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Bioinformatics Advance Access originally published online on October 1, 2008
Bioinformatics 2008 24(22):2608-2614; doi:10.1093/bioinformatics/btn498
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© The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Physical protein–protein interactions predicted from microarrays

Ta-tsen Soong 1,2,*, Kazimierz O. Wrzeszczynski 1,3,4 and Burkhard Rost 1,3,5

1Columbia University Center for Computational Biology and Bioinformatics (C2B2), 2Department of Biomedical Informatics, 3Department of Biochemistry and Molecular Biophysics, 4Integrated Program in Cellular, Molecular and Biomedical Studies and 5NorthEast Structural Genomics Consortium (NESG) and New York Consortium on Membrane Proteins (NYCOMPS), Columbia University, New York, NY, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Microarray expression data reveal functionally associated proteins. However, most proteins that are associated are not actually in direct physical contact. Predicting physical interactions directly from microarrays is both a challenging and important task that we addressed by developing a novel machine learning method optimized for this task.

Results: We validated our support vector machine-based method on several independent datasets. At the same levels of accuracy, our method recovered more experimentally observed physical interactions than a conventional correlation-based approach. Pairs predicted by our method to very likely interact were close in the overall network of interaction, suggesting our method as an aid for functional annotation. We applied the method to predict interactions in yeast (Saccharomyces cerevisiae). A Gene Ontology function annotation analysis and literature search revealed several probable and novel predictions worthy of future experimental validation. We therefore hope our new method will improve the annotation of interactions as one component of multi-source integrated systems.

Contact: ts2186{at}columbia.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Alfonso Valencia

Received on April 26, 2008; revised on August 30, 2008; accepted on September 17, 2008

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