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|Title: ||Bayesian variable selection method for modeling dose-response microarray data under simple order restrictions|
|Authors: ||Otava, Martin|
|Issue Date: ||2012|
|Citation: ||Komárek, Arnošt; Nagy, Stanislav (Ed.). Proceedings of the 27nd International Workshop on Statistical Modelling (IWSM), p. 673-679|
|Abstract: ||The aim of the analysis presented below is to investigate dose-response relationship in a microarray setting. Typically, in dose-response experiments the outcome of interest is measured in several (increasing)
dose levels and the goal of the analysis is to establish the relationship which represents the dependency of
the response on dose.
Bayesian modeling of dose-response microarray data offers the possibility to jointly establish the dose
response relationships between gene expression and increasing doses of therapeutic compound and to
determine the nature of the relationships wherever it exists.
The Bayesian variable selection approach provides a modeling framework that allows estimating the posterior probabilities for a given set of pre-speciﬁed models and in particular the posterior probability of
the model estimated under the null hypothesis of no dose effect. The posterior probabilities are used for
multiplicity adjustment using the direct posterior probability approach.|
|Type: ||Proceedings Paper|
|Appears in Collections: ||Research publications|
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