TY - JOUR
T1 - A non-parametric bayesian approach for predicting rna secondary structures
AU - Sato, Kengo
AU - Hamada, Michiaki
AU - Mituyama, Toutai
AU - Asai, Kiyoshi
AU - Sakakibara, Yasubumi
N1 - Funding Information:
This work was supported in part by a grant from “Functional RNA Project” funded by the New Energy and Industrial Technology Development Organization (NEDO) of Japan, and was also supported in part by Grant-in-Aid for Scientific Research on Priority Area “Comparative Genomics” No. 17018029 from the Ministry of Education, Culture, Sports, Science and Technology of Japan. We thank Dr. Hisanori Kiryu and our colleagues from the RNA Informatics Team at the Computational Biology Research Center (CBRC) for fruitful discussions.
PY - 2010/8
Y1 - 2010/8
N2 - 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.
AB - 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.
KW - RNA secondary structure prediction
KW - non-parametric Bayesian
KW - stochastic context-free grammars
UR - http://www.scopus.com/inward/record.url?scp=77955572164&partnerID=8YFLogxK
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U2 - 10.1142/S0219720010004926
DO - 10.1142/S0219720010004926
M3 - Article
AN - SCOPUS:77955572164
SN - 0219-7200
VL - 8
SP - 727
EP - 742
JO - Journal of Bioinformatics and Computational Biology
JF - Journal of Bioinformatics and Computational Biology
IS - 4
ER -