Prediction of subcelluar localization using maximal-margin spherical support vector machine

Prediction of subcellular localization of various proteins is an important and well-studied problem. Each compartment in cell has specific tasks, and proteins in each compartment are synthesized to fulfill these tasks, and for this reason, an effective predictive system for protein subcellular localization is crucial. Therefore, we propose a prediction based on maximal margin sphere-structure multi-class support vector, and use some different types of composition in amino acid for features. The experimental results show that the proposed method is better than transitional support vector machine.