NDLI: An approach for RNA secondary structure prediction based on Bayesian network

Content Provider	IEEE Xplore Digital Library
Author	Tianhua Wu Zhidong Deng Dandan Song
Copyright Year	2009
Description	Author affiliation: Department of Computer Science, National Laboratory of Information Science and Technology, Tsinghua University, Beijing 100084, China (Tianhua Wu; Zhidong Deng; Dandan Song)
Abstract	RNA secondary structure prediction is a fundamental problem in bioinformatics. This paper proposes a new approach to predict RNA secondary structure based on Bayesian network. Compared to the existing sophisticated prediction approaches such as Zuker's algorithm and the stochastic context-free grammar (SCFG) model, Bayesian network can naturally incorporate a priori knowledge from different models sources, and moreover, they have great expression capabilities. Our approach provides an effective method of combining free energy information of Zuker algorithm with statistical information from SCFG probability model. Basically, the proposed approach is suitable to all kinds of existing SCFG grammar models. Taking the BJK grammar model as an example, this paper gives a complete description of our prediction algorithm. When performing on RNA datasets with known structures, the experimental results show that the prediction accuracy is considerably improved. The sensitivity and the correlation coefficient are increased by 7.91% and 5.70%, respectively, compared to the SCFG approach alone.
Starting Page	24
Ending Page	30
File Size	163808
Page Count	7
File Format	PDF
ISBN	9781424427567
DOI	10.1109/CIBCB.2009.4925703
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	RNA Bayesian methods Predictive models Dynamic programming Stochastic processes Context modeling Accuracy Packaging Heuristic algorithms Probability distribution
Content Type	Text
Resource Type	Article

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