Linked Life Data

21029848

Source:http://linkedlifedata.com/resource/pubmed/id/21029848

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2010-10-29
pubmed:abstractText
Genetic epidemiology is increasingly focused on complex diseases involving multiple genes and environmental factors, often interacting in complex ways. Although standard frequentist methods still have a role in hypothesis generation and testing for discovery of novel main effects and interactions, Bayesian methods are particularly well suited to modeling the relationships in an integrated "systems biology" manner. In this chapter, we provide an overview of the principles of Bayesian analysis and their advantages in this context and describe various approaches to applying them for both model building and discovery in a genome-wide setting. In particular, we highlight the ability of Bayesian methods to construct complex probability models via a hierarchical structure and to account for uncertainty in model specification by averaging over large spaces of alternative models.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0065-2660
pubmed:author
pubmed:copyrightInfo
Copyright © 2010 Elsevier Inc. All rights reserved.
pubmed:issnType
Print
pubmed:volume
72
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
47-71
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Complex system approaches to genetic analysis Bayesian approaches.
pubmed:affiliation
Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA.
pubmed:publicationType
Journal Article, Review