关键词: 位置特异性得分矩阵, 自协方差变换, 支持向量机, 递归特征消除, 夹克刀检验

Abstract: Obtaining information on subcellular location of apoptosis proteins plays an important role for revealing the apoptosis mechanism and understanding the biological function of apoptosis proteins. It is usually time-consuming and costly to determine the subcellular location only relying on wet-bench experiments. Hence, it has become one of the most important research fields in bioinformatics to develop fast and effective computational methods to predict apoptosis proteins subcellular location. In this study, amino acid composition, dipeptide composition and auto covariance variables are extracted to represent a protein based on position specific scoring matrix. Then, recursive feature elimination（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 show that the proposed method provides the state-of-the-art performance in comparison with other existing methods.

Key words: position specific scoring matrix, auto covariance transformation, support vector machine, recursive feature elimination, jackknife test