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Article

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

Bioinformatics (Oxford, England) - 1 Mar 2012

Chen Gary K

Abstract excerpt

UNLABELLED: 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...

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Identifiers

PMID: 22238272
DOI: 10.1093/bioinformatics/bts015
PMCID: PMC3289918

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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