Using AdaboostSVM to predict the GPCR functional Classes

摘要：

AdaBoost incorporating properly designed RBFSVM is a popular boosting method and demonstrates better generalization performance than SVM on imbalanced classification problems. This paper discusses the application of AdaBoostSVM algorithms to the problem of G-protein-coupled receptor classes prediction in which the pseudo amino acid composition is derived by combining “the cellular automaton image and the Ziv-Lempel complexity. In the experiments of classifing GPCRs form no-GPCRs and GPCRs’ six main families, the jackknife test overall accuracy rates are 96.8% and 89.04%, respectively. The experimental results suggest that the AdaBoostSVM holds potential to be a useful algorithm for understanding the functions of GPCRs and other proteins.

作者: Wang-Ren Qiu Xuan Xiao Zhen-Yu Zhang

作者单位: School of Information Engineering Jingdezhen Ceramic Institute Jingdezhen, China School of Computer Engineering Jingdezhen Ceramic Institute Jingdezhen, China College of Science,Dalian Jiaotong university Dalian,China

会议类型: 国际会议

会议名称: The 4th International Conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-06-18（万方平台首次上网日期，不代表论文的发表时间）