蛋白质二级结构的协同训练预测方法*

针对蛋白质二级结构机器学习预测方法，忽略氨基酸疏水性特征以及氨基酸之间的长程作用和准确率不高的现状，进行了比较实验分析。采用氨基酸对应的疏水能值替换蛋白质中相应的氨基酸，得到疏水能值的序列实验结果表明，用长的疏水能值序列，训练BP网络，对长程作用起主导的E结构的预测效果好。由于Profile编码特征和疏水能值特征是独立的冗余视图，基于协同训练思想，提出Cotraining算法。该算法的主要步骤是在Profile特征空间训练SVM分类器，在疏水性特征空间训练BP神经网络分类器，协同对氨基酸二级结构进行预测

Protein secondary structure co-training prediction method

Machine learning based protein secondary structure prediction methods suffer low prediction accuracy because they ignore the amino acid hydrophobic property and the interaction between far away amino acids. A sequence of hydrophobic value can be build by replacing the amino acid by its hydrophobic value. Experiments show that the BP neural network using long amino hydrophobic value sequence works well in prediction of E structure which is controlled mainly by long amino acid-amino acid interaction. Because both the Profile space and the hydrophobic energy value space are sufficient and redundant views, this paper proposes a Co-training algorithm. In the proposed algorithm, there are two classifiers. One is SVM classifier trained in Profile space, and the other is BP neural network classifier trained in hydrophobic value space, and they predict one amino acid secondary structure independently. If these two classifiers have different prediction results with one amino acid, an arbitration rule proposed in this paper is employed to make the final decision which is based on an active selecting strategy. Suspected sample and creditable sample are defined according to the characteristics of the classifiers and spaces to arbitrate the controversial prediction results. The experimental results show that the proposed algorithm has higher prediction accuracy both in E structure which controlled mainly by long interaction and H structure which controlled mainly by short interaction than existing algorithms.