Multiple graph regularized protein domain ranking

Handle URI:
http://hdl.handle.net/10754/325469
Title:
Multiple graph regularized protein domain ranking
Authors:
Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computational Bioscience Research Center (CBRC)
Citation:
Wang J, Bensmail H, Gao X (2012) Multiple graph regularized protein domain ranking. BMC Bioinformatics 13: 307. doi:10.1186/1471-2105-13-307.
Publisher:
Springer Nature
Journal:
BMC Bioinformatics
Issue Date:
19-Nov-2012
DOI:
10.1186/1471-2105-13-307
PubMed ID:
23157331
PubMed Central ID:
PMC3583823
ARXIV:
arXiv:1208.3779
Type:
Article
ISSN:
14712105
Additional Links:
http://arxiv.org/abs/1208.3779
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Jim Jing-Yanen
dc.contributor.authorBensmail, Halimaen
dc.contributor.authorGao, Xinen
dc.date.accessioned2014-08-27T09:52:42Z-
dc.date.available2014-08-27T09:52:42Z-
dc.date.issued2012-11-19en
dc.identifier.citationWang J, Bensmail H, Gao X (2012) Multiple graph regularized protein domain ranking. BMC Bioinformatics 13: 307. doi:10.1186/1471-2105-13-307.en
dc.identifier.issn14712105en
dc.identifier.pmid23157331en
dc.identifier.doi10.1186/1471-2105-13-307en
dc.identifier.urihttp://hdl.handle.net/10754/325469en
dc.description.abstractBackground: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.relation.urlhttp://arxiv.org/abs/1208.3779en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en
dc.subjectGlobal structureen
dc.subjectGraph modelen
dc.subjectInitial graphen
dc.subjectIterative algorithmen
dc.subjectManifold structuresen
dc.subjectObjective functionsen
dc.subjectPair-wise comparisonen
dc.subjectParameter selectionen
dc.subjectProtein domainsen
dc.subjectRanking algorithmen
dc.subjectRanking methodsen
dc.subjectRanking performanceen
dc.subjectSingle graphen
dc.subjectStructural biologyen
dc.subjectAlgorithmsen
dc.subjectGraph theoryen
dc.subjectWebsitesen
dc.subjectProteinsen
dc.subjectalgorithmen
dc.subjectcomputer programen
dc.subjectmethodologyen
dc.subjectprotein databaseen
dc.subjectprotein tertiary structureen
dc.subjectsequence alignmenten
dc.subjectAlgorithmsen
dc.subjectDatabases, Proteinen
dc.subjectProtein Structure, Tertiaryen
dc.subjectSequence Alignmenten
dc.subjectSoftwareen
dc.titleMultiple graph regularized protein domain rankingen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalBMC Bioinformaticsen
dc.identifier.pmcidPMC3583823en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionQatar Computing Research Institute, Doha 5825, Qataren
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
dc.identifier.arxividarXiv:1208.3779en
kaust.authorWang, Jim Jing-Yanen
kaust.authorGao, Xinen

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