain, Lindsay G. Cowell & Barry Smith - 2012 - In Goldfain Albert, Cowell Lindsay G. & Smith Barry (eds.), Proceeedings of the Third International Conference on Biomedical Ontology (CEUR 897).
    A repository of clinically associated Staphylococcus aureus (Sa) isolates is used to semi‐automatically generate a set of application ontologies for specific subfamilies of Sa‐related disease. Each such application ontology is compatible with the Infectious Disease Ontology (IDO) and uses resources from the Open Biomedical Ontology (OBO) Foundry. The set of application ontologies forms a lattice structure beneath the IDO‐Core and IDO‐extension reference ontologies. We show how this lattice can be used to define a strategy for the construction of a new (...)
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  8. VO: Vaccine Ontology.Yongqun He, Lindsay Cowell, Alexander D. Diehl, H. L. Mobley, Bjoern Peters, Alan Ruttenberg, Richard H. Scheuermann, Ryan R. Brinkman, Melanie Courtot, Chris Mungall, Barry Smith & Others - 2009 - In ICBO 2009: Proceedings of the First International Conference on Biomedical Ontology. Buffalo:
    Vaccine research, as well as the development, testing, clinical trials, and commercial uses of vaccines involve complex processes with various biological data that include gene and protein expression, analysis of molecular and cellular interactions, study of tissue and whole body responses, and extensive epidemiological modeling. Although many data resources are available to meet different aspects of vaccine needs, it remains a challenge how we are to standardize vaccine annotation, integrate data about varied vaccine types and resources, and support advanced vaccine (...)
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  9. Coordinating Coronavirus Research: The COVID-19 Infectious Disease Ontology.John Beverley, Shane Babcock, Barry Smith, Yongqun He, Eric Merrell, Lindsay Cowell, Regina Hurley & Sebastian Duesing - 2022 - Proceedings of the International Conference on Biomedical Ontologies.
    The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Ontologies – structured, controlled, vocabularies – are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the virus research domain, following the principles of the Open Biological and Biomedical Ontology (OBO) Foundry and drawing on the resources of the Infectious Disease Ontology (IDO) Core. We report the development of the Virus Infectious (...)
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  10. Clonal complexes in biomedical ontologies.Albert Goldfain, Lindsay Cowell & Barry Smith - 2009 - In ICBO 2009: Proceedings of the First International Conference on Biomedical Ontology. pp. 168.
    An accurate classification of bacteria is essential for the proper identification of patient infections and subsequent treatment decisions. Multi-Locus Sequence Typing (MLST) is a genetic technique for bacterial classification. MLST classifications are used to cluster bacteria into clonal complexes. Importantly, clonal complexes can serve as a biological species concept for bacteria, facilitating an otherwise difficult taxonomic classification. In this paper, we argue for the inclusion of terms relating to clonal complexes in biomedical ontologies.
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  11. Ontological representation of CDC Active Bacterial Core Surveillance Case Reports.Albert Goldfain, Barry Smith & Lindsay G. Cowell - 2014 - Proceedings of the Fifth International Conference on Biomedical Ontology 1327:74-77.
    The Center for Disease Control and Prevention’s Active Bacterial Core Surveillance (CDC ABCs) Program is a collaborative effort betweeen the CDC, state health departments, laboratories, and universities to track invasive bacterial pathogens of particular importance to public health [1]. The year-end surveillance reports produced by this program help to shape public policy and coordinate responses to emerging infectious diseases over time. The ABCs case report form (CRF) data represents an excellent opportunity for data reuse beyond the original surveillance purposes.
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