使用核SVM和分割PSSM预测凋亡蛋白亚细胞位置  

Predicting Subcellular Localization of Apoptotic Proteins Using Kernel Svm and Segmentation Pssm Method

作　　者：夏新男 

机构地区：[1]云南大学,信息学院,云南 昆明

出　　处：《计算机科学与应用》2021年第3期710-719,共10页Computer Science and Application

摘　　要：凋亡蛋白与人类的一些疾病密切相关。准确的获得凋亡蛋白的亚细胞位置对理解疾病的发病机制和药物研发有至关重要的作用。目前,研究者们主要是通过蛋白质序列获取特征信息,从而对蛋白质亚细胞位置进行预测定位并获得了较好的结果。在本文中,我们首先改进了PSSM特征提取方法,对PSSM按行分块以获得凋亡蛋白序列的局部信息,我们称之为SePSSM,其次加入7种物理化学性质对氨基酸分类获取凋亡蛋白序列的全局信息。最终将得到的两种特征融合输入到使用不同核函数的SVM中进行预测定位,预测结果通过Jackknife检验得到。实验结果表明,对PSSM进行分割要优于无分隔,RBF核函数要优于其他核函数,融合特征在ZD98和ZW225数据集上获得了较好的效果,这表明我们的方法是有效的。Apoptosis proteins are closely related to some human diseases. Accurate identification of the sub-cellular location of apoptosis proteins is crucial for understanding the pathogenesis of diseases and drug development. At present, researchers mainly obtain feature information from protein sequences to predict the subcellular location of proteins and obtain good results. In this paper, we first improved the feature extraction method of PSSM, segmented the PSSM matrix by row to obtain the local information of the apoptotic protein sequence, which is called SePSSM. Secondly, seven physicochemical properties were added to classify amino acids to obtain the global information of apoptotic protein sequence. Finally, the obtained two features are fused and input into SVM using different kernel functions for prediction, and the prediction results were obtained by Jackknife test. The experimental results show that PSSM method with segmentation is better than that without segmentation, the RBF kernel function is better than other kernel functions, and the fusion feature has achieved better results on the ZD98 and ZW225 datasets, which shows that our method is effec-tive.

关 键 词：凋亡蛋白 PSSM 分割 物理化学性质 核SVM 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]