Protein ranking: from local to global structure in the protein similarity network.
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.
Weston
Jason
J
NEC Laboratories America, 4 Independence Way, Princeton, NJ 08540, USA.
Elisseeff
Andre
A
Zhou
Dengyong
D
Leslie
Christina S
CS
Noble
William Stafford
WS
D000465:Algorithms; D030561:Databases, Nucleic Acid; D030562:Databases, Protein; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics
D011506:Proteins