A non-parametric bayesian approach for predicting rna secondary structures

Kengo Sato, Michiaki Hamada, Toutai Mituyama, Kiyoshi Asai, Yasubumi Sakakibara

研究成果: Article査読

8 被引用数 (Scopus)

抄録

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). Here non-parametric means 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.

本文言語English
ページ(範囲)727-742
ページ数16
ジャーナルJournal of Bioinformatics and Computational Biology
8
4
DOI
出版ステータスPublished - 2010 8

ASJC Scopus subject areas

  • 生化学
  • 分子生物学
  • コンピュータ サイエンスの応用

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