Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding
KAUST DepartmentComputational Bioscience Research Center (CBRC)
MetadataShow full item record
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.
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.
PublisherOxford University Press (OUP)
PubMed Central IDPMC3694668
The following license files are associated with this item:
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 email@example.com
- Fitting a geometric graph to a protein-protein interaction network.
- Authors: Higham DJ, Rasajski M, Przulj N
- Issue date: 2008 Apr 15
- Stringent DDI-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.
- Authors: Zhou H, Rezaei J, Hugo W, Gao S, Jin J, Fan M, Yong CH, Wozniak M, Wong L
- Issue date: 2013
- Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data.
- Authors: You ZH, Lei YK, Gui J, Huang DS, Zhou X
- Issue date: 2010 Nov 1
- A Two-Stage Geometric Method for Pruning Unreliable Links in Protein-Protein Networks.
- Authors: Zhu L, Deng SP, Huang DS
- Issue date: 2015 Jul
- Complex discovery from weighted PPI networks.
- Authors: Liu G, Wong L, Chua HN
- Issue date: 2009 Aug 1