Automatic parameter learning for multiple network alignment

Authors:
Jason Flannick;Antal Novak;Chuong B. Do;Balaji S. Srinivasan;Serafim Batzoglou
Affiliations:
Department of Computer Science, Stanford University, Stanford, CA;Department of Computer Science, Stanford University, Stanford, CA;Department of Computer Science, Stanford University, Stanford, CA;Department of Statistics, Stanford University, Stanford, CA;Department of Computer Science, Stanford University, Stanford, CA
Venue:
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Year:
2008

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Abstract

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 http://graemlin.stanford.edu.