13,188 research outputs found
In Silico Approaches and the Role of Ontologies in Aging Research
The 2013 Rostock Symposium on Systems Biology and Bioinformatics in Aging Research was again dedicated to dissecting the aging process using in silico means. A particular focus was on ontologies, as these are a key technology to systematically integrate heterogeneous information about the aging process. Related topics were databases and data integration. Other talks tackled modeling issues and applications, the latter including talks focussed on marker development and cellular stress as well as on diseases, in particular on diseases of kidney and skin
Infectious Disease Ontology
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
Integrating Genomic Knowledge Sources through an Anatomy Ontology
Modern genomic research has access to a plethora of knowledge sources. Often, it is imperative that researchers combine and integrate knowledge from multiple perspectives. Although some technology exists for connecting data and knowledge bases, these methods are only just begin-ning to be successfully applied to research in modern cell biology. In this paper, we argue that one way to integrate multiple knowledge sources is through anatomy—both generic cellular anatomy, as well as anatomic knowledge about the tissues and organs that may be studied via microarray gene expression experiments. We present two examples where we have combined a large ontology of human anatomy (the FMA) with other genomic knowledge sources: the gene ontology (GO) and the mouse genomic databases (MGD) of the Jackson Labs. These two initial examples of knowledge integration provide a proof of concept that anatomy can act as a hub through which we can usefully combine a variety of genomic knowledge and data
The Requirements for Ontologies in Medical Data Integration: A Case Study
Evidence-based medicine is critically dependent on three sources of
information: a medical knowledge base, the patients medical record and
knowledge of available resources, including where appropriate, clinical
protocols. Patient data is often scattered in a variety of databases and may,
in a distributed model, be held across several disparate repositories.
Consequently addressing the needs of an evidence-based medicine community
presents issues of biomedical data integration, clinical interpretation and
knowledge management. This paper outlines how the Health-e-Child project has
approached the challenge of requirements specification for (bio-) medical data
integration, from the level of cellular data, through disease to that of
patient and population. The approach is illuminated through the requirements
elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three
diseases being studied in the EC-funded Health-e-Child project.Comment: 6 pages, 1 figure. Presented at the 11th International Database
Engineering & Applications Symposium (Ideas2007). Banff, Canada September
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The Infectious Disease Ontology in the Age of COVID-19
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
Constructing a lattice of Infectious Disease Ontologies from a Staphylococcus aureus isolate repository
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
Analysis of the human diseasome reveals phenotype modules across common, genetic, and infectious diseases
Phenotypes are the observable characteristics of an organism arising from its
response to the environment. Phenotypes associated with engineered and natural
genetic variation are widely recorded using phenotype ontologies in model
organisms, as are signs and symptoms of human Mendelian diseases in databases
such as OMIM and Orphanet. Exploiting these resources, several computational
methods have been developed for integration and analysis of phenotype data to
identify the genetic etiology of diseases or suggest plausible interventions. A
similar resource would be highly useful not only for rare and Mendelian
diseases, but also for common, complex and infectious diseases. We apply a
semantic text- mining approach to identify the phenotypes (signs and symptoms)
associated with over 8,000 diseases. We demonstrate that our method generates
phenotypes that correctly identify known disease-associated genes in mice and
humans with high accuracy. Using a phenotypic similarity measure, we generate a
human disease network in which diseases that share signs and symptoms cluster
together, and we use this network to identify phenotypic disease modules
Interdisciplinary perspectives on the development, integration and application of cognitive ontologies
We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data
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