一种使用隐藏神经网络的简单快速二级结构预测方法。

A simple and fast secondary structure prediction method using hidden neural networks.

作者信息

Lin Kuang, Simossis Victor A, Taylor Willam R, Heringa Jaap

机构信息

Division of Mathematical Biology, The National Institute for Medical Research The Ridgeway, Mill Hill, London NW7 1AA, UK.

出版信息

Bioinformatics. 2005 Jan 15;21(2):152-9. doi: 10.1093/bioinformatics/bth487. Epub 2004 Sep 17.

DOI:10.1093/bioinformatics/bth487

PMID:15377504

Abstract

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.

AVAILABILITY

YASPIN is available on-line at the Centre for Integrative Bioinformatics website (http://ibivu.cs.vu.nl/programs/yaspinwww/) at the Vrije University in Amsterdam and will soon be mirrored on the Mathematical Biology website (http://www.mathbio.nimr.mrc.ac.uk) at the NIMR in London.

CONTACT

kxlin@nimr.mrc.ac.uk

摘要

动机

在本文中，我们提出了一种二级结构预测方法YASPIN，与当前最先进的方法不同，该方法利用单个神经网络在七状态局部结构方案中预测二级结构元件，然后使用隐马尔可夫模型对输出进行优化，从而为预测提供更多信息。

结果

将YASPIN与当前表现最佳的二级结构预测方法进行了比较，如PHDpsi、PROFsec、SSPro2、JNET和PSIPRED。根据Q3、SOV和马修斯相关系数准确性度量，在独立的EVA5序列集上的总体预测准确性与表现最佳的方法相当。在链预测方面，YASPIN在Q3和SOV分数方面显示出最高的准确性。