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

ABSTRACT:

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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.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Chen GK 

PROVIDER: S-EPMC3289918 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature