A non-parametric bayesian approach for predicting RNA secondary structures

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

*この研究の対応する著者

研究成果: Conference contribution

1 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
ホスト出版物のタイトルAlgorithms in Bioinformatics - 9th International Workshop, WABI 2009, Proceedings
ページ286-297
ページ数12
DOI
出版ステータスPublished - 2009
外部発表はい
イベント9th International Workshop on Algorithms in Bioinformatics, WABI 2009 - Philadelphia, PA, United States
継続期間: 2009 9 122009 9 13

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5724 LNBI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference9th International Workshop on Algorithms in Bioinformatics, WABI 2009
国/地域United States
CityPhiladelphia, PA
Period09/9/1209/9/13

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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