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Third IEEE International Conference on Data Mining (ICDM'03)
Inference of Protein-Protein Interactions by Unlikely Profile Pair
Melbourne, Florida
November 19-November 22
ISBN: 0-7695-1978-4
Byung-Hoon Park, Oak Ridge National Laboratory
George Ostrouchov, Oak Ridge National Laboratory
Gong-Xin Yu, Oak Ridge National Laboratory
Al Geist, Oak Ridge National Laboratory
Andrey Gorin, Oak Ridge National Laboratory
Nagiza F. Samatova, Oak Ridge National Laboratory
We note that a set of statistically "unusual" protein-profile pairs in experimentally determined database of protein-protein interactions can typify protein-protein interactions, and propose a novel method called PICUPP that sifts such protein-profile pairs using a statistical simulation. It is demonstrated that unusual Pfam and InterPro profile pairs can be extracted from the DIP database using a bootstrapping approach. We particularly illustrate that such protein-profile pairs can be used for predicting putative pairs of interacting proteins. Their prediction accuracies are around 86% and 90% when InterPro and Pfam profiles are used, respectively at 75% confidence level.
Citation:
Byung-Hoon Park, George Ostrouchov, Gong-Xin Yu, Al Geist, Andrey Gorin, Nagiza F. Samatova, "Inference of Protein-Protein Interactions by Unlikely Profile Pair," icdm, pp.735, Third IEEE International Conference on Data Mining (ICDM'03), 2003
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