Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

Handle URI:
http://hdl.handle.net/10754/325437
Title:
Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding
Authors:
Cannistraci, Carlo; Alanis Lobato, Gregorio ( 0000-0001-9339-4229 ) ; Ravasi, Timothy ( 0000-0002-9950-465X )
Abstract:
Motivation: 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.
KAUST Department:
Computational Bioscience Research Center (CBRC)
Citation:
Cannistraci 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.
Publisher:
Oxford University Press
Journal:
Bioinformatics
Issue Date:
21-Jun-2013
DOI:
10.1093/bioinformatics/btt208
PubMed ID:
23812985
PubMed Central ID:
PMC3694668
Type:
Article
ISSN:
13674803
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorCannistraci, Carloen
dc.contributor.authorAlanis Lobato, Gregorioen
dc.contributor.authorRavasi, Timothyen
dc.date.accessioned2014-08-27T09:51:18Z-
dc.date.available2014-08-27T09:51:18Z-
dc.date.issued2013-6-21en
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.en
dc.identifier.issn13674803en
dc.identifier.pmid23812985en
dc.identifier.doi10.1093/bioinformatics/btt208en
dc.identifier.urihttp://hdl.handle.net/10754/325437en
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.en
dc.language.isoenen
dc.publisherOxford University Pressen
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.comen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0en
dc.subjectalgorithmen
dc.subjectgene ontologyen
dc.subjectmethodologyen
dc.subjectprotein analysisen
dc.subjectsystems biologyen
dc.subjectAlgorithmsen
dc.subjectGene Ontologyen
dc.subjectProtein Interaction Mappingen
dc.subjectSystems Biologyen
dc.titleMinimum curvilinearity to enhance topological prediction of protein interactions by network embeddingen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalBioinformaticsen
dc.identifier.pmcidPMC3694668en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDivision of Medical Genetics, Department of Medicine, University of California, San Diego, CA 92093-0688, United Statesen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorCannistraci, Carloen
kaust.authorAlanis Lobato, Gregorioen
kaust.authorRavasi, Timothyen

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