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The National Center for Biomedical Ontology is a consortium of leading biologists, clinicians, informaticians, and ontologists who develop innovative technology and methods allowing scientists to create, disseminate, and manage biomedical information and knowledge in machine-processable form. Our visionis that all biomedical knowledge and data are disseminated on the Internet using principled ontologies, such that they are semantically interoperable and useful for improving biomedical science and clinical care. Our resources include the Open Biomedical Ontologies (OBO) library, the Open Biomedical Data (OBD) repositories, and tools for accessing and using this information in research. The Center collaborates with biomedical researchers conducting Driving Biological Projects to enable outside research and stimulate technology development in the Center. The Center undertakes outreach and educational activities (Biomedical Informatics Program) to train future researchers to use biomedical ontologies and related tools with the goal of enhancing scientific discovery.

The OBO Foundry is a collaborative experiment involving developers of science-based ontologies who are establishing a set of principles for ontology development with the goal of creating a suite of orthogonal interoperable reference ontologies in the biomedical domain. The groups developing ontologies who have expressed an interest in this goal are listed below, followed by other relevant efforts in this domain.

Welcome to the National Center for Biomedical Ontology's BioPortal. BioPortal is a Web-based application for accessing and sharing biomedical ontologies. New features in BioPortal 2.0 include: * Full ontology navigation using Flash visualization * Web-service access to BioPortal content and capabilities, which enables developers to use our BioPortal services in their tools. * Ability to add Marginal Notes to classes in BioPortal ontologies, a feature that enables the community to comment on ontologies and to discuss their contents * Ability to create Point to Point Mappings between concepts in different BioPortal ontologies * Bulk export of ontology-to-ontology mappings in RDF format * Navigation of multiple ontologies, which enables users to have several ontologies opened simultaneously in different tabs in the user interface * URIs for all ontology content, which enable developers to access and share BioPortal content from their applications * Improved support through Protégé for ontologies in OWL format

NCBO Seminar Series talks occur via the Web and teleconference at 10:00AM, Pacific time, the 1st and 3rd Wednesday of every month. This series aims to showcase new projects, technologies and ideas in biomedical ontology by featuring the work of a different collaborator each session. It is open to anyone interested, regardless of location or affiliation.

The prototype we describe is a biomedical knowledge base, constructed for a demonstration at Banff WWW2007 , that integrates 15 distinct data sources using currently available Semantic Web technologies such as the W3C standard Web Ontology Language [OWL] and Resource Description Framework [RDF]. This report outlines which resources were integrated, how the knowledge base was constructed using free and open source triple store technology, how it can be queried using the W3C Recommended RDF query language SPARQL [SPARQL], and what resources and inferences are involved in answering complex queries. While the utility of the knowledge base is illustrated by identifying a set of genes involved in Alzheimer's Disease, the approach described here can be applied to any use case that integrates data from multiple domains.

The aims of clinical and translational research are to achieve a better understanding of the pathogenesis of human disease in order to develop effective diagnostic, therapeutic and prevention strategies. Biomedical informatics can play an important role in supporting this research by facilitating the management, integration, analysis and exchange of data derived from and related to the research problems being studied. A key aspect of this support is to bring clarity, rigor and formalism to the representation of 1. disease initiation, progression, pathogenesis, signs, symptoms, assessments, clinical and laboratory findings, disease diagnosis, treatment, treatment response and outcome, and 2. the interrelations between these distinct entities both in patient management and in clinical research, thus allowing the data to be more readily retrievable and shareable, and more able to serve in the support of algorithmic reasoning.

The Biomedical Grid Terminology (BiomedGT) is an open, collaboratively developed terminology for translational research. BiomedGT builds on the strengths of the NCI Thesaurus, including concept orientation, description logic, and public accessibility. While the current terminology has been seeded with NCI Thesaurus content, it is being restructured to facilitate open content development. The goal is to evolve BiomedGT into a set of federated sub-terminologies, with content maintained by experts in the relevant research communities.

Drug Ontology is developed by the nosology project at Stanford Center for Biomedical Informatics Research. This project seeks to discover new therapeutic uses and adverse effects of drugs by finding diseases that have gene expression profiles similar to those of the known indications and adverse effects of drugs. The objectives of the Drug Ontology are: * defines a core set of concepts and relationships that allow us to integrate information from multiple sources, * provides classification services along multiple axes, * provides links to external sources so that data not in the ontology can be queried from these sources.

novo|seek is a search engine for biomedical literature in Medline

# The primary intention of creating aTags is not the categorization of the document, but the representation of the key facts inside the document. Key facts in the biomedical domain might be, for example, "Protein A interacts with protein B" or "Overexpression of protein A in tissue B is the cause of disease C". # An aTag is comprised of a set of associated entities. The size of the set is arbitrary, but will typically lie between 2 and 5 entities. For example, the fact "Protein A binds to protein B" can be represented with an aTag comprising of the three entities "Protein A", "Molecular interaction" and "Protein B". Similarly, the fact "Overexpression of protein A in tissue B is the cause of disease C" can be represented with an aTag comprising of the four entities "Overexpression", "Protein A", "Tissue B" and "Disease C". # Each document or database entry can be described with an arbitrary number of such aTags. Each aTag can be associated with the relevant portions of text or data in a fine granularity. # The entities in an aTag are not simple strings, but resources that are part of ontologies and RDF/OWL-enabled databases. For example, "Protein A" and "Protein B" are resources that are defined in the UniProt database, whereas "Molecular Interaction" is a class in the branch of biological processes of the Gene Ontology. They are identified with their URIs.