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