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dc.contributor.authorMcDonald, John F.
dc.date.accessioned2019-06-27T15:13:47Z
dc.date.available2019-06-27T15:13:47Z
dc.date.issued2019-06-12
dc.identifier.urihttp://hdl.handle.net/1853/61462
dc.descriptionPresented on June 12, 2019 at 10:00 a.m. in the Georgia Tech Hotel and Conference Center, Georgia Institute of Technology.en_US
dc.descriptionThe second-annual Machine Learning in Science and Engineering (MLSE) Conference highlights advances in research that utilize methods of artificial intelligence, the development of new machine learning algorithms designed for science and engineering problems, and the ways these methods lead to innovations across various fields. Researchers from academia, government, and industry will gather to explore the future of research in science and engineering.en_US
dc.descriptionPLENARY TALK - John F. McDonald is a professor in the School of Biological Sciences at Georgia Tech, the director of the Integrated Cancer Research Center, and chief scientific officer of the Ovarian Cancer Institute. His research lab takes an integrated systems approach to the study of cancer. They view cancer not as a defect in any particular gene or protein, but as a de-regulated cellular/inter-cellular process. An understanding of such complex processes requires the implementation of experimental approaches that can provide an integrative holistic or “systems” view of intra-and inter-cellular process. They employ a number high-throughput genomic (e.g., DNA-seq, RNA-seq, microarray) technologies to gather systems data on the status of cancer cells. Additionally, they strive to integrate the exceptional strengths that exist at Georgia Tech in the fields of engineering and the computational sciences.en_US
dc.descriptionRuntime: 61:48 minutesen_US
dc.format.extent61:48 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesIDEaS Conferences ; Machine Learning in Science and Engineeringen_US
dc.subjectDiagnosticsen_US
dc.subjectMachine learningen_US
dc.titleThe Potential of Machine Learning for Improved Diagnostics and Treatmenten_US
dc.title.alternativeMachine Learning in Science and Engineering Conference - The Potential of Machine Learning for Improved Diagnostics and Treatmenten_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Data Engineering and Scienceen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Biological Sciencesen_US


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