Protein Secondary Structure Prediction via Kernel Minimum Squared Error

摘要：

In this paper, we propose a new protein secondary structure prediction method based on kernel minimum square error (KMSE). KMSE is a supervised pattern classification method, which has been successfully applied to a wide range of pattern recognition problems. The naive KMSE focuses on two-class problem, so it can not be directly applied for protein secondary structure prediction. We design a multi-class classifier based on KMSE for protein secondary structure prediction. The results of our experiments carried out on the rs126 dataset show that the performance of our method is better than that of PCA and LDA. Our method achieves a very high degree of prediction accuracy with simple computation, and we believe it is an effective method for the prediction of the secondary structure of protein.

关键词： protein secondary prediction machine learning kernel method classification

作者: Yong Xu Qi Zhu

作者单位: Bio-Computing Research Center Harbin Institute of Technology ShenZhen Graduate School ShenZhen, 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: 34-38

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）