王鲜芳, 王俊美, 王晓雷, 张悦. 基于改进的MIMLSVM预测蛋白质功能[J]. 信阳师范学院学报(自然科学版), 2017, 30(4): 642-646. DOI: 10.3969/j.issn.1003-0972.2017.04.028
引用本文: 王鲜芳, 王俊美, 王晓雷, 张悦. 基于改进的MIMLSVM预测蛋白质功能[J]. 信阳师范学院学报(自然科学版), 2017, 30(4): 642-646. DOI: 10.3969/j.issn.1003-0972.2017.04.028
WANG Xianfang, WANG Junmei, WANG Xiaolei, ZHANG Yue. Protein Function Prediction Based on the Improved MIMLSVM[J]. Journal of Xinyang Normal University (Natural Science Edition), 2017, 30(4): 642-646. DOI: 10.3969/j.issn.1003-0972.2017.04.028
Citation: WANG Xianfang, WANG Junmei, WANG Xiaolei, ZHANG Yue. Protein Function Prediction Based on the Improved MIMLSVM[J]. Journal of Xinyang Normal University (Natural Science Edition), 2017, 30(4): 642-646. DOI: 10.3969/j.issn.1003-0972.2017.04.028

基于改进的MIMLSVM预测蛋白质功能

Protein Function Prediction Based on the Improved MIMLSVM

  • 摘要: 针对MIMLSVM算法预测精度不高的问题,设计了一种新的基于改进MIMLSVM预测蛋白质功能模型.首先,采用改进的Hausdorff方法计算包之间的空间距离,并结合K-Medoids方法将MIML (多示例多标签)问题退化为多标签问题,以提高预测精度;然后,利用SVM算法将多标签问题转化为多个独立的二分类问题,结合蛋白质数据的特点,建立蛋白质功能预测模型,并利用粒子群算法优化模型参数;最后,通过对7种生物蛋白质功能预测的实验,证明所建模型的优越性.

     

    Abstract: To solve the problem that the MIMLSVM algorithm has low prediction accuracy, a new model was presented to predict the function of protein using an improved MIMLSVM algorithm. Firstly, the modified Hausdorff distance was used to calculate the space distance of each bag. Besides,to improve prediction accuracy, the K-Medoids wad used to covert the MIML (Multi-Instance Multi-Label) question to the multi-label question. Secondly, the multi-label problem was transformed into the multiple independent binary classification problems by using SVM algorithm. According to the characteristics of the protein data, a model was presented to predict the function of protein. Meanwhile, the Particle Swarm Optimization algorithm was referred to optimize the parameters of model. Finally, the experiment was taken on seven organisms. The experimental results showed that the proposed model was superior in the prediction of function of protein.

     

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