Parallel computation for coefficients of determination in the context of multivariate gene-expression analysis

  • Authors:

  • Edward B. Suh;Daniel E. Russ;Edward R. Dougherty;Seungchan Kim;Michael L. Bittner;Yidong Chen;Robert L. Martino

  • Affiliations:

  • Division of Computational Bioscience, Center for Information Technology, National Institutes of Health Bethesda, Maryland;Division of Computational Bioscience, Center for Information Technology, National Institutes of Health Bethesda, Maryland;Department of Electrical Engineering, Texas A&M University, College Station, Texas;Cancer Genetic Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland;Cancer Genetic Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland;Cancer Genetic Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland;Division of Computational Bioscience, Center for Information Technology, National Institutes of Health Bethesda, Maryland

  • Venue:
  • Biocomputing
  • Year:
  • 2004

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Abstract

This paper presents a parallel program for assessing the codetermination of gene transcriptional states from large-scale simultaneous gene expression measurements with cDNA microarrays. The parallel program is based on the coefficient of determination, which has been proposed for the analysis of gene interaction via multivariate expression arrays and the construction of genetic regulatory network models. Parallel computing is key in the application of the coefficient of determination to a large set of genes owing to the large number of expression-based functions that must be statistically designed and compared. The parallel program, Parallel Analysis of Gene Expression (PAGE), exploits the inherent parallelism exhibited in the proposed codetermination methodology. An application to a Markovian regulatory network is given.