An integrated approach to predictive genomic analytics

  • Authors:

  • Jason E. McDermott;Bob Baddeley;Susan Stevens;Antonio Sanfilippo;Rick Riensche;Mary Stenzel-Poore;Ronald Taylor;Russ Jensen

  • Affiliations:

  • Pacific Northwest National Lab, Richland, WA;Pacific Northwest National Lab, Richland, WA;Oregon Health & Science University, Portland, OR;Pacific Northwest National Lab, Richland, WA;Pacific Northwest National Lab, Richland, WA;Oregon Health & Science University, Portland, OR;Pacific Northwest National Lab, Richland, WA;Pacific Northwest National Lab, Richland, WA

  • Venue:
  • Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
  • Year:
  • 2010

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Abstract

A variety of methods and algorithms have recently been employed in the analysis of gene expression data, including reverse-engineering and knowledge-based pathway modeling, semantic gene similarity, network analysis and clustering. These methods and algorithms address different subparts of the same overall challenge and need to be applied in combination to address predictive genomic analysis as a whole. In this paper, we present an integrated approach to predictive genomic analysis that achieves this objective and describe an application of the approach to the study of neuroprotection in stroke.