title: Coverage and error models of protein-protein interaction data by directed graph analysis.
abstract: Using a directed graph model for bait to prey systems and a multinomial error model, we assessed the error statistics in all published large-scale datasets for Saccharomyces cerevisiae and characterized them by three traits: the set of tested interactions, artifacts that lead to false-positive or false-negative observations, and estimates of the stochastic error rates that affect the data. These traits provide a prerequisite for the estimation of the protein interactome and its modules.
authors.affiliation: EMBL, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. tchiang@ebi.ac.uk
authors.forename: Tony
authors.initials: T
authors.lastname: Chiang
authors.forename: Denise
authors.initials: D
authors.lastname: Scholtens
authors.forename: Deepayan
authors.initials: D
authors.lastname: Sarkar
authors.forename: Robert
authors.initials: R
authors.lastname: Gentleman
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Huber
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D030562:Databases, Protein; D005656:Fungal Proteins; D015966:Gene Expression Regulation, Fungal; D023281:Genomics / Q000379:methods; D008954:Models, Biological; D025941:Protein Interaction Mapping; D020543:Proteome; D040901:Proteomics / Q000379:methods; D012044:Regression Analysis; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics; D013269:Stochastic Processes
chemical_list: D005656:Fungal Proteins; D020543:Proteome