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This article is part of the supplement: NIPS workshop on New Problems and Methods in Computational Biology

Open Access Proceedings

Choosing negative examples for the prediction of protein-protein interactions

Asa Ben-Hur12* and William Stafford Noble34

Author Affiliations

1 Department of Computer Science, Colorado State University, Fort Collins CO, USA

2 Department of Computer Science, University of Colorado, Boulder CO, USA

3 Department of Genome Sciences, University of Washington, Seattle WA, USA

4 Department of Computer Science and Engineering, University of Washington, Seattle WA, USA

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BMC Bioinformatics 2006, 7(Suppl 1):S2  doi:10.1186/1471-2105-7-S1-S2

Published: 20 March 2006


The protein-protein interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions. This need has prompted the development of a number of methods for predicting protein-protein interactions based on various sources of data and methodologies. The common method for choosing negative examples for training a predictor of protein-protein interactions is based on annotations of cellular localization, and the observation that pairs of proteins that have different localization patterns are unlikely to interact. While this method leads to high quality sets of non-interacting proteins, we find that this choice can lead to biased estimates of prediction accuracy, because the constraints placed on the distribution of the negative examples makes the task easier. The effects of this bias are demonstrated in the context of both sequence-based and non-sequence based features used for predicting protein-protein interactions.