基于ELM的蛋白质二级结构预测及其后处理  

A Prediction Framework Based on Extreme Learning Machine for Secondary Structure of Protein

作　　者：赵相国[1] 王国仁[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110004

出　　处：《东北大学学报（自然科学版）》2009年第10期1402-1405,共4页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(60773221;60773219);国家高技术研究发展计划项目(2006AA09Z139;2007AA01Z192)

摘　　要：将ELM应用到蛋白质二级结构模型的训练中,在此基础上提出了基于概率的合并算法(probability-based combining,PBC),用该算法预测结果的合并.根据生物学中关于蛋白质二级结构的特征提出了预测结果的Helix-后处理(Helix-post-processing,HPP)算法,对合并后的预测结果进行有效的后处理,从而进一步提高预测结果的准确率.分别在CB513和RS126两个数据集上进行了实验,实验结果表明,预测结果的准确率是令人满意的,尤其是实现了训练时间上的显著缩短.A prediction framework was proposed for training the secondary structure model of protein, based on a new effective learning algorithm, i.e., the extreme learning machine （ELM）. Then, to merge the predicted results together better, a probability-based combining （PBC） algorithm was proposed with a Helix-post-processing （HPP） algorithm set out according to the biological features of protein＇s secondary structure, which will provide efficient post-processing effect on the predicted results after merging m as to improve their accuracy further. The experiments were carried out on the datasets CB513 and RS126 separately, and the predicted results showed that the accuracy of the proposed algorithms is satisfactory especially the training time that is shortened greatly.

关 键 词：蛋白质二级结构预测 ELM 基于概率的合并算法 Helix-后处理算法 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]