Infectious Disease Ontology

Cowell, Lindsay G.; Smith, Barry

doi:10.1007/978-1-4419-1327-2_19

Cited by 83 publications

(60 citation statements)

References 60 publications

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“…The existence of the ontology portals [14][15][16][17] where the terminology from different research fields is collected in the form of ontology is just one of the assumptions to successfully use the terminology. The outcome of the survey highlighted that many laboratories use the standards developed just in the particular laboratory.…”

Section: Resultsmentioning

confidence: 99%

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BioWes – From Design of Experiment, through Protocol to Repository, Control, Standardization and Back-Tracking

Bárta

Císař

Soloviov

et al. 2015

Bioinformatics and Biomedical Engineering

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Background:One of the main challenges in modern science is the amount of data produced by the experimental work; it is difficult to store, organize and share the scientific data and to extract the wealth of knowledge. Experimental method descriptions in scientific publications are often incomplete, which complicates experimental reproducibility. The proposed system was created in order to address these issues. It provides a solution for management of the experimental data and metadata to support the reproducibility. Implementation:The system is implemented as a repository for experiment descriptions and experimental data. It has three main entry points: desktop application for protocol design and data processing, web interface dedicated for protocol and data management, and web-based interface for mobile devices suitable for the field experiments. The functionality of desktop client can be extended using the custom plug-ins for data extraction and data processing. The system provides several methods to support experimental reproducibility: standardized terminology support, data and metadata at a single location, standardized protocol design or protocol evolution. Results and discussion:The system was tested in the framework of international infrastructure project AQUAEXCEL with five pilot installations at different institutes. The general testing in Tissue culture certified laboratory, Institute of complex systems and IFREMER verified the usability under different research infrastructures. The specific testing focused on the data processing modules and plug-ins demonstrated the modularity of the system for the specific conditions. The BioWes system represents experimental data as black box and therefore can handle any data type so as to provide broad usability for a variety of experiments and provide the data management infrastructure to improve the reproducibility and data sharing. Conclusions:The proposed system provides the tools for standard data management operations and extends the support by the standardization possibilities, protocol evolution with visualization features and modularity based on the data processing modules and device communication plug-ins. The software can be used at different organization levels: from a single researcher (to improve data organization) to research consortium through the central protocols management repository. Support from the Cisar et al. BioMed Eng OnLine 2016, 15(Suppl 1):S74 DOI 10.1186 BioMedical Engineering OnLine protocol design until being shared with the standardization features helps to improve the reproducibility of research work. The platform provides support from experimental protocol design to cooperation using simple sharing. RESEARCH

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Section: Resultsmentioning

confidence: 99%

“…Therefore standardization is hidden to the user or it is offered as a list of standardized terms based on the standardization provided by one of the standardization portals [14][15][16][17].…”

Section: Standardizationmentioning

confidence: 99%

BioWes – From Design of Experiment, through Protocol to Repository, Control, Standardization and Back-Tracking

Bárta

Císař

Soloviov

et al. 2015

Bioinformatics and Biomedical Engineering

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“…For DOLCE we select the following ontologies: DMOP.owl, which is an ontology about data mining optimisation [7], the Naive animal ontology2.owl ontology of animals 3 , OntoDerm 5.3.owl about dermatology [4], the SceneOntology.owl ontology of spatial scenes and objects specifically for visual recognition 4 , and SEGOv3.owl for describing relations between geographic occurrences and properties observed by sensors 5 . For BFO, we select the following ontologies: bco.owl about biological collections 6 , the epidemiology ontology.owl for the epidemiology and pub-lic health domain 7 , ero.owl intended for representing biomedical research resources 8 , IDO.9.19.07.owl representing infectious diseases [3], the proper name on -tology v1.8.owl that contains proper names for re-use in biomedical ontologies 9 , and the SAO.owl ontology about subcellular anatomy of the nervous system [12]. For GFO as s O f , we select the following ontologies: the pid.owl about primary immunodeficiency diseases [1] and two biological core ontologies, gfo-bio.…”

Section: Methodsmentioning

confidence: 99%

Feasibility of Automated Foundational Ontology Interchangeability

Khan

Keet

2014

Lecture Notes in Computer Science

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Abstract. While a foundational ontology can solve interoperability issues among the domain ontologies aligned to it, multiple foundational ontologies have been developed. Thus, there are still interoperability issues among domain ontologies aligned to different foundational ontologies. Questions arise about the feasibility of linking one's ontology to multiple foundational ontologies to increase its potential for uptake. To answer this, we have developed the tool SUGOI, Software Used to Gain Ontology Interchangeability, which allows a user to interchange automatically a domain ontology among the DOLCE, BFO and GFO foundational ontologies. The success of swapping based on equivalence varies by source ontology, ranging from 2 to 82% and averaging at 36% for the ontologies included in the evaluation. This is due to differences in coverage, notably DOLCE's qualities and BFO and GFO's roles, and amount of mappings. SUGOI therefore also uses subsumption mappings so that every domain ontology can be interchanged, preserves the structure of the ontology, and increases its potential for usability.

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“…To facilitate integrative research in infectious diseases, a community-based Infectious Disease Ontology (IDO) has been developed [4]. IDO is aimed to include a suite of interoperable ontologies that jointly cover the entire infectious disease domain, spanning infectious disease specialties and the clinical care, public health, and biomedical research.…”

Section: Medical Microbiology and Diagnosismentioning

confidence: 99%

“…As a formal ontology developed using the Web Ontology Language (OWL) [6], IDOBRU provides both machine-readable and human-readable vocabulary and supports computer-assisted automated reasoning. For example, based on biological knowledge captured within the ontology, simple scripts can be developed to query IDOBRU and identify 229 Brucella virulence factors, 29 protective antigens, and one protein that is both virulence factor and protective antigen [4]. IDOBRU also includes different Brucella diagnostic methods such as specimen culturing, immunoassays, and Polymerase Chain Reaction (PCR) assays.…”

Section: Medical Microbiology and Diagnosismentioning

confidence: 99%