Guo-Sheng Han and Zu-Guo Yu* Pages 357 - 363 ( 7 )
Background: The subcellular localization of a protein is closely related with its functions and interactions. More and more evidences show that proteins may simultaneously exist at, or move between, two or more different subcellular localizations. Therefore, predicting protein subcellular localization is an important but challenging problem.Observation: Most of the existing methods for predicting protein subcellular localization assume that a protein locates at the single site. Although a few methods have been proposed to deal with proteins with multiple sites, correlations between subcellular localization are not efficiently taken into account. In this paper, we propose a integrated method for predicting protein subcellular localizations with both single and multiple sites. Method: Firstly, we extend the multi-label radial basis function (ML-RBF) method to the regularized version, and augment the first layer of ML-RBF to take local correlations between subcellular localization into account. Secondly, we embed the modified ML-RBF into a multi-label error-correcting output codes (ECOC) method in order to further consider the subcellular localization dependency. We name our method ML-rRBF-ECOC. Finally, the performance of ML-rRBF-ECOC is evaluated on three benchmark datasets. Results: The results demonstrate that ML-rRBF-ECOC has highly competitive performance to the related multi-label learning method and some state-of-the-art methods for predicting protein subcellular localizations with multiple sites. Considering dependency between subcellular localizations can contribute to the improvement of prediction performance. Conclusion: This also indicate that correlations between different subcellular localizations really exist. Our method at least play a complementary role to existing methods for predicting protein subcellular localizations with multiple sites.
Subcellular localization, multiple sites, multi-label radial basis function, error-correcting output codes, multi-label, label correlations.
School of Mathematics and computational Science, Xiangtan University, Xiangtan, School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Qld