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Abstract:
Background:
Protein-protein interaction networks are key to a systems-level understanding of cellular biology.
However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed
to assess the confidence of individual interactions. Most of them require the integration of additional data like protein
expression and interaction homology information. While being certainly useful, such additional data are not always
available and may introduce additional bias and ambiguity.
Results:
We propose a novel, network topology based interaction confidence assessment method called CAPPIC
(cluster-based assessment of protein-protein interaction confidence). It exploits the network’s inherent modular
architecture for assessing the confidence of individual interactions. Our method determines algorithmic parameters
intrinsically and does not require any parameter input or reference sets for confidence scoring.
Conclusions:
On the basis of five yeast and two human physical interactome maps inferred using different
techniques, we show that CAPPIC reliably assesses interaction confidence and its performance compares well to other
approaches that are also based on network topology. The confidence score correlates with the agreement in
localization and biological process annotations of interacting proteins. Moreover, it corroborates experimental
evidence of physical interactions. Our method is not limited to physical interactome maps as we exemplify with a
large yeast genetic interaction network. An implementation of CAPPIC is available at http://intscore.molgen.mpg.de
