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Bioinformatics Advance Access published online on November 14, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn597
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© 2008 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.

Combining multiple positive training sets to generate confidence scores for protein-protein interactions

Jingkai Yu 1 and Russell L. Finley, Jr. 1,2,*

1Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, 540 East Canfield, Detroit, MI 48201.
2Department of Biochemistry and Molecular Biology, School of Medicine, Wayne State University, 540 East Canfield, De-troit, MI 48201.

*To whom correspondence should be addressed. Dr. Russell L. Finley, Jr., E-mail: rfinley{at}wayne.edu


  Abstract

Motivation: High-throughput experimental and computational methods are generating a wealth of protein-protein interaction data for a variety of organisms. However, data produced by current state-of-the-art methods include many false positives, which can hinder the analyses needed to derive biological insights. One way to address this problem is to assign confidence scores that reflect the reliability and biological significance of each interaction. Most previously de-scribed scoring methods use a set of likely true positives to train a model to score all interactions in a dataset. A single positive training set, however, may be biased and not representative of true interac-tion space.

Results: We demonstrate a method to score protein interactions by utilizing multiple independent sets of training positives to reduce the potential bias inherent in using a single training set. We used a set of benchmark yeast protein interactions to show that our approach outperforms other scoring methods. Our approach can also score interactions across data types, which makes it more widely applica-ble than many previously proposed methods. We applied the method to protein interaction data from both Drosophila mela-nogaster and Homo sapiens. Independent evaluations show that the resulting confidence scores accurately reflect the biological signifi-cance of the interactions.

Associate Editor: Prof. Burkhard Rost

Received on June 26, 2008; revised on November 12, 2008; accepted on November 13, 2008

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