Stochastic context-free grammers for tRNA modeling

Stochastic context-free grammars for tRNA modeling.

Stochastic context-free grammars (SCFGs) are applied to the problems of folding, aligning and modeling families of tRNA sequences. SCFGs capture the sequences' common primary and secondary structure and generalize the hidden Markov models (HMMs) used in related work on protein and DNA. Results show that after having been trained on as few as 20 tRNA sequences from only two tRNA subfamilies (mit...

Stochastic Context Free Grammars for modeling RNAs

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 whic...

Language modeling using stochastic context-free grammars

Recent Methods for RNA Modeling Using Stochastic Context-Free Grammars

Stochastic context-free grammars (SCFGs) can be applied to the problems of folding, aligning and modeling families of homologous RNA sequences. SCFGs capture the sequences' common primary and secondary structure and generalize the hidden Markov models (HMMs) used in related work on protein and DNA. This paper discusses our new algorithm, Tree-Grammar EM, for deducing SCFG parameters automatical...

Stochastic Multiple Context-Free Grammar for RNA Pseudoknot Modeling

Several grammars have been proposed for modeling RNA pseudoknotted structure. In this paper, we focus on multiple context-free grammars (MCFGs), which are natural extension of context-free grammars and can represent pseudoknots, and extend a specific subclass of MCFGs to a probabilistic model called SMCFG. We present a polynomial time parsing algorithm for finding the most probable derivation t...