title: Protein ranking: from local to global structure in the protein similarity network.
abstract: Biologists regularly search databases of DNA or protein sequences for evolutionary or functional relationships to a given query sequence. We describe a ranking algorithm that exploits the entire network structure of similarity relationships among proteins in a sequence database by performing a diffusion operation on a precomputed, weighted network. The resulting ranking algorithm, evaluated by using a human-curated database of protein structures, is efficient and provides significantly better rankings than a local network search algorithm such as psi-blast.
authors.affiliation: NEC Laboratories America, 4 Independence Way, Princeton, NJ 08540, USA.
authors.forename: Jason
authors.initials: J
authors.lastname: Weston
authors.forename: Andre
authors.initials: A
authors.lastname: Elisseeff
authors.forename: Dengyong
authors.initials: D
authors.lastname: Zhou
authors.forename: Christina S
authors.initials: CS
authors.lastname: Leslie
authors.forename: William Stafford
authors.initials: WS
authors.lastname: Noble
mesh_terms: D000465:Algorithms; D030561:Databases, Nucleic Acid; D030562:Databases, Protein; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics
chemical_list: D011506:Proteins