计算机技术与发展2017，Vol.27Issue(6)：17-21,5.DOI:10.3969/j.issn.1673-629X.2017.06.004
基于Minimum Cuts的蛋白质交互识别
Identification of Protein-protein Interaction with Minimum Cuts
摘要
Abstract
Protein-Protein Interaction (PPI) information is significant for biological and medical research,and is an important content in biomedicine field.The recognition of PPI with large-scale corpus can significantly reduce the cost of manual annotation by directly using the existing PPI database.Therefore,a method for PPI with Minimum Cuts based on the large-scale corpus has been proposed.Based on the framework of relational similarity,Minimum Cuts classifier not only uses SVM to predict the classification initially of a single protein,but also makes use of the similarity between the protein pairs to determine the results which are more accurate.The experimental results show that the Minimum Cuts classifier is better than the SVM classifier for the recognition of PPI.When the training data is 20%,the recognition results of the Minimum Cuts classifier gets better performance than that of an SVM classifier trained with 80%.关键词
关系相似性/MinimumCuts/支持向量机/蛋白质交互Key words
relational similarity/Minimum Cuts/SVM/protein-protein interaction分类
信息技术与安全科学引用本文复制引用
张景,吴红梅,牛耘..基于Minimum Cuts的蛋白质交互识别[J].计算机技术与发展,2017,27(6):17-21,5.基金项目
国家自然科学基金资助项目(61202132) （61202132）
