Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics 2014
DOI: 10.1093/acprof:oso/9780198709022.003.0013
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Bayesian, Systems-based, Multilevel Analysis of Associations for Complex Phenotypes: from Interpretation to Decision

Abstract: The relative scarcity of the results reported by genetic association studies (GAS) prompted many research directions. Despite the centrality of the concept of association in GASs, refined concepts of association are missing; meanwhile, various feature subset selection methods became de facto standards for defining multivariate relevance. On the other hand, probabilistic graphical models, including Bayesian networks (BNs) are more and more popular, as they can learn nontransitive, multivariate, nonlinear relati… Show more

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Cited by 9 publications
(10 citation statements)
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“…Additionally, it can be difficult to differentiate AR from other common diseases, such as non-allergic rhinitis (NAR) and chronic rhinosinusitis, as these diseases share similar symptoms with AR in young children. These similarities are particularly common in preschool-aged children 18. We diagnosed current AR as nasal symptoms and also used physician diagnoses to reduce potential errors in the misdiagnosis of AR.…”
Section: Discussionmentioning
confidence: 99%
“…Additionally, it can be difficult to differentiate AR from other common diseases, such as non-allergic rhinitis (NAR) and chronic rhinosinusitis, as these diseases share similar symptoms with AR in young children. These similarities are particularly common in preschool-aged children 18. We diagnosed current AR as nasal symptoms and also used physician diagnoses to reduce potential errors in the misdiagnosis of AR.…”
Section: Discussionmentioning
confidence: 99%
“…Bayesian networks offer a rich language for genetic association studies, because they exhaustively and exactly represent strongly relevant variables and their interactions through the Markov Blanket Set and Markov Blanket Graph features and they are able to evaluate multiple targets. Furthermore, this Bayesian global relevance analysis method provides posterior probabilities, which are direct statements about hypotheses; thus, it can also be used to construct probabilistic data analytic knowledge bases in genetic association studies to support complex querying, off-line meta-analysis, and fusion with background knowledge 18192021…”
Section: Methodsmentioning
confidence: 99%
“…, SNP), on the other hand 0 means that there is no such relationship. Posterior probabilities of strong relevance greater than or equal to 0.5 are regarded as relevant, and above 0.75 as convincing 18192021…”
Section: Methodsmentioning
confidence: 99%
“…A relevant relationship was defined when the probability was >0.3. The applied method was developed by our research team; for further details see the section in the book “Probabilistic graphical models for genetics” [34]. …”
Section: Methodsmentioning
confidence: 99%