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Integrated Protein Interaction Networks for 11 Microbes

  • Balaji S. Srinivasan
  • Antal F. Novak
  • Jason A. Flannick
  • Serafim Batzoglou
  • Harley H. McAdams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3909)

Abstract

We have combined four different types of functional genomic data to create high coverage protein interaction networks for 11 microbes. Our integration algorithm naturally handles statistically dependent predictors and automatically corrects for differing noise levels and data corruption in different evidence sources. We find that many of the predictions in each integrated network hinge on moderate but consistent evidence from multiple sources rather than strong evidence from a single source, yielding novel biology which would be missed if a single data source such as coexpression or coinheritance was used in isolation. In addition to statistical analysis, we demonstrate via case study that these subtle interactions can discover new aspects of even well studied functional modules. Our work represents the largest collection of probabilistic protein interaction networks compiled to date, and our methods can be applied to any sequenced organism and any kind of experimental or computational technique which produces pairwise measures of protein interaction.

Keywords

Functional Module Protein Interaction Network Protein Pair Linkage Prediction Network Integration 
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 2006

Authors and Affiliations

  • Balaji S. Srinivasan
    • 1
    • 2
  • Antal F. Novak
    • 3
  • Jason A. Flannick
    • 3
  • Serafim Batzoglou
    • 3
  • Harley H. McAdams
    • 2
  1. 1.Department of Electrical Engineering 
  2. 2.Department of Developmental Biology 
  3. 3.Department of Computer ScienceStanford UniversityStanfordUSA

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