title: Predictive genomics of cardioembolic stroke.
abstract: Cardioembolic stroke is a complex disease resulting from the interaction of numerous factors. Using data from Genes Affecting Stroke Risk and Outcome Study (GASROS), we show that a multivariate predictive model built using Bayesian networks is able to achieve a predictive accuracy of 86% on the fitted values as computed by the area under the receiver operating characteristic curve relative to that of the individual single nucleotide polymorphism with the highest prognostic performance (area under the receiver operating characteristic curve=60%).
authors.affiliation: Harvard-Partners Center for Genetics and Genomics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA. marco_ramoni@harvard.edu
authors.forename: Rachel Badovinac
authors.initials: RB
authors.lastname: Ramoni
authors.forename: Blanca E
authors.initials: BE
authors.lastname: Himes
authors.forename: Michele M
authors.initials: MM
authors.lastname: Sale
authors.forename: Karen L
authors.initials: KL
authors.lastname: Furie
authors.forename: Marco F
authors.initials: MF
authors.lastname: Ramoni
mesh_terms: D001499:Bayes Theorem; D023281:Genomics; D006801:Humans; D008957:Models, Genetic; D015999:Multivariate Analysis; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011379:Prognosis; D012307:Risk Factors; D020521:Stroke / Q000175:diagnosis / Q000453:epidemiology / Q000235:genetics*; D044465:White People / Q000235:genetics