A non-parametric bayesian approach for predicting RNA secondary structures

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages286-297
Number of pages12
Volume5724 LNBI
DOIs
Publication statusPublished - 2009
Event9th International Workshop on Algorithms in Bioinformatics, WABI 2009 - Philadelphia, PA, United States
Duration: 2009 Sep 122009 Sep 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5724 LNBI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th International Workshop on Algorithms in Bioinformatics, WABI 2009
CountryUnited States
CityPhiladelphia, PA
Period09/9/1209/9/13

Fingerprint

RNA Secondary Structure
RNA
Bayesian Approach
Dirichlet Process
Structure Prediction
Context-free Grammar
Context free grammars
Production Rules
Generative Models
Secondary Structure
Bioinformatics
Model

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Sato, K., Hamada, M., Mituyama, T., Asai, K., & Sakakibara, Y. (2009). A non-parametric bayesian approach for predicting RNA secondary structures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5724 LNBI, 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; Asai, Kiyoshi; Sakakibara, Yasubumi.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5724 LNBI 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 proceedingConference contribution

Sato, K, Hamada, M, Mituyama, T, Asai, K & Sakakibara, Y 2009, A non-parametric bayesian approach for predicting RNA secondary structures. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5724 LNBI, 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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5724 LNBI. 2009. p. 286-297. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-04241-6_24
Sato, Kengo ; Hamada, Michiaki ; Mituyama, Toutai ; Asai, Kiyoshi ; Sakakibara, Yasubumi. / A non-parametric bayesian approach for predicting RNA secondary structures. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5724 LNBI 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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