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

Author

Listed:
  • Mason Samuel A.

    (Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, UK)

  • Sayyid Faiz

    (Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK)

  • Kirk Paul D.W.

    (MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, CB2 0SR, UK)

  • Starr Colin

    (MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, CB2 0SR, UK)

  • Wild David L.

    (Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, UK)

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/.

Suggested Citation

  • Mason Samuel A. & Sayyid Faiz & Kirk Paul D.W. & Starr Colin & Wild David L., 2016. "MDI-GPU: accelerating integrative modelling for genomic-scale data using GP-GPU computing," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(1), pages 83-86, March.
    • Handle: RePEc:bpj:sagmbi:v:15:y:2016:i:1:p:83-86:n:2
    DOI: 10.1515/sagmb-2015-0055
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    Keywords

    Bayesian; clustering; GPU;
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