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MDI-GPU : accelerating integrative modelling for genomic-scale data using GP-GPU computing

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Mason, Sam, Sayyid, Faiz, Kirk, Paul, Starr, Colin and Wild, David L. (2016) MDI-GPU : accelerating integrative modelling for genomic-scale data using GP-GPU computing. Statistical Applications in Genetics and Molecular Biology, 15 (1). pp. 83-86. doi:10.1515/sagmb-2015-0055 ISSN 1544-6115.

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Official URL: http://dx.doi.org/10.1515/sagmb-2015-0055,

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Abstract

The integration of multi-dimensional datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct - but often complementary - information. However, the large amount of data adds burden to any inference task. Flexible Bayesian methods may reduce the necessity for strong modelling assumptions, but can also increase the computational burden. We present an improved implementation of a Bayesian correlated clustering algorithm, that permits integrated clustering to be routinely performed across multiple datasets, each with tens of thousands of items. By exploiting GPU based computation, we are able to improve runtime performance of the algorithm by almost four orders of magnitude. This permits analysis across genomic-scale data sets, greatly expanding the range of applications over those originally possible. MDI is available here: http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre
Library of Congress Subject Headings (LCSH): Graphics processing units
Journal or Publication Title: Statistical Applications in Genetics and Molecular Biology
Publisher: Walter de Gruyter
ISSN: 1544-6115
Official Date: 24 February 2016
Dates:
DateEvent
24 February 2016Published
5 January 2016Accepted
Volume: 15
Number: 1
Number of Pages: 5
Page Range: pp. 83-86
DOI: 10.1515/sagmb-2015-0055
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 21 March 2016
Date of first compliant Open Access: 1 April 2017
Funder: Engineering and Physical Sciences Research Council (EPSRC)
Grant number: EP/I036575/1 (EPSRC), EP/J020281/1 (EPSRC)

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