Using Semi-supervised Discriminant Analysis to Predict Subcellular Localization of Gram-negative Bacterial Proteins

Abstract

In this paper, an effective dimension reduction approach called semi-supervised discriminant analysis (SDA) is employed to deal with the protein subcellular localization problem. Firstly, a novel protein sequence encoding method that consists of pseudo amino acid composition (PseAAC) and dipeptide composition (DC) is introduced to represent a protein. Secondly, the SDA algorithm is applied to extract the essential discriminant features from the combined feature data set consisting of PseAAC and DC. Finally, the K-nearest neighbor (K-NN) classifier is used to identify the subcellular localization of Gram-positive bacterial proteins. The proposed method can effective utilize both manifold information and the class information of the protein samples to guide the produce of protein subcellular localization. To evaluate the prediction performance of the proposed algorithm, a jackknife test based on nearest neighbor algorithm is employed on the gram-negative bacterial proteins data set. The results show that we can get a high total accuracy in a low-dimensional feature space, which indicates that the proposed approach is effective and practical.