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 (...) bearer. A definition of ‘protective resistance’ is proposed for use in the Infectious Disease Ontology (IDO) and we show how this definition can be used to characterize the antibiotic resistance in Methicillin-Resistant Staphylococcus aureus (MRSA). The ontological relations between entities in our MRSA case study are used alongside a series of logical inference rules to illustrate logical reasoning about resistance. A description logic representation of blocking dispositions is also provided. We demonstrate that our characterization of resistance is sufficiently general to cover two other cases of resistance in the infectious disease domain involving HIV and malaria. (shrink)

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 (...) information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain. (shrink)

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 (...) present applications of IDO Core within various areas of infectious disease research, together with an overview of all IDO extension ontologies and the methodology on the basis of which they are built. We also survey recent developments involving IDO, including the creation of IDO Virus; the Coronaviruses Infectious Disease Ontology (CIDO); and an extension of CIDO focused on COVID-19 (IDO-CovID-19).We also discuss how these ontologies might assist in information-driven efforts to deal with the ongoing COVID-19 pandemic, to accelerate data discovery in the early stages of future pandemics, and to promote reproducibility of infectious disease research. (shrink)

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 (...) along with a specific use case in IDO. Description logic restrictions are used to formalize statements relating to these combinations. (shrink)

Recent increases in the volume and diversity of life science data and information and an increasing emphasis on data sharing and interoperability have resulted in the creation of a large number of biological ontologies, including the Cell Ontology (CL), designed to provide a standardized representation of cell types for data annotation. Ontologies have been shown to have significant benefits for computational analyses of large data sets and for automated reasoning applications, leading to organized attempts to improve the structure and formal (...) rigor of ontologies to better support computation. Currently, the CL employs multiple is_a relations, defining cell types in terms of histological, functional, and lineage properties, and the majority of definitions are written with sufficient generality to hold across multiple species. This approach limits the CL's utility for computation and for cross-species data integration. Results: To enhance the CL's utility for computational analyses, we developed a method for the ontological representation of cells and applied this method to develop a dendritic cell ontology (DC-CL). DC-CL subtypes are delineated on the basis of surface protein expression, systematically including both species-general and species-specific types and optimizing DC-CL for the analysis of flow cytometry data. We avoid multiple uses of is_a by linking DC-CL terms to terms in other ontologies via additional, formally defined relations such as has_function. This approach brings benefits in the form of increased accuracy, support for reasoning, and interoperability with other ontology resources. Accordingly, we propose our method as a general strategy for the ontological representation of cells. DC-CL is available from http://www.obofoundry.org. (shrink)

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 (...) taxonomy of infectious disease incorporating genetic, molecular, and clinical data. We also outline how faceted browsing and query of annotated data is supported using a lattice application ontology. (shrink)

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 (...) data analysis and inference. To address these problems, the community-based Vaccine Ontology (VO) has been developed through collaboration with vaccine researchers and many national and international centers and programs, including the National Center for Biomedical Ontology (NCBO), the Infectious Disease Ontology (IDO) Initiative, and the Ontology for Biomedical Investigations (OBI). VO utilizes the Basic Formal Ontology (BFO) as the top ontology and the Relation Ontology (RO) for definition of term relationships. VO is represented in the Web Ontology Language (OWL) and edited using the Protégé-OWL. Currently VO contains more than 2000 terms and relationships. VO emphasizes on classification of vaccines and vaccine components, vaccine quality and phenotypes, and host immune response to vaccines. These reflect different aspects of vaccine composition and biology and can thus be used to model individual vaccines. More than 200 licensed vaccines and many vaccine candidates in research or clinical trials have been modeled in VO. VO is being used for vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). Multiple VO applications will be presented. (shrink)

Rapidly, accurately and easily interpreting generated data is of fundamental concern. Ontologies – structured controlled vocabularies – support interoperability and prevent the development of data silos which undermine interoperability. The Open Biological and Biomedical Ontologies (OBO) Foundry serves to ensure ontologies remain interoperable through adherence by its members to core ontology design principles. For example, the Infectious Disease Ontology (IDO) Core includes terminological content common to investigations of all infectious diseases. Ontologies covering more specific infectious diseases in turn extend from (...) IDOCore, such as the Coronavirus Infectious Disease Ontology (CIDO). The growing list of virus-specific IDO extensions has motivated construction of a reference ontology covering content common to viral infectious disease investigations: the Virus Infectious Disease Ontology (VIDO). Additionally the present pandemic has motivated construction of a more specific extension of CIDO covering terminological contents specific to the pandemic: the COVID-19 Infectious Disease Ontology (IDO-COVID-19). We report here the development of VIDO and IDO-COVID-19. More specifically we examine newly minted terms for each ontology, showcase reuse of terms from existing OBO ontologies, motivate choicepoints for ontological decisions based on research from relevant life sciences, apply ontology terms to explicate viral pathogenesis, and discuss the annotating power of virus ontologies for use in machine-learning projects. (shrink)

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.

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.