Multiple graph regularized protein domain ranking
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KAUST Department
Computational Bioscience Research Center (CBRC)Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Structural and Functional Bioinformatics Group
Date
2012-11-19Preprint Posting Date
2012-08-18Online Publication Date
2012-11-19Print Publication Date
2012Permanent link to this record
http://hdl.handle.net/10754/325469Metadata
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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.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 NatureJournal
BMC BioinformaticsPubMed ID
23157331PubMed Central ID
PMC3583823arXiv
1208.3779Additional Links
http://arxiv.org/abs/1208.3779Scopus Count
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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 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.
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