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- Title
Going the Distance for Protein Function Prediction: A New Distance Metric for Protein Interaction Networks.
- Authors
Cao, Mengfei; Zhang, Hao; Park, Jisoo; Daniels, Noah M.; Crovella, Mark E.; Cowen, Lenore J.; Hescott, Benjamin
- 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.
- Subjects
PROTEIN-protein interactions; PROTEIN transport; PROXIMITY matrices; COMPUTER science; FUNCTIONAL analysis
- Publication
PLoS ONE, 2013, Vol 8, Issue 10, p1
- ISSN
1932-6203
- Publication type
Article
- DOI
10.1371/journal.pone.0076339