Prediction of protein-protein interactions using symmetrical encoding scheme

摘要：

The computational prediction of protein-protein interactions is currently an important issue in biology. In this paper, a K-local hyperplane distance nearest neighbor (HKNN) classifiers with symmetrical encoding scheme is proposed to predict protein-protein interactions. Moreover, a new sample encoding scheme, named symmetrical encoding scheme (SYES), for protein pair is developed by which a single protein-protein pair is mapped to two symmetrical points in the sample space. To evaluate the prediction performance of this encoding scheme, the ten-fold cross validation has been employed on two real data sets. The results indicate that this encoding scheme outperforms the sum encoding scheme to some extent, and the method proposed is comparable to other methods.

关键词： protein-protein interactions symmetrical encoding scheme machine learning

作者: Qingshan Ni Zhengzhi Wang Qingjuan Han Guangyun Wang Yingjie Zhao Gangguo Li

作者单位: College of Electro-Mechanic and Automation,National University of Defense Technology,Changsha,Hunan Fangchenggang Entry-Exit Inspection and Quarantine Bureau,Fangchenggang,Guangxi,538001,China

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）