title: Assessing experimentally derived interactions in a small world.
abstract: Experimentally determined networks are susceptible to errors, yet important inferences can still be drawn from them. Many real networks have also been shown to have the small-world network properties of cohesive neighborhoods and short average distances between vertices. Although much analysis has been done on small-world networks, small-world properties have not previously been used to improve our understanding of individual edges in experimentally derived graphs. Here we focus on a small-world network derived from high-throughput (and error-prone) protein-protein interaction experiments. We exploit the neighborhood cohesiveness property of small-world networks to assess confidence for individual protein-protein interactions. By ascertaining how well each protein-protein interaction (edge) fits the pattern of a small-world network, we stratify even those edges with identical experimental evidence. This result promises to improve the quality of inference from protein-protein interaction networks in particular and small-world networks in general.
authors.affiliation: Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.
authors.forename: Debra S
authors.initials: DS
authors.lastname: Goldberg
authors.forename: Frederick P
authors.initials: FP
authors.lastname: Roth
mesh_terms: D016000:Cluster Analysis; D008433:Mathematics; D008954:Models, Biological; D011506:Proteins / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism
chemical_list: D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins