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dc.contributor.authorCao, Mengfeien_US
dc.contributor.authorZhang, Haoen_US
dc.contributor.authorPark, Jisooen_US
dc.contributor.authorHescott, Benjaminen_US
dc.contributor.authorDaniels, Noah M.en_US
dc.contributor.authorCrovella, Mark E.en_US
dc.contributor.authorCowen, Lenore J.en_US
dc.coverage.spatialUnited Statesen_US
dc.date.accessioned2017-01-31T21:32:21Z
dc.date.available2017-01-31T21:32:21Z
dc.date.issued2013
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/24194834
dc.identifier.citationCao 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.0076339
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/2144/20241
dc.descriptionDue 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-48a2-93b2-3cc79af518a9en_US
dc.description.abstractIn 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.en_US
dc.description.sponsorshipMC, HZ, NMD and LJC were supported in part by National Institutes of Health (NIH) R01 grant GM080330. JP was supported in part by NIH grant R01 HD058880. This material is based upon work supported by the National Science Foundation under grant numbers CNS-0905565, CNS-1018266, CNS-1012910, and CNS-1117039, and supported by the Army Research Office under grant W911NF-11-1-0227 (to MEC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en_US
dc.format.extente76339 - ?en_US
dc.languageeng
dc.language.isoen_US
dc.relation.ispartofPLoS One
dc.rightsAttribution 4.0 United Statesen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/us/
dc.subjectAlgorithmsen_US
dc.subjectProteinsen_US
dc.subjectModels, geneticen_US
dc.subjectProtein interaction mapsen_US
dc.titleGoing the distance for protein function prediction: a new distance metric for protein interaction networksen_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pone.0076339
pubs.notesEmbargo: No embargoen_US
pubs.organisational-group/Boston Universityen_US
pubs.organisational-group/Boston University/College of Arts & Sciencesen_US
pubs.organisational-group/Boston University/College of Arts & Sciences/Department of Computer Scienceen_US
dc.identifier.orcid0000-0002-5005-7019 (Crovella, Mark E)


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Attribution 4.0 United States
Except where otherwise noted, this item's license is described as Attribution 4.0 United States