优化多核SVM的蛋白质二级结构预测

Protein secondary structure prediction based on optimized multi-kernel SVM

蛋白质序列的不同特征提取方式对蛋白质结构分类有很大的影响。为更好地表达蛋白质结构信息,基于特征融合思想构建特征向量,并使用一种基于多核支持向量机的方法,以多个核函数的线性加权代替传统的单一核函数,在对多类特征进行整合后构造SimpleMKL分类模型;利用梯度下降法迭代求解核函数的权值系数,并校准核函数参数和不同特征表达的融合效果。实验结果表明,该方法提高了蛋白质二级结构分类精度,在分类精度方面有明显优势,有助于准确预测蛋白质的二级结构。

The different feature extraction methods of protein sequence have great influence on protein structure classification. For better expression of protein structure information,the feature vectors are constructed based on feature fusion idea,and the traditional single kernel function is replaced by the linear weighting of multiple kernel functions by means of the method based on multi-kernel SVM(support vector machine);the SimpleMKL classification model is constructed after integrating the multi-class features,the weight coefficient of kernel function is solved iteratively by means of the gradient descent method,and the fusion effects of kernel function parameters and different feature expressions are calibrated. The experimental results show that the proposed method improves the classification accuracy of protein secondary structure,and has obvious advantages in classification accuracy,which is helpful to accurately predict the secondary structure of protein.