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2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) (2011)
Atlanta, Georgia USA
Nov. 12, 2011 to Nov. 15, 2011
ISBN: 978-1-4577-1612-6
pp: 997
Michael A. Grasso , Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, MD 21201
Darshana Dalvi , Department of Computer Science and EE, University of Maryland Baltimore County, Baltimore, MD 21250
Soma Das , Department of Computer Science and EE, University of Maryland Baltimore County, Baltimore, MD 21250
Matthew Gately , Department of Computer Science and EE, University of Maryland Baltimore County, Baltimore, MD 21250
Vlad Korolev , Department of Computer Science and EE, University of Maryland Baltimore County, Baltimore, MD 21250
Yelena Yesha , Department of Computer Science and EE, University of Maryland Baltimore County, Baltimore, MD 21250
ABSTRACT
Type 2 diabetes and coronary artery disease are commonly occurring polygenic diseases, which are responsible for significant morbidity and mortality. The identification of people at risk for these conditions has historically been based on clinical factors alone. Advances in genetics have raised the hope that genetic testing may aid in disease prediction, treatment, and prevention. Although intuitive, the addition of genetic information to increase the accuracy of disease prediction remains an unproven hypothesis. We present an overview of genetic issues involved in polygenic diseases, and summarize ongoing efforts to use this information for disease prediction.
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CITATION

V. Korolev, Y. Yesha, M. A. Grasso, D. Dalvi, S. Das and M. Gately, "Genetic information for chronic disease prediction," 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), Atlanta, Georgia USA, 2011, pp. 997.
doi:10.1109/BIBMW.2011.6112535
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