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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics$
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Christine Sinoquet and Raphaël Mourad

Print publication date: 2014

Print ISBN-13: 9780198709022

Published to Oxford Scholarship Online: December 2014

DOI: 10.1093/acprof:oso/9780198709022.001.0001

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Prediction of Clinical Outcomes from Genome-wide Data

Prediction of Clinical Outcomes from Genome-wide Data

Chapter:
(p.431) Chapter 17 Prediction of Clinical Outcomes from Genome-wide Data
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

Shyam Visweswaran

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0017

Prognosis is an essential tool in medicine for estimating the likely outcomes of a disease and for preventing it. The traditional approach relies on measuring physiological and environmental parameters. With the recent availability of genome-wide data, it is now possible to incorporate the genetic information for predicting complex diseases. Probabilistic graphical models are well-known for their efficiency in predictive issue and thus represent good candidate models in this context. The probabilistic graphical model framework provides many assets: data uncertainty modeling, fast probabilistic inference algorithms, easy incorporation of expert knowledge and good predictive performance.

Keywords:   clinical outcomes, genome-wide data, naive Bayes model, Bayesian model averaging, ROC curve

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