The center for causal discovery of biomedical knowledge from big data.
The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scient...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 22; no. 6; pp. 1132 - 1137 |
|---|---|
| Autores principales: | , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Nov2015
|
| 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=110875722&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110875722 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: 110875722 110875722 NLM26138794 10.1093/jamia/ocv059 NLM26138794 PMC5009908 [Available on 11/01/16] 110875722 ppf: 1132 ppct: 5 formats: tig: atl: The center for causal discovery of biomedical knowledge from big data. aug: au: Cooper, Gregory F. Bahar, Ivet Becich, Michael J. Benos, Panayiotis V. Berg, Jeremy Espino, Jeremy U. Glymour, Clark Jacobson, Rebecca Crowley Kienholz, Michelle Lee, Adrian V. Xinghua Lu Scheines, Richard Lu, Xinghua affil: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA sug: subj: Research, Medical Data Collection Algorithms United States Clinical Assessment Tools ab: The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|