title: Predicting protein complex membership using probabilistic network reliability.
abstract: 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.
authors.affiliation: Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, USA.
authors.forename: Saurabh
authors.initials: S
authors.lastname: Asthana
authors.forename: Oliver D
authors.initials: OD
authors.lastname: King
authors.forename: Francis D
authors.initials: FD
authors.lastname: Gibbons
authors.forename: Frederick P
authors.initials: FP
authors.lastname: Roth
mesh_terms: D005656:Fungal Proteins / Q000737:chemistry; D046911:Macromolecular Substances; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011336:Probability; D025941:Protein Interaction Mapping / Q000379:methods / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012984:Software
chemical_list: D005656:Fungal Proteins; D046911:Macromolecular Substances