A non-parametric Bayesian approach for predicting RNA secondary structures
A non-parametric Bayesian approach for predicting RNA secondary structures
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预测 RNA 二级结构的非参数贝叶斯方法
DOI：
10.1142/s0219720010004926
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发表时间：
2010
影响因子：
1
通讯作者：
Yasufumi Sakakibara
中科院分区：
文献类型：
--
作者：
Kengo Sato;Michiaki Hamada;Toutai Mituyama;Kiyoshi Asai;Yasufumi Sakakibara
由于许多功能RNA都形成了与其功能相关的稳定二级结构，因此RNA二级结构预测是生物信息学中的一个重要问题。我们提出了一种新的模型，用于生成RNA二级结构的基础上的非参数贝叶斯方法，称为分层Dirichlet过程随机上下文无关文法（HDP-SCFG）。Herenon-parametric意味着某些元参数（如非终结符和产生式规则的数量）不必固定。相反，它们的分布被推断，以便适应（在贝叶斯意义上）所提供的训练序列。我们的RNA二级结构预测结果表明，HDP-SCFG比基于MFE和其他生成模型更准确。
Since many functional RNAs form stable secondary structures which are related to their functions, RNA secondary structure prediction is a crucial problem in bioinformatics. We propose a novel model for generating RNA secondary structures based on a non-parametric Bayesian approach, called hierarchical Dirichlet processes for stochastic context-free grammars (HDP-SCFGs). Herenon-parametricmeans that some meta-parameters, such as the number of non-terminal symbols and production rules, do not have to be fixed. Instead their distributions are inferred in order to be adapted (in the Bayesian sense) to the training sequences provided. The results of our RNA secondary structure predictions show that HDP-SCFGs are more accurate than the MFE-based and other generative models.