Improved prediction of subcellular location for apoptosis proteins by the dual-layer support vector machine.
Apoptosis proteins play an important role in the development and homeostasis of an organism. The accurate prediction of subcellular location for apoptosis proteins is very helpful for understanding the mechanism of apoptosis and their biological functions. However, most of the existing predictive methods are designed by utilizing a single classifier, which would limit the further improvement of their performances. In this paper, a novel predictive method, which is essentially a multi-classifier system, has been proposed by combing a dual-layer support vector machine (SVM) with multiple compositions including amino acid composition (AAC), dipeptide composition (DPC) and amphiphilic pseudo amino acid composition (Am-Pse-AAC). As a demonstration, the predictive performance of our method was evaluated on two datasets of apoptosis proteins, 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. The promising results indicate that our method can be a complementary tool for the prediction of subcellular location.
D000465:Algorithms; D000595:Amino Acid Sequence; D051017:Apoptosis Regulatory Proteins / Q000737:chemistry*; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D030562:Databases, Protein; D042541:Intracellular Space / Q000737:chemistry*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011237:Predictive Value of Tests
D051017:Apoptosis Regulatory Proteins