Computing consistency between microarray data and known gene regulation relationships

  • Authors:

  • Dong-Guk Shin;Saira A. Kazmi;Baikang Pei;Yoo-Ah Kim;Jeffrey Maddox;Ravi Nori;Alan Wong;Winfried Krueger;David Rowe

  • Affiliations:

  • Department of Computer Science and Engineering, University of Connecticut, Storrs, CT;Department of Computer Science and Engineering, University of Connecticut, Storrs, CT;Department of Computer Science and Engineering, University of Connecticut, Storrs, CT;Department of Computer Science and Engineering, University of Connecticut, Storrs, CT;Department of Computer Science and Engineering, University of Connecticut, Storrs, CT;Department of Computer Science and Engineering, University of Connecticut, Storrs, CT;Department of Developmental Biology, University of Connecticut Health Center, Farmington, CT;Department of Developmental Biology, University of Connecticut Health Center, Farmington, CT;Department of Developmental Biology, University of Connecticut Health Center, Farmington, CT

  • Venue:
  • IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
  • Year:
  • 2009

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Abstract

Microarray experiments produce expression patterns for thousands of genes at once. On the other hand, biomedical literature contains large amounts of gene regulation relationship information accumulated over the years. One obvious requirement is an automated way of comparing microarray data with the collection of known gene regulation relationships. Such an automated comparison is imperative because it can help biologists rapidly understand the context of a given microarray experiment. In addition, the consistency measure can be used to either validate or refute the hypothesis being tested using the microarray experiment. In this paper we present a systematic way of examining the consistency between a given set of microarray data and known gene regulation relationships. We first introduce a simple gene regulation network model with two separate algorithms designed to isolate a maximally consistent network. Subsequently, we extend the model to take into account multiple regulating factors for a single gene while highlighting both consistencies and inconsistencies. We illustrate the effectiveness of our approach with two practical examples, one that picks the peroxisome proliferator-activated receptor (PPAR) pathway as highly consistent from multiple pathways of Kyoto encyclopedia of genes and genomes (KEGG), and another that isolates key regulatory relationships involving nfkb1 and others known for macrophage's counter response to inflammation.