Big biomedical data as the key resource for discovery science.
Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including i...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 22; no. 6; pp. 1126 - 1132 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Nov2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110875721&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110875721 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Nov2015 vid: 22 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 110875721 110875721 NLM26198305 10.1093/jamia/ocv077 NLM26198305 PMC5009918 [Available on 11/01/16] 110875721 ppf: 1126 ppct: 6 formats: tig: atl: Big biomedical data as the key resource for discovery science. aug: au: Toga, Arthur W. Foster, Ian Kesselman, Carl Madduri, Ravi Chard, Kyle Deutsch, Eric W. Price, Nathan D. Glusman, Gustavo Heavner, Benjamin D. Dinov, Ivo D. Ames, Joseph Van Horn, John Kramer, Roger Hood, Leroy affil: Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA sug: subj: Data Collection Research, Medical Neurosciences United States National Institutes of Health (U.S.) Scales ab: Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an "-ome to home" approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center's computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson's and Alzheimer's. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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