Predicting protein subcellular localization based on a semi-supervised algorithm

This paper introduces a semi-supervised method for protein subcellular localization prediction. Feature extraction plays a key role in protein subcellular localization, and can greatly improve the performance of a classifier. In this paper we propose a novel semi-supervised dimensionality reduction method for extracting features. The experimental results show that the proposed method is efficient and feasible. Compared with other methods, our method can achieve relatively higher prediction accuracy. Particularly, it is found that semi-supervised method is superior to other methods in classification performance.