DocumentCode :
2767554
Title :
Genetic information for chronic disease prediction
Author :
Grasso, Michael A. ; Dalvi, Darshana ; Das, Soma ; Gately, Matthew ; Korolev, Vlad ; Yesha, Yelena
Author_Institution :
Dept. of Emergency Med., Univ. of Maryland, Baltimore, MD, USA
fYear :
2011
fDate :
12-15 Nov. 2011
Firstpage :
997
Lastpage :
997
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.
Keywords :
bioinformatics; diseases; genetics; Type 2 diabetes; chronic disease prediction; coronary artery disease; disease prevention; disease treatment; genetic information; genetic testing; morbidity; mortality; polygenic disease; Accuracy; Arteries; Diabetes; Diseases; Educational institutions; Genetics; Machine learning; coronary artery disease; genetics; machine learning; type 2 diabetes mellitus;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine Workshops (BIBMW), 2011 IEEE International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4577-1612-6
Type :
conf
DOI :
10.1109/BIBMW.2011.6112535
Filename :
6112535
Link To Document :
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