Prediction of Subcellular Location for Apoptosis Proteins by Dual-layer Support Vector Machine Based on Multiple Compositions

The accurate prediction of subcellular location for apoptosis proteins is very helpful for understanding the mechanism of apoptosis and their biological functions. In this paper, a predictive method has been presented by using a dual- layer support vector machine (SVM) based on multiple compositions of a protein including amino acid composition (AAC), dipeptide composition (DPC) and amphiphilic pseudo amino acid composition (Am-Pse-AAC). And the predictive performance of our method is evaluated on two datasets involving the standard dataset ZD98 generated by Zhou and Doctor, and a larger dataset ZW225 generated by Zhang et al.. With the jackknife test, the overall accuracies of our method on the two datasets reach 94.90% and 88.44%, respectively. To the best of our knowledge, our method is superior to the previous approaches in terms of predictive accuracy and Matthew´s correlation coefficient. All the results indicate that our method can be a complementary tool for the prediction of subcellular location.