A semi-supervised learning approach for RNA secondary structure prediction
A semi-supervised learning approach for RNA secondary structure prediction
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DOI：
10.1016/j.compbiolchem.2015.02.002
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发表时间：
2015-08-01
影响因子：
3.1
通讯作者：
Hamada, Michiaki
中科院分区：
文献类型：
--
作者：
Yonemoto, Haruka;Asai, Kiyoshi;Hamada, Michiaki
RNA二级结构预测是RNA生物信息学中的一项关键技术。大多数RNA二级结构预测算法使用概率模型，其中模型参数是用可靠的RNA二级结构训练的。由于难以通过实验程序（例如NMR或X射线晶体结构分析）确定RNA二级结构，因此仍有许多RNA序列可用于训练，其二级结构尚未通过实验确定。在本文中，我们介绍了一种新的半监督学习方法的训练参数的概率模型的RNA二级结构中，我们不仅采用RNA序列与注释的二级结构，但也与未知的二级结构。我们的模型是基于混合生成（随机上下文无关文法）和判别模型（条件随机场），已成功地应用于自然语言处理。计算实验表明，将二级结构未知的RNA序列加入到训练中，提高了二级结构预测的准确性。据我们所知，这是第一次研究半监督学习方法用于RNA二级结构预测。当可靠结构的数量有限时，这种技术将是有用的。(C)2015爱思唯尔有限公司版权所有。
RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited. (C) 2015 Elsevier Ltd. All rights reserved.