应用ANN/HMM混合模型预测蛋白质二级结构

针对3状态隐马尔可夫模型（hidden Markov model,HMM）预测蛋白质二级结构准确率不高的问题，提出15状态HMM，通过改进的算法与BP神经网络相结合进行二级结构预测。研究对象为CB513数据集中筛选出的492条蛋白质序列，将其随机均分7组。应用混合模型进行预测，对准确率进行7交叉验证，Q3准确率达7721%，SOV值为7252%。结果表明，混合模型既能充分考虑相邻氨基酸残基间的相互影响，也能在一定程度上照顾二级结构的远程相关性，因此带来了较好的预测准确率。

Hybrid model of ANN/HMM for protein secondary structure prediction

Aimed at the lower accuracy of 3-state hidden Markov model for protein secondary structure prediction,proposed 15-state HMM.Using modified algorithm of HMM to predict secondary structure combined with BP neural networks.Selected 492 proteins from the dataset CB513,and divided them into 7 even subsets.Applied the hybrid model to predict secondary structure and evaluated its accuracy by 7-fold cross validation.The hybrid model appeared to be very efficient,with Q3 score of 77.21% and SOV of 72.52%.The results show that the hybrid model not only captures the local information,but also considers the long-distance information.So it gets higher prediction accuracy.