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Methods

Predicting Protein Complex Membership Using Probabilistic Network Reliability

Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, USA

Evidence for specific protein–protein interactions is increasingly available from both small- and large-scale studies, and can be viewed as a network. It has previously been noted that errors are frequent among large-scale studies, and that error frequency depends on the large-scale method used. Despite knowledge of the error-prone nature of interaction evidence, edges (connections) in this network are typically viewed as either present or absent. However, use of a probabilistic network that considers quantity and quality of supporting evidence should improve inference derived from protein networks. Here we demonstrate inference of membership in a partially known protein complex by using a probabilistic network model and an algorithm previously used to evaluate reliability in communication networks.

NOTE ADDED IN PROOF

After submission of our manuscript, another work was published with the same goal of predicting membership in partially known protein complexes (Bader, J.S. 2003. Greedily building protein networks with confidence. Bioinformatics. 19: 1869–1874). One important difference is that the work by Bader ranks protein candidates according to the probability of their connection to the core complex via the single most probable path, while the ProNet method ranks according to the probability of connection via any path.

¹ Corresponding author.
E-MAIL fritz_roth{at}hms.harvard.edu; FAX (617) 432-3557.

[Supplemental material is available online at www.genome.org. Software called Complexpander, which predicts new members of partially known protein complexes, is available at the authors' Web site http://llama.med.harvard.edu/Software.html.]

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