Gene Reachability Using Page Ranking on Gene Co-expression Networks

  • Pinaki Sarder
  • Weixiong Zhang
  • J. Perren Cobb
  • Arye Nehorai


We modify the Google Page-Rank algorithm, which is primarily used for ranking web pages, to analyze the gene reachability in complex gene co-expression networks. Our modification is based on the average connections per gene. We propose a new method to compute the metric of average connections per gene, inspired by the Page-Rank algorithm. We calculate this average as eight for human genome data and three to seven for yeast genome data. Our algorithm provides clustering of genes. The proposed analogy between web pages and genes may offer a new way to interpret gene networks.


Ingenuity Pathway Analysis Gene Ranking Connectivity Information Average Connection Gene Connection 
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.



The work of W. Zhang was supported by the NSF grants IIS-0535257 and DBI-0743797 and a grant from the Alzheimer’s Association. The work of J. P. Cobb was supported by the NIH grants R21GM075023 and R01GM59960.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Pinaki Sarder
    • 1
  • Weixiong Zhang
    • 2
  • J. Perren Cobb
    • 3
  • Arye Nehorai
    • 4
  1. 1.Department of Computer Science and EngineeringWashington University in St.LouisSt. LouisUSA
  2. 2.Departments of Computer Science and Engineering and GeneticsWashington University in St. LouisSt. LouisUSA
  3. 3.Department of Anesthesia, Critical Care, and Pain MedicineMassachusetts General HospitalBostonUSA
  4. 4.Department of Electrical and Systems EngineeringWashington University in St. LouisSt. LouisUSA

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