This article explores the interdependences between subcellular locations and incorporates them with support vector machines for prediction of protein subcellular localisation. Traditional prediction systems utilise a 'flat' structure of classifiers, such as the one-versus-all and one-versus-one schemes, with amino acid compositions to perform the prediction. Apart from those existing studies that ignore the interdependences between subcellular locations, we take advantage of a hierarchical structure to organise the subcellular locations and model their relationships. Here, we propose to use four kinds of hierarchical prediction methods and make comparative studies on three datasets. Experimental results show that three of the hierarchical models outperform the traditional 'flat' model in terms of tree loss values. In particular, one hierarchical model outperforms the traditional 'flat' model for all evaluation measures. Moreover, we gained some valuable insights into the sorting process by using hierarchical structures.
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protein subcellular localisation;
support vector machines
Document Type: Research Article
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China,MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems of Shanghai Jiao Tong University, Shanghai, China
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong
Publication date: March 1, 2011
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