Detecting Disease-Specific Dysregulated Pathways Via Analysis of Clinical Expression Profiles

  • Igor Ulitsky
  • Richard M. Karp
  • Ron Shamir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4955)


We present a method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. These subnetworks provide a signature of the disease potentially useful for diagnosis, pinpoint possible pathways affected by the disease, and suggest targets for drug intervention. Our method uses microarray gene expression profiles derived in clinical case-control studies to identify genes significantly dysregulated in disease specimens, combined with protein interaction data to identify connected sets of genes. Our core algorithm searches for minimal connected subnetworks in which the number of dysregulated genes in each diseased sample exceeds a given threshold. We have applied the method in a study of Huntington’s disease caudate nucleus expression profiles and in a meta-analysis of breast cancer studies. In both cases the results were statistically significant and appeared to home in on compact pathways enriched with hallmarks of the diseases.


Average Short Path Length Human Molecular Genetic Human Protein Interaction Network Dysregulated Pathway Active Subnetwork 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Igor Ulitsky
    • 1
  • Richard M. Karp
    • 2
  • Ron Shamir
    • 1
  1. 1.School of Computer ScienceTel-Aviv UniversityTel-AvivIsrael
  2. 2.International Computer Science InstituteBerkeley 

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