A scalable and portable framework for massively parallel variable selection in genetic association studies

Summary: The deluge of data emerging from high-throughput sequencing technologies poses large analytical challenges when testing for association to disease. We introduce a scalable framework for variable selection, implemented in C++ and OpenCL, that fits regularized regression across multiple Graph...

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Detalles Bibliográficos
Autor principal: Chen, Gary K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2012
Materias:
Applications Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289918/
https://www.ncbi.nlm.nih.gov/pubmed/22238272
http://dx.doi.org/10.1093/bioinformatics/bts015
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author Chen, Gary K.
author_facet Chen, Gary K.
author_sort Chen, Gary K.
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description Summary: The deluge of data emerging from high-throughput sequencing technologies poses large analytical challenges when testing for association to disease. We introduce a scalable framework for variable selection, implemented in C++ and OpenCL, that fits regularized regression across multiple Graphics Processing Units. Open source code and documentation can be found at a Google Code repository under the URL http://bioinformatics.oxfordjournals.org/content/early/2012/01/10/bioinformatics.bts015.abstract. Contact: gary.k.chen@usc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-32899182012-02-29 A scalable and portable framework for massively parallel variable selection in genetic association studies Chen, Gary K. Bioinformatics Applications Note Summary: The deluge of data emerging from high-throughput sequencing technologies poses large analytical challenges when testing for association to disease. We introduce a scalable framework for variable selection, implemented in C++ and OpenCL, that fits regularized regression across multiple Graphics Processing Units. Open source code and documentation can be found at a Google Code repository under the URL http://bioinformatics.oxfordjournals.org/content/early/2012/01/10/bioinformatics.bts015.abstract. Contact: gary.k.chen@usc.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2012-03-01 2012-01-11 /pmc/articles/PMC3289918/ /pubmed/22238272 http://dx.doi.org/10.1093/bioinformatics/bts015 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Chen, Gary K.
A scalable and portable framework for massively parallel variable selection in genetic association studies
title A scalable and portable framework for massively parallel variable selection in genetic association studies
title_full A scalable and portable framework for massively parallel variable selection in genetic association studies
title_fullStr A scalable and portable framework for massively parallel variable selection in genetic association studies
title_full_unstemmed A scalable and portable framework for massively parallel variable selection in genetic association studies
title_short A scalable and portable framework for massively parallel variable selection in genetic association studies
title_sort scalable and portable framework for massively parallel variable selection in genetic association studies
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289918/
https://www.ncbi.nlm.nih.gov/pubmed/22238272
http://dx.doi.org/10.1093/bioinformatics/bts015
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