基于支持向量机方法的蛋白可溶性预测

按照蛋白质序列中残基的相对可溶性，将其分为两类(表面／内部)和三类(表面／中间／内部)进行预测。选择不同窗宽和参数对数据进行训练和预测，以确保得到最好的分类效果，并同其他已有方法进行比较。对同一数据集不同分类阈值的预测结果显示，支持向量机方法对蛋白质可溶性的整体预测效果优于神经网络和信息论的方法。其中，对两类数据的最优分类结果达到79．0％，对三类数据的最优分类结果达到67．5％，表明支持向量机是蛋白质残基可溶性预测的一种有效方法。

PREDICTION OF PROTEIN SOLVENT ACCESSIBILITY WITH SUPPORT VECTOR MACHINE

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.