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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 pre-computed, weighted network. The resulting ranking algorithm, evaluated 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.
@article{2586, title = {Protein ranking: from local to global structure in the protein similarity network}, journal = {Proceedings of the National Academy of Science}, 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 pre-computed, weighted network. The resulting ranking algorithm, evaluated 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.}, volume = {101}, number = {17}, pages = {6559-6563}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, year = {2004}, slug = {2586}, author = {Weston, J. and Elisseeff, A. and Zhou, D. and Leslie, C. and Noble, WS.} }