Multiple graph regularized protein domain ranking

BMC Bioinformatics

Methodology Article

Jim Jing-Yan Wang¹  Xin Gao²  Halima Bensmail³ 

[1] Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia;Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia;Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia;Qatar Computing Research Institute, 5825, Doha, Qatar;

关键词: Protein Domain;    Graph Weight;    Ranking Score;    Graph Regularization;    Fold Level;   

DOI  :  10.1186/1471-2105-13-307

 received in 2012-05-22, accepted in 2012-10-29,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundProtein 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.ResultsTo 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.ConclusionThe 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.

【 授权许可】

Unknown   
© Wang et al; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. 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.