• 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