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Incorporating Knowledge of Topology Improves Reconstruction of Interaction Networks from Microarray Data

  • Peter Larsen
  • Eyad Almasri
  • Guanrao Chen
  • Yang Dai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4983)

Abstract

Reconstruction of biological interaction networks from high-throughput experimental data is one of the most challenging problems in bioinformatics. These networks have specific topologies, whose characteristics are defined by evolutionary relationships between proteins and the physical limitations imposed on proteins interacting in three-dimensional space. In this study, a method is proposed applying the topology of known biological networks to the analysis of microarray data for protein-protein binding interactions. In this method, genomic biological networks are derived from the body of published scientific literature. The numbers of interacting neighbors for proteins of specific molecular functions are observed. That information is used in the analysis of microarray expression data to regenerate biological networks using a rank-based algorithm, Gene Ontology Restricted Value Neighborhood (GRV-N). The results of this analysis demonstrate that incorporating knowledge of network topology improves the ability of expression analysis to reconstruct interaction networks with a high degree of biological relevance.

Keywords

Rank-based algorithm Gene Ontology Gene expression Co-expression network Network topology 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Peter Larsen
    • 1
  • Eyad Almasri
    • 2
  • Guanrao Chen
    • 3
  • Yang Dai
    • 2
  1. 1.Core Genomics Laboratory, Research Resource Center (MC937)University of Illinois at ChicagoChicagoUSA
  2. 2.Department of Bioengineering (MC063)University of Illinois at ChicagoChicagoUSA
  3. 3.Department of Computer Science (MC152)University of Illinois at ChicagoChicagoUSA

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