Inference of Protein-Protein Interactions by Unlikely Profile Pair

  • Authors:

  • Byung-Hoon Park;George Ostrouchov;Gong-Xin Yu;Al Geist;Andrey Gorin;Nagiza F. Samatova

  • Affiliations:

  • -;-;-;-;-;-

  • Venue:
  • ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
  • Year:
  • 2003

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Abstract

We note that a set of statistically "unusual" protein-profilepairs in experimentally determined database ofprotein-protein interactions can typify protein-proteininteractions, and propose a novel method calledPICUPP that sifts such protein-profile pairs using astatistical simulation. It is demonstrated that unusualPfam and InterPro profile pairs can be extracted fromthe DIP database using a bootstrapping approach. Weparticularly illustrate that such protein-profile pairs canbe used for predicting putative pairs of interactingproteins. Their prediction accuracies are around 86%and 90% when InterPro and Pfam profiles are used,respectively at 75% confidence level.