Extending Stochastic Context-Free Grammars for an Application in Bioinformatics

  • Frank Weinberg
  • Markus E. Nebel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6031)


We extend stochastic context-free grammars such that the probability of applying a production can depend on the length of the subword that is generated from the application and show that existing algorithms for training and determining the most probable parse tree can easily be adapted to the extended model without losses in performance. Furthermore we show that the extended model is suited to improve the quality of predictions of RNA secondary structures.

The extended model may also be applied to other fields where SCFGs are used like natural language processing. Additionally some interesting questions in the field of formal languages arise from it.


Secondary Structure Prediction Quality Derivation Tree Rule Probability Partial Derivation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Frank Weinberg
    • 1
  • Markus E. Nebel
    • 1
  1. 1.Department of Computer SciencesUniversity of KaiserslauternKaiserslauternGermany

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