A non-parametric bayesian approach for predicting rna secondary structures

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

Research output: Contribution to journalArticle

7 Citations (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
Pages (from-to)727-742
Number of pages16
JournalJournal of Bioinformatics and Computational Biology
Volume8
Issue number4
DOIs
Publication statusPublished - 2010 Aug
Externally publishedYes

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Keywords

  • non-parametric Bayesian
  • RNA secondary structure prediction
  • stochastic context-free grammars

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications

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