Linked Life Data

19272865

Source:http://linkedlifedata.com/resource/pubmed/id/19272865

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2009-4-24
pubmed:abstractText
Numerical simulations entail modeling assumptions that impact outcomes. Therefore, characterizing, in a probabilistic sense, the relationship between the variability of model selection and the variability of outcomes is important. Under certain assumptions, the stochastic collocation method offers a computationally feasible alternative to traditional Monte Carlo approaches for assessing the impact of model and parameter variability. We propose a framework that combines component shape parameterization with the stochastic collocation method to study the effect of drug depot shape variability on the outcome of drug diffusion simulations in a porcine model. We use realistic geometries segmented from MR images and employ level-set techniques to create two alternative univariate shape parameterizations. We demonstrate that once the underlying stochastic process is characterized, quantification of the introduced variability is quite straightforward and provides an important step in the validation and verification process.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1558-2531
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
56
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
609-20
pubmed:dateRevised
2010-9-22
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Using the stochastic collocation method for the uncertainty quantification of drug concentration due to depot shape variability.
pubmed:affiliation
Scientific Computing and Imaging Institute and the School of Computing, University of Utah, Salt Lake City, UT 84112, USA. jsam@sci.utah.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural