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 proceedingConference contribution

1 Citation (Scopus)


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 publicationAlgorithms in Bioinformatics - 9th International Workshop, WABI 2009, Proceedings
Number of pages12
Publication statusPublished - 2009
Externally publishedYes
Event9th International Workshop on Algorithms in Bioinformatics, WABI 2009 - Philadelphia, PA, United States
Duration: 2009 Sept 122009 Sept 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5724 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Workshop on Algorithms in Bioinformatics, WABI 2009
Country/TerritoryUnited States
CityPhiladelphia, PA

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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