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dc.contributor.authorCannistraci, Carlo
dc.contributor.authorAlanis Lobato, Gregorio
dc.contributor.authorRavasi, Timothy
dc.date.accessioned2014-08-27T09:51:18Z
dc.date.available2014-08-27T09:51:18Z
dc.date.issued2013-06-19
dc.identifier.citationCannistraci CV, Alanis-Lobato G, Ravasi T (2013) Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding. Bioinformatics 29: i199-i209. doi:10.1093/bioinformatics/btt208.
dc.identifier.issn13674803
dc.identifier.pmid23812985
dc.identifier.doi10.1093/bioinformatics/btt208
dc.identifier.urihttp://hdl.handle.net/10754/325437
dc.description.abstractMotivation: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. The Author 2013.
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0
dc.subjectalgorithm
dc.subjectgene ontology
dc.subjectmethodology
dc.subjectprotein analysis
dc.subjectsystems biology
dc.subjectAlgorithms
dc.subjectGene Ontology
dc.subjectProtein Interaction Mapping
dc.subjectSystems Biology
dc.titleMinimum curvilinearity to enhance topological prediction of protein interactions by network embedding
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentIntegrative Systems Biology Lab
dc.identifier.journalBioinformatics
dc.identifier.pmcidPMC3694668
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDivision of Medical Genetics, Department of Medicine, University of California, San Diego, CA 92093-0688, United States
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personCannistraci, Carlo
kaust.personAlanis Lobato, Gregorio
kaust.personRavasi, Timothy
refterms.dateFOA2018-06-13T15:23:42Z
dc.date.published-online2013-06-19
dc.date.published-print2013-07


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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com