基于混合核函数SVM的蛋白质相互作用预测方法

针对蛋白质相互作用的预测问题,提出一种以余弦核和线性差值累加核为基核的对偶混合核函数SVM的蛋白质相互作用预测方法.该方法充分考虑了蛋白质的结构域特征,同时根据蛋白质相互作用数据应具有顺序无关的特点,将"对偶"思想引入SVM核函数中.对两个真实的蛋白质相互作用数据集Yeast PPI和Human PPI的测试结果表明,提出的方法与其它方法相比能够有效地提高蛋白质相互作用预测的准确率.

Protein-protein interaction prediction method based on SVM with hybrid kernel function

This paper puts forward a kind of SVM based on the pairwise hybrid kernel function of cosine kernel and linear differential accumulate kernel for protein-protein interaction prediction problem. This method considers the feature of protein domains fully. At the same time, according to the data of the protein-protein interaction should be have a feature of sequence-independent ,so take the idea of the "pairwise into the SVM kernel function. Testing on two real data of Yeast PPI and Human PPI , and the results show that the new method in this paper can improve the accuracy of predicting protein-protein interaction effectively compared with other methods.