National Center for Biomedical Ontology: advancing biomedicine through structured organization of scientific knowledge

• Published in: Omics (Larchmont, N.Y.) Vol. 10; no. 2; p. 185
• Main Authors: Rubin, Daniel L, Lewis, Suzanna E, Mungall, Chris J, Misra, Sima, Westerfield, Monte, Ashburner, Michael, Sim, Ida, Chute, Christopher G, Solbrig, Harold, Storey, Margaret-Anne, Smith, Barry, Day-Richter, John, Noy, Natalya F, Musen, Mark A
• Format: Journal Article
• Language: English
• Published: United States 01.06.2006
• Subjects: Biomedical Research - standards; Internet; National Institutes of Health (U.S.); Semantics; Software; United States; United States
• ISSN: 1536-2310
• DOI: 10.1089/omi.2006.10.185

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease.