Cao, MengfeiZhang, HaoPark, JisooHescott, BenjaminDaniels, Noah M.Crovella, Mark E.Cowen, Lenore J.2017-01-312017-01-312013Cao M, Zhang H, Park J, Daniels NM, Crovella ME, Cowen LJ, et al. (2013) Going the Distance for Protein Function Prediction: A New Distance Metric for Protein Interaction Networks. PLoS ONE 8(10): e76339. doi:10.1371/journal.pone.00763391932-6203https://hdl.handle.net/2144/20241Due to an error introduced in the production process, the x-axes in the first panels of Figure 1 and Figure 7 are not formatted correctly. The correct Figure 1 can be viewed here: http://dx.doi.org/10.1371/annotation/343bf260-f6ff-48a2-93b2-3cc79af518a9In 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.e76339 - ?en-USAttribution 4.0 United Stateshttp://creativecommons.org/licenses/by/4.0/us/AlgorithmsProteinsModels, geneticProtein interaction mapsArticle10.1371/journal.pone.00763390000-0002-5005-7019 (Crovella, Mark E)