title: Going the distance for protein function prediction: a new distance metric for protein interaction networks.
abstract: In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.
authors.affiliation: Department of Computer Science, Tufts University, Medford, Massachusetts, United States of America.
authors.forename: Mengfei
authors.initials: M
authors.lastname: Cao
authors.forename: Hao
authors.initials: H
authors.lastname: Zhang
authors.forename: Jisoo
authors.initials: J
authors.lastname: Park
authors.forename: Noah M
authors.initials: NM
authors.lastname: Daniels
authors.forename: Mark E
authors.initials: ME
authors.lastname: Crovella
authors.forename: Lenore J
authors.initials: LJ
authors.lastname: Cowen
authors.forename: Benjamin
authors.initials: B
authors.lastname: Hescott
mesh_terms: D000465:Algorithms; D008957:Models, Genetic; D060066:Protein Interaction Maps / Q000235:genetics*; D011506:Proteins / Q000378:metabolism*
chemical_list: D011506:Proteins