UMBC ebiquity

Genetic information for chronic desease prediction

Authors: Michael A. Grasso, Darshana Dalvi, Sanmay Das, Vladimir Korolev, and Yelena Yesha

Book Title: Bioinformatics and Biomedicine Workshops (BIBMW)

Date: November 30, 2012

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.

Type: InProceedings

Publisher: IEEE

Tags: learning, personalized medicine

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