关键词: 蛋白质亚核定位, 位置特异性得分矩阵, 自互协方差变换, 递归特征消除

Abstract: The knowledge of protein subnuclear location in eukaryotic cells plays a very important role for understanding the biological functions of proteins. As it is very difficult and challenging to predict it at the subnuclear level using computational methods, a method which combines Auto Cross Covariance（ACC） transformation and Recursive Feature Elimination（RFE） has been proposed. ACC transformation is first employed to extract features to represent the proteins based on Position Specific Scoring Matrix（PSSM）. Then, RFE is adopted to select the optimal features. Finally, the reduced features are input to a Support Vector Machine（SVM） to perform the prediction. Jackknife tests on two widely used datasets（SC714 and LD504） show that the proposed method is very promising and performs better than most of existing methods.

Key words: protein subnuclear location, position specific scoring matrix, auto cross covariance transformation, recursive feature elimination