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Machine learning identifies SNPs predictive of advanced coronary artery calcium in ClinSeq® and Framingham Heart Study cohorts
Cihan Oguz, Shurjo K Sen, Adam R Davis, Yi-Ping Fu, Christopher J O’Donnell, Gary H Gibbons
doi: https://doi.org/10.1101/102350
Cihan Oguz
1Cardiovascular Disease Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
Shurjo K Sen
1Cardiovascular Disease Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
Adam R Davis
1Cardiovascular Disease Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
Yi-Ping Fu
2Office of Biostatistics Research, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
3Framingham Heart Study, Boston University School of Medicine, Boston, MA, USA
Christopher J O’Donnell
3Framingham Heart Study, Boston University School of Medicine, Boston, MA, USA
4Center for Population Genomics, MAVERIC, VA Healthcare System, Boston, MA, USA
5Cardiology Section Administration, VA Healthcare System, Boston, MA, USA
6Department of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Gary H Gibbons
1Cardiovascular Disease Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
7Office of the Director, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
Article usage
Posted January 24, 2017.
Machine learning identifies SNPs predictive of advanced coronary artery calcium in ClinSeq® and Framingham Heart Study cohorts
Cihan Oguz, Shurjo K Sen, Adam R Davis, Yi-Ping Fu, Christopher J O’Donnell, Gary H Gibbons
bioRxiv 102350; doi: https://doi.org/10.1101/102350
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