Prediction of protein secondary structure using large margin nearest neighbor classification

摘要：

Prediction of protein secondary structure from a primary sequence plays a critical role in structural biology. In this paper, we introduce a novel method for protein secondary structure prediction by using PSSM profiles and large margin nearest neighbor classification. Although the PSSM profiles and traditional nearest neighbor (NN) method can be directly used to predict secondary structure, since the PSSM profiles are not specifically designed for protein secondary structure prediction, the NN method could not achieve satisfactory prediction accuracy. To addressing this problem, we use a large margin nearest neighbor model to learn a Mahalanobis distance metric via convex semidefinite programming for nearest neighbor classification. Then, an energybased rule is invoked to assign secondary structure. Tests show that, compared with other NN methods, significant performance improvement has been achieved with respect to prediction accuracy by the proposed method.

关键词： Nearest neighbor distance metric large margin protein secondary structure prediction

作者: Wei Yang Kuanquan Wang Wangmeng Zuo

作者单位: Biocomputing Research Centre, School of Computer Science and Technology Harbin institute of Technology Harbin 150001, China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Advanced Computer Control(2011年IEEE第三届高端计算机控制国际会议 ICACC2011)

会议地点: 哈尔滨

会议语种:英文

页码: 202-205

在线出版日期: 2011-01-18（万方平台首次上网日期，不代表论文的发表时间）