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dc.contributor.author Shterev, ID
dc.contributor.author Jung, SH
dc.contributor.author George, SL
dc.contributor.author Owzar, K
dc.coverage.spatial England
dc.date.accessioned 2011-06-21T17:27:56Z
dc.date.issued 2010-06-16
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/20553619
dc.identifier 1471-2105-11-329
dc.identifier.citation BMC Bioinformatics, 2010, 11 pp. 329 - ?
dc.identifier.uri http://hdl.handle.net/10161/4333
dc.description.abstract BACKGROUND: Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed. RESULTS: We have developed a CUDA based implementation, permGPU, that employs graphics processing units in microarray association studies. We illustrate the performance and applicability of permGPU within the context of permutation resampling for a number of test statistics. An extensive simulation study demonstrates a dramatic increase in performance when using permGPU on an NVIDIA GTX 280 card compared to an optimized C/C++ solution running on a conventional Linux server. CONCLUSIONS: permGPU is available as an open-source stand-alone application and as an extension package for the R statistical environment. It provides a dramatic increase in performance for permutation resampling analysis in the context of microarray association studies. The current version offers six test statistics for carrying out permutation resampling analyses for binary, quantitative and censored time-to-event traits.
dc.format.extent 329 - ?
dc.language ENG
dc.language.iso en_US en_US
dc.relation.ispartof BMC Bioinformatics
dc.relation.isversionof 10.1186/1471-2105-11-329
dc.subject Gene Expression Profiling
dc.subject Genetic Association Studies
dc.subject Humans
dc.subject Microarray Analysis
dc.subject Neoplasms
dc.subject RNA
dc.subject Software
dc.title permGPU: Using graphics processing units in RNA microarray association studies.
dc.title.alternative en_US
dc.type Journal Article
dc.description.version Version of Record en_US
duke.date.pubdate 2010-6-16 en_US
duke.description.endpage 329 en_US
duke.description.issue en_US
duke.description.startpage 329 en_US
duke.description.volume 11 en_US
dc.relation.journal Bmc Bioinformatics en_US
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/20553619
pubs.organisational-group /Duke
pubs.organisational-group /Duke/School of Medicine
pubs.organisational-group /Duke/School of Medicine/Basic Science Departments
pubs.organisational-group /Duke/School of Medicine/Basic Science Departments/Biostatistics & Bioinformatics
pubs.organisational-group /Duke/School of Medicine/Institutes and Centers
pubs.organisational-group /Duke/School of Medicine/Institutes and Centers/Duke Cancer Institute
pubs.publication-status Published online
pubs.volume 11
dc.identifier.eissn 1471-2105

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