基于二叉树支持向量机的蛋白质结构类预测

提出了一种基于二叉树支持向量机(BT-SVM)的蛋白质结构类多类预测新方法.采用26维的向量来表示蛋白质序列的特征.BT-SVM多类分类方法能消除SVM在多分类问题中存在的不可分数据问题.采用两个经典数据集作为测试数据,通过自身一致性和n折叠交叉验证方法测试了新方法的性能.预测结果表明新方法具有良好的预测能力,与使用同一数据集的已有结果相比较,新方法的Jackknife结果和目前最好的方法取得的结果相当,可作为蛋白质结构类预测的一个工具.

Protein Structural Class Prediction with Binary Tree-Based Support Vector Machines

A new mutil-classification method based on binary tree SVM (BT-SVM) is presented to predict protein structural class. The protein sequence, which is represented by 26-D vector, is used as input vector. BT-SVM method resolves unclassifiable regions for multiclass problems which can not be solved by SVM. Self-consistency and cross validation test are used to verify the performance of the proposal method on two benchmark datasets. Satisfactory test results demonstrate that the new method is promising. The Jackknife results of the new method are compared with the existing results on the same datasets. The results of the new method are almost the same as the ones of the best exiting method. It illuminates that the new method has good prediction performance and it will become a useful tool in protein structure class prediction.