PREDICTION OF PROTEIN SOLVENT ACCESSIBILITY WITH SUPPORT VECTOR MACHINE
基于支持向量机方法的蛋白可溶性预测

Residues in protein sequences can be divided into two classes (exposed/buried) or three classes (exposed/intermediate/buried) according to their relative solvent accessibility. Several lengths and parameters of window were explored to achieve the best performance. The prediction accuracies of support vector machine (SVM) for different cut-off thresholds were analyzed and compared with other methods, which showed that the SVM was a better method than neural network and information theory when using the same dataset. The best accuracy, in two-class problem, could be as high as 79.0%, and in three-class problem, could be as high as 67.5%. These results show that the support vector machine is an effective method in the prediction of protein solvent accessibility.