Automatic Parameter Learning for Multiple Network Alignment

  • Jason Flannick
  • Antal Novak
  • Chuong B. Do
  • Balaji S. Srinivasan
  • Serafim Batzoglou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4955)


We developed Græmlin 2.0, a new multiple network aligner with (1) a novel scoring function that can use arbitrary features of a multiple network alignment, such as protein deletions, protein duplications, protein mutations, and interaction losses; (2) a parameter learning algorithm that uses a training set of known network alignments to learn parameters for our scoring function and thereby adapt it to any set of networks; and (3) an algorithm that uses our scoring function to find approximate multiple network alignments in linear time.

We tested Græmlin 2.0’s accuracy on protein interaction networks from IntAct, DIP, and the Stanford Network Database. We show that, on each of these datasets, Græmlin 2.0 has higher sensitivity and specificity than existing network aligners. Græmlin 2.0 is available under the GNU public license at


Equivalence Class Protein Interaction Network Alignment Algorithm Multiple Network Edge Deletion 
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

  • Jason Flannick
    • 1
  • Antal Novak
    • 1
  • Chuong B. Do
    • 1
  • Balaji S. Srinivasan
    • 2
  • Serafim Batzoglou
    • 1
  1. 1.Department of Computer ScienceStanford UniversityStanfordUSA
  2. 2.Department of StatisticsStanford UniversityStanfordUSA

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