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Suzanna E. Lewis [5]Suzanna Lewis [1]
  1.  77
    The OBO Foundry: Coordinated Evolution of Ontologies to Support Biomedical Data Integration.Barry Smith, Michael Ashburner, Cornelius Rosse, Jonathan Bard, William Bug, Werner Ceusters, Louis J. Goldberg, Karen Eilbeck, Amelia Ireland, Christopher J. Mungall, Neocles Leontis, Alan Ruttenberg, Susanna-Assunta Sansone, Richard H. Scheuermann, Nigam Shah, Patricia L. Whetzel & Suzanna Lewis - 2007 - Nature Biotechnology 25 (11):1251--1255.
    The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or ‘ontologies’. Unfortunately, the very success of this approach has led to a proliferation of ontologies which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium has set in train a strategy to overcome this problem. Existing (...)
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  2. Promoting Coherent Minimum Reporting Guidelines for Biological and Biomedical Investigations: The MIBBI Project.Chris F. Taylor, Dawn Field, Susanna-Assunta Sansone, Jan Aerts, Rolf Apweiler, Michael Ashburner, Catherine A. Ball, Pierre-Alain Binz, Molly Bogue, Tim Booth, Alvis Brazma, Ryan R. Brinkman, Adam Michael Clark, Eric W. Deutsch, Oliver Fiehn, Jennifer Fostel, Peter Ghazal, Frank Gibson, Tanya Gray, Graeme Grimes, John M. Hancock, Nigel W. Hardy, Henning Hermjakob, Randall K. Julian, Matthew Kane, Carsten Kettner, Christopher Kinsinger, Eugene Kolker, Martin Kuiper, Nicolas Le Novere, Jim Leebens-Mack, Suzanna E. Lewis, Phillip Lord, Ann-Marie Mallon, Nishanth Marthandan, Hiroshi Masuya, Ruth McNally, Alexander Mehrle, Norman Morrison, Sandra Orchard, John Quackenbush, James M. Reecy, Donald G. Robertson, Philippe Rocca-Serra, Henry Rodriguez, Heiko Rosenfelder, Javier Santoyo-Lopez, Richard H. Scheuermann, Daniel Schober, Barry Smith & Jason Snape - 2008 - Nature Biotechnology 26 (8):889-896.
    Throughout the biological and biomedical sciences there is a growing need for, prescriptive ‘minimum information’ (MI) checklists specifying the key information to include when reporting experimental results are beginning to find favor with experimentalists, analysts, publishers and funders alike. Such checklists aim to ensure that methods, data, analyses and results are described to a level sufficient to support the unambiguous interpretation, sophisticated search, reanalysis and experimental corroboration and reuse of data sets, facilitating the extraction of maximum value from data sets (...)
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  3. The Environment Ontology: Contextualising Biological and Biomedical Entities.Pier Luigi Buttigieg, Norman Morrison, Barry Smith, Christopher J. Mungall & Suzanna E. Lewis - 2013 - Journal of Biomedical Semantics 4 (43):1-9.
    As biological and biomedical research increasingly reference the environmental context of the biological entities under study, the need for formalisation and standardisation of environment descriptors is growing. The Environment Ontology (ENVO) is a community-led, open project which seeks to provide an ontology for specifying a wide range of environments relevant to multiple life science disciplines and, through an open participation model, to accommodate the terminological requirements of all those needing to annotate data using ontology classes. This paper summarises ENVO’s motivation, (...)
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  4. National Center for Biomedical Ontology: Advancing Biomedicine Through Structured Organization of Scientific Knowledge.Daniel L. Rubin, Suzanna E. Lewis, Chris J. Mungall, Misra Sima, Westerfield Monte, Ashburner Michael, Christopher G. Chute, Ida Sim, Harold Solbrig, M. A. Storey, Barry Smith, John D. Richter, Natasha Noy & Mark A. Musen - 2006 - Omics: A Journal of Integrative Biology 10 (2):185-198.
    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 (...)
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  5.  84
    OBO Foundry in 2021: Operationalizing Open Data Principles to Evaluate Ontologies.Rebecca C. Jackson, Nicolas Matentzoglu, James A. Overton, Randi Vita, James P. Balhoff, Pier Luigi Buttigieg, Seth Carbon, Melanie Courtot, Alexander D. Diehl, Damion Dooley, William Duncan, Nomi L. Harris, Melissa A. Haendel, Suzanna E. Lewis, Darren A. Natale, David Osumi-Sutherland, Alan Ruttenberg, Lynn M. Schriml, Barry Smith, Christian J. Stoeckert, Nicole A. Vasilevsky, Ramona L. Walls, Jie Zheng, Christopher J. Mungall & Bjoern Peters - 2021 - BioaRxiv.
    Biological ontologies are used to organize, curate, and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies Foundry was created to address this by facilitating the development, harmonization, application, and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the (...)
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  6. Finding Our Way Through Phenotypes.Andrew R. Deans, Suzanna E. Lewis, Eva Huala, Salvatore S. Anzaldo, Michael Ashburner, James P. Balhoff, David C. Blackburn, Judith A. Blake, J. Gordon Burleigh, Bruno Chanet, Laurel D. Cooper, Mélanie Courtot, Sándor Csösz, Hong Cui, Barry Smith & Others - 2015 - PLoS Biol 13 (1):e1002033.
    Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that (...)
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