A non-parametric bayesian approach for predicting RNA secondary structures

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

doi:10.1007/978-3-642-04241-6_24

A non-parametric bayesian approach for predicting RNA secondary structures

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Algorithms in Bioinformatics - 9th International Workshop, WABI 2009, Proceedings
Pages	286-297
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-04241-6_24
Publication status	Published - 2009
Externally published	Yes
Event	9th International Workshop on Algorithms in Bioinformatics, WABI 2009 - Philadelphia, PA, United States Duration: 2009 Sept 12 → 2009 Sept 13

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5724 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Workshop on Algorithms in Bioinformatics, WABI 2009
Country/Territory	United States
City	Philadelphia, PA
Period	09/9/12 → 09/9/13

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-642-04241-6_24

Cite this

Sato, K., Hamada, M., Mituyama, T., Asai, K., & Sakakibara, Y. (2009). A non-parametric bayesian approach for predicting RNA secondary structures. In Algorithms in Bioinformatics - 9th International Workshop, WABI 2009, Proceedings (pp. 286-297). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5724 LNBI). https://doi.org/10.1007/978-3-642-04241-6_24

A non-parametric bayesian approach for predicting RNA secondary structures. / Sato, Kengo; Hamada, Michiaki; Mituyama, Toutai et al.
Algorithms in Bioinformatics - 9th International Workshop, WABI 2009, Proceedings. 2009. p. 286-297 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5724 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sato, K, Hamada, M, Mituyama, T, Asai, K & Sakakibara, Y 2009, A non-parametric bayesian approach for predicting RNA secondary structures. in Algorithms in Bioinformatics - 9th International Workshop, WABI 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5724 LNBI, pp. 286-297, 9th International Workshop on Algorithms in Bioinformatics, WABI 2009, Philadelphia, PA, United States, 09/9/12. https://doi.org/10.1007/978-3-642-04241-6_24

Sato K, Hamada M, Mituyama T, Asai K, Sakakibara Y. A non-parametric bayesian approach for predicting RNA secondary structures. In Algorithms in Bioinformatics - 9th International Workshop, WABI 2009, Proceedings. 2009. p. 286-297. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-04241-6_24

Sato, Kengo ; Hamada, Michiaki ; Mituyama, Toutai et al. / A non-parametric bayesian approach for predicting RNA secondary structures. Algorithms in Bioinformatics - 9th International Workshop, WABI 2009, Proceedings. 2009. pp. 286-297 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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A non-parametric bayesian approach for predicting RNA secondary structures

Abstract

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