A simple and fast secondary structure prediction method using hidden neural networks
A simple and fast secondary structure prediction method using hidden neural networks
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DOI：
10.1093/bioinformatics/bth487
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发表时间：
2005-01-15
期刊：
影响因子：
5.8
通讯作者：
Heringa, J
中科院分区：
文献类型：
--
作者：
Lin, K;Simossis, VA;Heringa, J
动机：在本文中，我们提出了一种二级结构预测方法YASPIN，它不同于目前最先进的方法，利用一个单一的神经网络来预测7状态局部结构方案中的二级结构元素，然后使用隐马尔可夫模型优化输出，从而为预测提供更多的信息。将YASPIN与目前性能最好的二级结构预测方法如PHDpsi、PROFsec、SSPro 2、JNET和PSIPRED进行了比较。根据Q3、SOV和Matthew的相关性准确度测量，独立EVA5序列集的总体预测准确度与表现最好的序列集相当。YASPIN在链预测的Q3和SOV评分方面显示出最高的准确性。
Motivation: In this paper, we present a secondary structure prediction method YASPIN that unlike the current state-of-the-art methods utilizes a single neural network for predicting the secondary structure elements in a 7-state local structure scheme and then optimizes the output using a hidden Markov model, which results in providing more information for the prediction.Results: YASPIN was compared with the current top-performing secondary structure prediction methods, such as PHDpsi, PROFsec, SSPro2, JNET and PSIPRED. The overall prediction accuracy on the independent EVA5 sequence set is comparable with that of the top performers, according to the Q3, SOV and Matthew's correlations accuracy measures. YASPIN shows the highest accuracy in terms of Q3 and SOV scores for strand prediction.