| Stochastic Context Free Grammars for modeling (2008) | |||||||||||||||
Abstract | |||||||||||||||
| Rapid advancement in generation of sequence data and complex computational techniques have encouraged researchers to understand the structure, folding, and function of molecules. One of the challenging problem in bioinformatics that have fascinated researchers from computer science community is the prediction of secondary structure of RNA. The primary aim of this work is to design a system which can predict the secondary structure of RNAs containing introns. We have used stochastic context free grammars to model RNAs, our task is to infer this grammar from RNA sequences possibly containing introns. We are using a combination of genetic search and hidden markov models for inferring stochastic context free grammar from RNA sequences. The inferred grammar can then be used to predict the secondary structure of RNAs as the derivation tree corresponds to the secondary structure of RNAs. I. | |||||||||||||||
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